chore: import upstream snapshot with attribution

python/semantic_kernel/agents/__init__.py

@@ -0,0 +1,64 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import importlib
+
+_AGENTS = {
+    "Agent": ".agent",
+    "AgentChat": ".group_chat.agent_chat",
+    "AgentGroupChat": ".group_chat.agent_group_chat",
+    "AgentSpec": ".agent",
+    "AgentRegistry": ".agent",
+    "AgentResponseItem": ".agent",
+    "AgentThread": ".agent",
+    "AutoGenConversableAgent": ".autogen.autogen_conversable_agent",
+    "AutoGenConversableAgentThread": ".autogen.autogen_conversable_agent",
+    "AzureAIAgent": ".azure_ai.azure_ai_agent",
+    "AzureAIAgentSettings": ".azure_ai.azure_ai_agent_settings",
+    "AzureAIAgentThread": ".azure_ai.azure_ai_agent",
+    "AzureAssistantAgent": ".open_ai.azure_assistant_agent",
+    "AssistantAgentThread": ".open_ai.openai_assistant_agent",
+    "AzureResponsesAgent": ".open_ai.azure_responses_agent",
+    "BedrockAgent": ".bedrock.bedrock_agent",
+    "BedrockAgentThread": ".bedrock.bedrock_agent",
+    "ChatCompletionAgent": ".chat_completion.chat_completion_agent",
+    "ChatHistoryAgentThread": ".chat_completion.chat_completion_agent",
+    "CopilotStudioAgent": ".copilot_studio.copilot_studio_agent",
+    "CopilotStudioAgentAuthMode": ".copilot_studio.copilot_studio_agent_settings",
+    "CopilotStudioAgentSettings": ".copilot_studio.copilot_studio_agent_settings",
+    "CopilotStudioAgentThread": ".copilot_studio.copilot_studio_agent",
+    "DeclarativeSpecMixin": ".agent",
+    "OpenAIAssistantAgent": ".open_ai.openai_assistant_agent",
+    "OpenAIResponsesAgent": ".open_ai.openai_responses_agent",
+    "ModelConnection": ".agent",
+    "ModelSpec": ".agent",
+    "ResponsesAgentThread": ".open_ai.openai_responses_agent",
+    "RunPollingOptions": ".open_ai.run_polling_options",
+    "register_agent_type": ".agent",
+    "ToolSpec": ".agent",
+    "ConcurrentOrchestration": ".orchestration.concurrent",
+    "SequentialOrchestration": ".orchestration.sequential",
+    "HandoffOrchestration": ".orchestration.handoffs",
+    "OrchestrationHandoffs": ".orchestration.handoffs",
+    "GroupChatOrchestration": ".orchestration.group_chat",
+    "RoundRobinGroupChatManager": ".orchestration.group_chat",
+    "BooleanResult": ".orchestration.group_chat",
+    "StringResult": ".orchestration.group_chat",
+    "MessageResult": ".orchestration.group_chat",
+    "GroupChatManager": ".orchestration.group_chat",
+    "MagenticOrchestration": ".orchestration.magentic",
+    "ProgressLedger": ".orchestration.magentic",
+    "MagenticManagerBase": ".orchestration.magentic",
+    "StandardMagenticManager": ".orchestration.magentic",
+}
+
+
+def __getattr__(name: str):
+    if name in _AGENTS:
+        submod_name = _AGENTS[name]
+        module = importlib.import_module(submod_name, package=__name__)
+        return getattr(module, name)
+    raise AttributeError(f"module {__name__} has no attribute {name}")
+
+
+def __dir__():
+    return list(_AGENTS.keys())

python/semantic_kernel/agents/__init__.pyi

@@ -0,0 +1,89 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from .agent import (
+    Agent,
+    AgentRegistry,
+    AgentResponseItem,
+    AgentSpec,
+    AgentThread,
+    DeclarativeSpecMixin,
+    ModelConnection,
+    ModelSpec,
+    ToolSpec,
+    register_agent_type,
+)
+from .autogen.autogen_conversable_agent import AutoGenConversableAgent, AutoGenConversableAgentThread
+from .azure_ai.azure_ai_agent import AzureAIAgent, AzureAIAgentThread
+from .azure_ai.azure_ai_agent_settings import AzureAIAgentSettings
+from .bedrock.bedrock_agent import BedrockAgent, BedrockAgentThread
+from .chat_completion.chat_completion_agent import ChatCompletionAgent, ChatHistoryAgentThread
+from .copilot_studio.copilot_studio_agent import CopilotStudioAgent, CopilotStudioAgentThread
+from .copilot_studio.copilot_studio_agent_settings import CopilotStudioAgentAuthMode, CopilotStudioAgentSettings
+from .group_chat.agent_chat import AgentChat
+from .group_chat.agent_group_chat import AgentGroupChat
+from .open_ai.azure_assistant_agent import AzureAssistantAgent
+from .open_ai.azure_responses_agent import AzureResponsesAgent
+from .open_ai.openai_assistant_agent import AssistantAgentThread, OpenAIAssistantAgent
+from .open_ai.openai_responses_agent import OpenAIResponsesAgent, ResponsesAgentThread
+from .open_ai.run_polling_options import RunPollingOptions
+from .orchestration.concurrent import ConcurrentOrchestration
+from .orchestration.group_chat import (
+    BooleanResult,
+    GroupChatManager,
+    GroupChatOrchestration,
+    MessageResult,
+    RoundRobinGroupChatManager,
+    StringResult,
+)
+from .orchestration.handoffs import HandoffOrchestration, OrchestrationHandoffs
+from .orchestration.magentic import MagenticManagerBase, MagenticOrchestration, ProgressLedger, StandardMagenticManager
+from .orchestration.sequential import SequentialOrchestration
+
+__all__ = [
+    "Agent",
+    "AgentChat",
+    "AgentGroupChat",
+    "AgentRegistry",
+    "AgentResponseItem",
+    "AgentSpec",
+    "AgentThread",
+    "AssistantAgentThread",
+    "AutoGenConversableAgent",
+    "AutoGenConversableAgentThread",
+    "AzureAIAgent",
+    "AzureAIAgentSettings",
+    "AzureAIAgentThread",
+    "AzureAssistantAgent",
+    "AzureResponsesAgent",
+    "BedrockAgent",
+    "BedrockAgentThread",
+    "BooleanResult",
+    "ChatCompletionAgent",
+    "ChatHistoryAgentThread",
+    "ConcurrentOrchestration",
+    "CopilotStudioAgent",
+    "CopilotStudioAgentAuthMode",
+    "CopilotStudioAgentSettings",
+    "CopilotStudioAgentThread",
+    "DeclarativeSpecMixin",
+    "GroupChatManager",
+    "GroupChatOrchestration",
+    "HandoffOrchestration",
+    "MagenticManagerBase",
+    "MagenticOrchestration",
+    "MessageResult",
+    "ModelConnection",
+    "ModelSpec",
+    "OpenAIAssistantAgent",
+    "OpenAIResponsesAgent",
+    "OrchestrationHandoffs",
+    "ProgressLedger",
+    "ResponsesAgentThread",
+    "RoundRobinGroupChatManager",
+    "RunPollingOptions",
+    "SequentialOrchestration",
+    "StandardMagenticManager",
+    "StringResult",
+    "ToolSpec",
+    "register_agent_type",
+]

python/semantic_kernel/agents/autogen/README.md

@@ -0,0 +1,20 @@
+## AutoGen Conversable Agent (v0.2.X)
+
+Semantic Kernel Python supports running AutoGen Conversable Agents provided in the 0.2.X package.
+
+### Limitations
+
+Currently, there are some limitations to note:
+
+- AutoGen Conversable Agents in Semantic Kernel run asynchronously and do not support streaming of agent inputs or responses.
+- The `AutoGenConversableAgent` in Semantic Kernel Python cannot be configured as part of a Semantic Kernel `AgentGroupChat`. As we progress towards GA for our agent group chat patterns, we will explore ways to integrate AutoGen agents into a Semantic Kernel group chat scenario.
+
+### Installation
+
+Install the `semantic-kernel` package with the `autogen` extra:
+
+```bash
+pip install semantic-kernel[autogen]
+```
+
+For an example of how to integrate an AutoGen Conversable Agent using the Semantic Kernel Agent abstraction, please refer to [`autogen_conversable_agent_simple_convo.py`](../../../samples/concepts/agents/autogen_conversable_agent/autogen_conversable_agent_simple_convo.py).

python/semantic_kernel/agents/autogen/autogen_conversable_agent.py

@@ -0,0 +1,321 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+import uuid
+from collections.abc import AsyncIterable, Callable
+from typing import TYPE_CHECKING, Any
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from autogen import ConversableAgent
+
+from semantic_kernel.agents.agent import Agent, AgentResponseItem, AgentThread
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.function_result_content import FunctionResultContent
+from semantic_kernel.contents.history_reducer.chat_history_reducer import ChatHistoryReducer
+from semantic_kernel.contents.text_content import TextContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import AgentInvokeException, AgentThreadOperationException
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.utils.feature_stage_decorator import experimental
+from semantic_kernel.utils.telemetry.agent_diagnostics.decorators import (
+    trace_agent_get_response,
+    trace_agent_invocation,
+)
+
+if TYPE_CHECKING:
+    from autogen.cache import AbstractCache
+
+    from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+    from semantic_kernel.kernel import Kernel
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class AutoGenConversableAgentThread(AgentThread):
+    """Azure AI Agent Thread class."""
+
+    def __init__(self, chat_history: ChatHistory | None = None, thread_id: str | None = None) -> None:
+        """Initialize the AutoGenConversableAgentThread Thread.
+
+        Args:
+            chat_history: The chat history for the thread. If None, a new ChatHistory instance will be created.
+            thread_id: The ID of the thread. If None, a new thread will be created.
+        """
+        super().__init__()
+        self._chat_history = chat_history or ChatHistory()
+        self._id = thread_id
+
+    @override
+    async def _create(self) -> str:
+        """Starts the thread and returns its ID."""
+        if not self._id:
+            self._id = f"thread_{uuid.uuid4().hex}"
+
+        return self._id
+
+    @override
+    async def _delete(self) -> None:
+        """Ends the current thread."""
+        self._chat_history.clear()
+
+    @override
+    async def _on_new_message(self, new_message: str | ChatMessageContent) -> None:
+        """Called when a new message has been contributed to the chat."""
+        if isinstance(new_message, str):
+            new_message = ChatMessageContent(role=AuthorRole.USER, content=new_message)
+
+        if (
+            not new_message.metadata
+            or "thread_id" not in new_message.metadata
+            or new_message.metadata["thread_id"] != self._id
+        ):
+            self._chat_history.add_message(new_message)
+
+    async def get_messages(self) -> AsyncIterable[ChatMessageContent]:
+        """Retrieve the current chat history.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        if self._is_deleted:
+            raise AgentThreadOperationException("Cannot retrieve chat history, since the thread has been deleted.")
+        if self._id is None:
+            await self.create()
+        for message in self._chat_history.messages:
+            yield message
+
+    async def reduce(self) -> ChatHistory | None:
+        """Reduce the chat history to a smaller size."""
+        if self._id is None:
+            raise AgentThreadOperationException("Cannot reduce chat history, since the thread is not currently active.")
+        if not isinstance(self._chat_history, ChatHistoryReducer):
+            return None
+        return await self._chat_history.reduce()
+
+
+@experimental
+class AutoGenConversableAgent(Agent):
+    """A Semantic Kernel wrapper around an AutoGen 0.2 `ConversableAgent`.
+
+    This allows one to use it as a Semantic Kernel `Agent`. Note: this agent abstraction
+    does not currently allow for the use of AgentGroupChat within Semantic Kernel.
+    """
+
+    conversable_agent: ConversableAgent
+
+    def __init__(self, conversable_agent: ConversableAgent, **kwargs: Any) -> None:
+        """Initialize the AutoGenConversableAgent.
+
+        Args:
+            conversable_agent: The existing AutoGen 0.2 ConversableAgent instance
+            kwargs: Other Agent base class arguments (e.g. name, id, instructions)
+        """
+        args: dict[str, Any] = {
+            "name": conversable_agent.name,
+            "description": conversable_agent.description,
+            "instructions": conversable_agent.system_message,
+            "conversable_agent": conversable_agent,
+        }
+
+        if kwargs:
+            args.update(kwargs)
+
+        super().__init__(**args)
+
+    @trace_agent_get_response
+    @override
+    async def get_response(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        **kwargs: Any,
+    ) -> AgentResponseItem[ChatMessageContent]:
+        """Get a response from the agent.
+
+        Args:
+            messages: The input chat message content either as a string, ChatMessageContent or
+                a list of strings or ChatMessageContent.
+            thread: The thread to use for the conversation. If None, a new thread will be created.
+            kwargs: Additional keyword arguments
+
+        Returns:
+            An AgentResponseItem of type ChatMessageContent object with the response and the thread.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: AutoGenConversableAgentThread(),
+            expected_type=AutoGenConversableAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        reply = await self.conversable_agent.a_generate_reply(
+            messages=[message.to_dict() async for message in thread.get_messages()],
+            **kwargs,
+        )
+
+        logger.info("Called AutoGenConversableAgent.a_generate_reply.")
+
+        return await self._create_reply_content(reply, thread)
+
+    @trace_agent_invocation
+    @override
+    async def invoke(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        recipient: "AutoGenConversableAgent | None" = None,
+        clear_history: bool = True,
+        silent: bool = True,
+        cache: "AbstractCache | None" = None,
+        max_turns: int | None = None,
+        summary_method: str | Callable | None = ConversableAgent.DEFAULT_SUMMARY_METHOD,
+        summary_args: dict | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentResponseItem[ChatMessageContent]]:
+        """A direct `invoke` method for the ConversableAgent.
+
+        Args:
+            messages: The input chat message content either as a string, ChatMessageContent or
+                a list of strings or ChatMessageContent.
+            thread: The thread to use for the conversation. If None, a new thread will be created.
+            recipient: The recipient ConversableAgent to chat with
+            clear_history: Whether to clear the chat history before starting. True by default.
+            silent: Whether to suppress console output. True by default.
+            cache: The cache to use for storing chat history
+            max_turns: The maximum number of turns to chat for
+            summary_method: The method to use for summarizing the chat
+            summary_args: The arguments to pass to the summary method
+            message: The initial message to send. If message is not provided,
+                the agent will wait for the user to provide the first message.
+            kwargs: Additional keyword arguments
+
+        Yields:
+            An AgentResponseItem of type ChatMessageContent object with the response and the thread.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: AutoGenConversableAgentThread(),
+            expected_type=AutoGenConversableAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        if summary_args is None:
+            summary_args = {}
+
+        if recipient is not None:
+            if not isinstance(recipient, AutoGenConversableAgent):
+                raise AgentInvokeException(
+                    f"Invalid recipient type: {type(recipient)}. "
+                    "Recipient must be an instance of AutoGenConversableAgent."
+                )
+
+            messages = [message async for message in thread.get_messages()]
+            chat_result = await self.conversable_agent.a_initiate_chat(
+                recipient=recipient.conversable_agent,
+                clear_history=clear_history,
+                silent=silent,
+                cache=cache,
+                max_turns=max_turns,
+                summary_method=summary_method,
+                summary_args=summary_args,
+                message=messages[-1].content,  # type: ignore
+                **kwargs,
+            )
+
+            logger.info(f"Called AutoGenConversableAgent.a_initiate_chat with recipient: {recipient}.")
+
+            for message in chat_result.chat_history:
+                msg = AutoGenConversableAgent._to_chat_message_content(message)  # type: ignore
+                await thread.on_new_message(msg)
+                yield AgentResponseItem(
+                    message=msg,
+                    thread=thread,
+                )
+        else:
+            reply = await self.conversable_agent.a_generate_reply(
+                messages=[message.to_dict() async for message in thread.get_messages()],
+            )
+
+            logger.info("Called AutoGenConversableAgent.a_generate_reply.")
+
+            yield await self._create_reply_content(reply, thread)
+
+    @override
+    def invoke_stream(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        kernel: "Kernel | None" = None,
+        arguments: KernelArguments | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentResponseItem["StreamingChatMessageContent"]]:
+        """Invoke the agent with a stream of messages."""
+        raise NotImplementedError("The AutoGenConversableAgent does not support streaming.")
+
+    @staticmethod
+    def _to_chat_message_content(message: dict[str, Any]) -> ChatMessageContent:
+        """Translate an AutoGen message to a Semantic Kernel ChatMessageContent."""
+        items: list[TextContent | FunctionCallContent | FunctionResultContent] = []
+        role = AuthorRole(message.get("role"))
+        name: str = message.get("name", "")
+
+        content = message.get("content")
+        if content is not None:
+            text = TextContent(text=content)
+            items.append(text)
+
+        if role == AuthorRole.ASSISTANT:
+            tool_calls = message.get("tool_calls")
+            if tool_calls is not None:
+                for tool_call in tool_calls:
+                    items.append(
+                        FunctionCallContent(
+                            id=tool_call.get("id"),
+                            function_name=tool_call.get("name"),
+                            arguments=tool_call.get("function").get("arguments"),
+                        )
+                    )
+
+        if role == AuthorRole.TOOL:
+            tool_responses = message.get("tool_responses")
+            if tool_responses is not None:
+                for tool_response in tool_responses:
+                    items.append(
+                        FunctionResultContent(
+                            id=tool_response.get("tool_call_id"),
+                            result=tool_response.get("content"),
+                        )
+                    )
+
+        return ChatMessageContent(role=role, items=items, name=name)  # type: ignore
+
+    async def _create_reply_content(
+        self, reply: str | dict[str, Any], thread: AgentThread
+    ) -> AgentResponseItem[ChatMessageContent]:
+        response: ChatMessageContent
+        if isinstance(reply, str):
+            response = ChatMessageContent(content=reply, role=AuthorRole.ASSISTANT)
+        elif isinstance(reply, dict):
+            response = ChatMessageContent(**reply)
+        else:
+            raise AgentInvokeException(f"Unexpected reply type from `a_generate_reply`: {type(reply)}")
+
+        await thread.on_new_message(response)
+
+        return AgentResponseItem(
+            message=response,
+            thread=thread,
+        )

python/semantic_kernel/agents/azure_ai/azure_ai_agent.py

@@ -0,0 +1,990 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import inspect
+import json
+import logging
+import sys
+from collections.abc import AsyncIterable, Awaitable, Callable, Iterable
+from copy import deepcopy
+from typing import TYPE_CHECKING, Any, ClassVar, Literal, TypeVar
+
+from azure.ai.agents.models import Agent as AzureAIAgentModel
+from azure.ai.agents.models import (
+    AzureAISearchQueryType,
+    AzureAISearchTool,
+    BingGroundingTool,
+    CodeInterpreterTool,
+    FileSearchTool,
+    OpenApiAnonymousAuthDetails,
+    OpenApiTool,
+    ResponseFormatJsonSchemaType,
+    ThreadMessageOptions,
+    ToolDefinition,
+    ToolResources,
+    TruncationObject,
+)
+from azure.ai.projects.aio import AIProjectClient
+from pydantic import Field
+
+from semantic_kernel.agents import (
+    Agent,
+    AgentResponseItem,
+    AgentSpec,
+    AgentThread,
+    AzureAIAgentSettings,
+    DeclarativeSpecMixin,
+    ToolSpec,
+    register_agent_type,
+)
+from semantic_kernel.agents.azure_ai.agent_thread_actions import AgentThreadActions
+from semantic_kernel.agents.azure_ai.azure_ai_channel import AzureAIChannel
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.agents.open_ai.run_polling_options import RunPollingOptions
+from semantic_kernel.connectors.ai.function_calling_utils import kernel_function_metadata_to_function_call_format
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import (
+    AgentInitializationException,
+    AgentInvokeException,
+    AgentThreadOperationException,
+)
+from semantic_kernel.functions import KernelArguments
+from semantic_kernel.functions.kernel_function import TEMPLATE_FORMAT_MAP
+from semantic_kernel.functions.kernel_plugin import KernelPlugin
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
+from semantic_kernel.utils.feature_stage_decorator import experimental
+from semantic_kernel.utils.naming import generate_random_ascii_name
+from semantic_kernel.utils.telemetry.agent_diagnostics.decorators import (
+    trace_agent_get_response,
+    trace_agent_invocation,
+    trace_agent_streaming_invocation,
+)
+from semantic_kernel.utils.telemetry.user_agent import APP_INFO, SEMANTIC_KERNEL_USER_AGENT
+
+if TYPE_CHECKING:
+    from azure.ai.agents.models import ToolResources
+    from azure.core.credentials_async import AsyncTokenCredential
+
+    from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+    from semantic_kernel.kernel_pydantic import KernelBaseSettings
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+AgentsApiResponseFormatOption = str | ResponseFormatJsonSchemaType
+
+_T = TypeVar("_T", bound="AzureAIAgent")
+
+
+# region Declarative Spec
+
+_TOOL_BUILDERS: dict[str, Callable[[ToolSpec, Kernel | None], ToolDefinition]] = {}
+
+
+def _register_tool(tool_type: str):
+    def decorator(fn: Callable[[ToolSpec, Kernel | None], ToolDefinition]):
+        _TOOL_BUILDERS[tool_type.lower()] = fn
+        return fn
+
+    return decorator
+
+
+@_register_tool("azure_ai_search")
+def _azure_ai_search(spec: ToolSpec) -> AzureAISearchTool:
+    opts = spec.options or {}
+
+    connections = opts.get("tool_connections")
+    if not connections or not isinstance(connections, list) or not connections[0]:
+        raise AgentInitializationException(f"Missing or malformed 'tool_connections' in: {spec}")
+    conn_id = connections[0]
+
+    index_name = opts.get("index_name")
+    if not index_name or not isinstance(index_name, str):
+        raise AgentInitializationException(f"Missing or malformed 'index_name' in: {spec}")
+
+    raw_query_type = opts.get("query_type", AzureAISearchQueryType.SIMPLE)
+    if type(raw_query_type) is str:
+        try:
+            query_type = AzureAISearchQueryType(raw_query_type.lower())
+        except ValueError:
+            raise AgentInitializationException(f"Invalid query_type '{raw_query_type}' in: {spec}")
+    else:
+        query_type = raw_query_type
+
+    filter_expr = opts.get("filter", "")
+
+    top_k = opts.get("top_k", 5)
+    if not isinstance(top_k, int):
+        raise AgentInitializationException(f"'top_k' must be an integer in: {spec}")
+
+    return AzureAISearchTool(
+        index_connection_id=conn_id,
+        index_name=index_name,
+        query_type=query_type,
+        filter=filter_expr,
+        top_k=top_k,
+    )
+
+
+@_register_tool("azure_function")
+def _azure_function(spec: ToolSpec) -> ToolDefinition:
+    # TODO(evmattso): Implement Azure Function tool support
+    raise NotImplementedError("Azure Function tools are not yet supported with the Azure AI Agent Declarative Spec.")
+
+
+@_register_tool("bing_grounding")
+def _bing_grounding(spec: ToolSpec) -> BingGroundingTool:
+    opts = spec.options or {}
+
+    connections = spec.options.get("tool_connections")
+    if not connections or not isinstance(connections, list) or not connections[0]:
+        raise AgentInitializationException(f"Missing or malformed 'tool_connections' in: {spec}")
+    conn_id = connections[0]
+
+    market = opts.get("market", "")
+    set_lang = opts.get("set_lang", "")
+    count = opts.get("count", 5)
+    if not isinstance(count, int):
+        raise AgentInitializationException(f"'count' must be an integer in: {spec}")
+    freshness = opts.get("freshness", "")
+
+    return BingGroundingTool(connection_id=conn_id, market=market, set_lang=set_lang, count=count, freshness=freshness)
+
+
+@_register_tool("code_interpreter")
+def _code_interpreter(spec: ToolSpec) -> CodeInterpreterTool:
+    file_ids = spec.options.get("file_ids")
+    return CodeInterpreterTool(file_ids=file_ids) if file_ids else CodeInterpreterTool()
+
+
+@_register_tool("file_search")
+def _file_search(spec: ToolSpec) -> FileSearchTool:
+    vector_store_ids = spec.options.get("vector_store_ids")
+    if not vector_store_ids or not isinstance(vector_store_ids, list) or not vector_store_ids[0]:
+        raise AgentInitializationException(f"Missing or malformed 'vector_store_ids' in: {spec}")
+    return FileSearchTool(vector_store_ids=vector_store_ids)
+
+
+@_register_tool("function")
+def _function(spec: ToolSpec, kernel: "Kernel") -> ToolDefinition:
+    def parse_fqn(fqn: str) -> tuple[str, str]:
+        parts = fqn.split(".")
+        if len(parts) != 2:
+            raise AgentInitializationException(f"Function `{fqn}` must be in the form `pluginName.functionName`.")
+        return parts[0], parts[1]
+
+    if not spec.id:
+        raise AgentInitializationException("Function ID is required for function tools.")
+    plugin_name, function_name = parse_fqn(spec.id)
+    funcs = kernel.get_list_of_function_metadata_filters({"included_functions": f"{plugin_name}-{function_name}"})
+
+    match len(funcs):
+        case 0:
+            raise AgentInitializationException(f"Function `{spec.id}` not found in kernel.")
+        case 1:
+            return kernel_function_metadata_to_function_call_format(funcs[0])  # type: ignore[return-value]
+        case _:
+            raise AgentInitializationException(f"Multiple definitions found for `{spec.id}`. Please remove duplicates.")
+
+
+@_register_tool("openapi")
+def _openapi(spec: ToolSpec) -> OpenApiTool:
+    opts = spec.options or {}
+
+    if not spec.id:
+        raise AgentInitializationException("OpenAPI tool requires a non-empty 'id' (used as name).")
+    if not spec.description:
+        raise AgentInitializationException(f"OpenAPI tool '{spec.id}' requires a 'description'.")
+
+    raw_spec = opts.get("specification")
+    if not raw_spec:
+        raise AgentInitializationException(f"OpenAPI tool '{spec.id}' is missing required 'specification' field.")
+
+    try:
+        parsed_spec = json.loads(raw_spec) if isinstance(raw_spec, str) else raw_spec
+    except json.JSONDecodeError as e:
+        raise AgentInitializationException(f"Invalid JSON in OpenAPI 'specification' field: {e}") from e
+
+    auth = opts.get("auth", OpenApiAnonymousAuthDetails())
+
+    return OpenApiTool(
+        name=spec.id,
+        description=spec.description,
+        spec=parsed_spec,
+        auth=auth,
+        default_parameters=opts.get("default_parameters"),
+    )
+
+
+def _build_tool(spec: ToolSpec, kernel: "Kernel") -> ToolDefinition:
+    if not spec.type:
+        raise AgentInitializationException("Tool spec must include a 'type' field.")
+
+    try:
+        builder = _TOOL_BUILDERS[spec.type.lower()]
+    except KeyError as exc:
+        raise AgentInitializationException(f"Unsupported tool type: {spec.type}") from exc
+
+    sig = inspect.signature(builder)
+    return builder(spec) if len(sig.parameters) == 1 else builder(spec, kernel)  # type: ignore[call-arg]
+
+
+def _build_tool_resources(tool_defs: list[ToolDefinition]) -> ToolResources | None:
+    """Collects tool resources from known tool types with resource needs."""
+    resources: dict[str, Any] = {}
+
+    for tool in tool_defs:
+        if isinstance(tool, CodeInterpreterTool):
+            resources["code_interpreter"] = tool.resources.code_interpreter
+        elif isinstance(tool, AzureAISearchTool):
+            resources["azure_ai_search"] = tool.resources.azure_ai_search
+        elif isinstance(tool, FileSearchTool):
+            resources["file_search"] = tool.resources.file_search
+
+    return ToolResources(**resources) if resources else None
+
+
+# endregion
+
+# region Thread
+
+
+@experimental
+class AzureAIAgentThread(AgentThread):
+    """Azure AI Agent Thread class."""
+
+    def __init__(
+        self,
+        *,
+        client: AIProjectClient,
+        messages: list[ThreadMessageOptions] | None = None,
+        metadata: dict[str, str] | None = None,
+        thread_id: str | None = None,
+        tool_resources: "ToolResources | None" = None,
+    ) -> None:
+        """Initialize the Azure AI Agent Thread.
+
+        Args:
+            client: The Azure AI Project client.
+            messages: The messages to initialize the thread with.
+            metadata: The metadata for the thread.
+            thread_id: The ID of the thread
+            tool_resources: The tool resources for the thread.
+        """
+        super().__init__()
+
+        if client is None:
+            raise ValueError("Client cannot be None")
+
+        self._client = client
+        self._id = thread_id
+        self._messages = messages or []
+        self._metadata = metadata
+        self._tool_resources = tool_resources
+
+    @override
+    async def _create(self) -> str:
+        """Starts the thread and returns its ID."""
+        try:
+            response = await self._client.agents.threads.create(
+                messages=self._messages,
+                metadata=self._metadata,
+                tool_resources=self._tool_resources,
+            )
+        except Exception as ex:
+            raise AgentThreadOperationException(
+                "The thread could not be created due to an error response from the service."
+            ) from ex
+        return response.id
+
+    @override
+    async def _delete(self) -> None:
+        """Ends the current thread."""
+        if self._id is None:
+            raise AgentThreadOperationException("The thread cannot be deleted because it has not been created yet.")
+        try:
+            await self._client.agents.threads.delete(self._id)
+        except Exception as ex:
+            raise AgentThreadOperationException(
+                "The thread could not be deleted due to an error response from the service."
+            ) from ex
+
+    @override
+    async def _on_new_message(self, new_message: str | ChatMessageContent) -> None:
+        """Called when a new message has been contributed to the chat."""
+        if isinstance(new_message, str):
+            new_message = ChatMessageContent(role=AuthorRole.USER, content=new_message)
+
+        if (
+            not new_message.metadata
+            or "thread_id" not in new_message.metadata
+            or new_message.metadata["thread_id"] != self.id
+        ):
+            assert self.id is not None  # nosec
+            await AgentThreadActions.create_message(self._client, self.id, new_message)
+
+    async def get_messages(self, sort_order: Literal["asc", "desc"] = "desc") -> AsyncIterable[ChatMessageContent]:
+        """Get the messages in the thread.
+
+        Args:
+            sort_order: The order to sort the messages in. Either "asc" or "desc".
+
+        Yields:
+            An AsyncIterable of ChatMessageContent of the messages in the thread.
+        """
+        if self._is_deleted:
+            raise ValueError("The thread has been deleted.")
+        if self._id is None:
+            await self.create()
+        assert self.id is not None  # nosec
+        async for message in AgentThreadActions.get_messages(self._client, self.id, sort_order=sort_order):
+            yield message
+
+
+@experimental
+@register_agent_type("foundry_agent")
+class AzureAIAgent(DeclarativeSpecMixin, Agent):
+    """Azure AI Agent class."""
+
+    client: AIProjectClient
+    definition: AzureAIAgentModel
+    polling_options: RunPollingOptions = Field(default_factory=RunPollingOptions)
+
+    channel_type: ClassVar[type[AgentChannel]] = AzureAIChannel
+
+    def __init__(
+        self,
+        *,
+        arguments: "KernelArguments | None" = None,
+        client: AIProjectClient,
+        definition: AzureAIAgentModel,
+        kernel: "Kernel | None" = None,
+        plugins: list[KernelPlugin | object] | dict[str, KernelPlugin | object] | None = None,
+        polling_options: RunPollingOptions | None = None,
+        prompt_template_config: "PromptTemplateConfig | None" = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize the Azure AI Agent.
+
+        Args:
+            arguments: The KernelArguments instance
+            client: The AzureAI Project client. See "Quickstart: Create a new agent" guide
+                https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart?pivots=programming-language-python-azure
+                for details on how to create a new agent.
+            definition: The AzureAI Agent model created via the AzureAI Project client.
+            kernel: The Kernel instance used if invoking plugins
+            plugins: The plugins for the agent. If plugins are included along with a kernel, any plugins
+                that already exist in the kernel will be overwritten.
+            polling_options: The polling options for the agent.
+            prompt_template_config: The prompt template configuration. If this is provided along with
+                instructions, the prompt template will be used in place of the instructions.
+            **kwargs: Additional keyword arguments
+        """
+        args: dict[str, Any] = {
+            "client": client,
+            "definition": definition,
+            "name": definition.name or f"azure_agent_{generate_random_ascii_name(length=8)}",
+            "description": definition.description,
+        }
+
+        if definition.id is not None:
+            args["id"] = definition.id
+        if kernel is not None:
+            args["kernel"] = kernel
+        if arguments is not None:
+            args["arguments"] = arguments
+        if (
+            definition.instructions
+            and prompt_template_config
+            and definition.instructions != prompt_template_config.template
+        ):
+            logger.info(
+                f"Both `instructions` ({definition.instructions}) and `prompt_template_config` "
+                f"({prompt_template_config.template}) were provided. Using template in `prompt_template_config` "
+                "and ignoring `instructions`."
+            )
+
+        if plugins is not None:
+            args["plugins"] = plugins
+        if definition.instructions is not None:
+            args["instructions"] = definition.instructions
+        if prompt_template_config is not None:
+            args["prompt_template"] = TEMPLATE_FORMAT_MAP[prompt_template_config.template_format](
+                prompt_template_config=prompt_template_config
+            )
+            if prompt_template_config.template is not None:
+                # Use the template from the prompt_template_config if it is provided
+                args["instructions"] = prompt_template_config.template
+        if polling_options is not None:
+            args["polling_options"] = polling_options
+        if kwargs:
+            args.update(kwargs)
+
+        super().__init__(**args)
+
+    @staticmethod
+    def create_client(
+        credential: "AsyncTokenCredential",
+        endpoint: str | None = None,
+        api_version: str | None = None,
+        **kwargs: Any,
+    ) -> AIProjectClient:
+        """Create the Azure AI Project client using the connection string.
+
+        Args:
+            credential: The credential
+            endpoint: The Azure AI Foundry endpoint
+            api_version: Optional API version to use
+            kwargs: Additional keyword arguments
+
+        Returns:
+            AIProjectClient: The Azure AI Project client
+        """
+        if endpoint is None:
+            ai_agent_settings = AzureAIAgentSettings()
+            if not ai_agent_settings.endpoint:
+                raise AgentInitializationException("Please provide a valid Azure AI endpoint.")
+            endpoint = ai_agent_settings.endpoint
+
+        client_kwargs: dict[str, Any] = {
+            **kwargs,
+            **({"user_agent": SEMANTIC_KERNEL_USER_AGENT} if APP_INFO else {}),
+        }
+
+        if api_version:
+            client_kwargs["api_version"] = api_version
+
+        return AIProjectClient(
+            credential=credential,
+            endpoint=endpoint,
+            **client_kwargs,
+        )
+
+    # region Declarative Spec
+
+    @override
+    @classmethod
+    async def _from_dict(
+        cls: type[_T],
+        data: dict,
+        *,
+        kernel: Kernel,
+        prompt_template_config: PromptTemplateConfig | None = None,
+        **kwargs,
+    ) -> _T:
+        """Create an Azure AI Agent from the provided dictionary.
+
+        Args:
+            data: The dictionary containing the agent data.
+            kernel: The kernel to use for the agent.
+            prompt_template_config: The prompt template configuration.
+            kwargs: Additional keyword arguments. Note: unsupported keys may raise validation errors.
+
+        Returns:
+            AzureAIAgent: The Azure AI Agent instance.
+        """
+        client: AIProjectClient = kwargs.pop("client", None)
+        if client is None:
+            raise AgentInitializationException("Missing required 'client' in AzureAIAgent._from_dict()")
+
+        spec = AgentSpec.model_validate(data)
+
+        if "settings" in kwargs:
+            kwargs.pop("settings")
+
+        args = data.pop("arguments", None)
+        arguments = None
+        if args:
+            arguments = KernelArguments(**args)
+
+        # Handle arguments from kwargs, merging with any arguments from data
+        if "arguments" in kwargs and kwargs["arguments"] is not None:
+            incoming_args = kwargs["arguments"]
+            arguments = arguments | incoming_args if arguments is not None else incoming_args
+
+        if spec.id:
+            existing_definition = await client.agents.get_agent(spec.id)
+
+            # Create a mutable clone
+            definition = deepcopy(existing_definition)
+
+            # Selectively override attributes from spec
+            if spec.name is not None:
+                setattr(definition, "name", spec.name)
+            if spec.description is not None:
+                setattr(definition, "description", spec.description)
+            if spec.instructions is not None:
+                setattr(definition, "instructions", spec.instructions)
+            if spec.extras:
+                merged_metadata = dict(getattr(definition, "metadata", {}) or {})
+                merged_metadata.update(spec.extras)
+                setattr(definition, "metadata", merged_metadata)
+
+            return cls(
+                definition=definition,
+                client=client,
+                kernel=kernel,
+                prompt_template_config=prompt_template_config,
+                arguments=arguments,
+                **kwargs,
+            )
+
+        if not (spec.model and spec.model.id):
+            raise ValueError("model.id required when creating a new Azure AI agent")
+
+        # Build tool definitions & resources
+        tool_objs = [_build_tool(t, kernel) for t in spec.tools if t.type != "function"]
+        tool_defs = [d for tool in tool_objs for d in (tool.definitions if hasattr(tool, "definitions") else [tool])]
+        tool_resources = _build_tool_resources(tool_objs)
+
+        try:
+            agent_definition = await client.agents.create_agent(
+                model=spec.model.id,
+                name=spec.name,
+                description=spec.description,
+                instructions=spec.instructions,
+                tools=tool_defs,
+                tool_resources=tool_resources,
+                metadata=spec.extras,
+                **kwargs,
+            )
+        except Exception as ex:
+            print(f"Error creating agent: {ex}")
+
+        return cls(
+            definition=agent_definition,
+            client=client,
+            kernel=kernel,
+            arguments=arguments,
+            prompt_template_config=prompt_template_config,
+            **kwargs,
+        )
+
+    @override
+    @classmethod
+    def resolve_placeholders(
+        cls: type[_T],
+        yaml_str: str,
+        settings: "KernelBaseSettings | None" = None,
+        extras: dict[str, Any] | None = None,
+    ) -> str:
+        """Substitute ${AzureAI:Key} placeholders with fields from AzureAIAgentSettings and extras."""
+        import re
+
+        pattern = re.compile(r"\$\{([^}]+)\}")
+
+        # Build the mapping only if settings is provided and valid
+        field_mapping: dict[str, Any] = {}
+
+        if settings is None:
+            settings = AzureAIAgentSettings()
+
+        if not isinstance(settings, AzureAIAgentSettings):
+            raise AgentInitializationException(f"Expected AzureAIAgentSettings, got {type(settings).__name__}")
+
+        field_mapping.update({
+            "ChatModelId": getattr(settings, "model_deployment_name", None),
+            "Endpoint": getattr(settings, "endpoint", None),
+            "AgentId": getattr(settings, "agent_id", None),
+            "BingConnectionId": getattr(settings, "bing_connection_id", None),
+            "AzureAISearchConnectionId": getattr(settings, "azure_ai_search_connection_id", None),
+            "AzureAISearchIndexName": getattr(settings, "azure_ai_search_index_name", None),
+        })
+
+        if extras:
+            field_mapping.update(extras)
+
+        def replacer(match: re.Match[str]) -> str:
+            """Replace the matched placeholder with the corresponding value from field_mapping."""
+            full_key = match.group(1)  # for example, AzureAI:AzureAISearchConnectionId
+            section, _, key = full_key.partition(":")
+            if section != "AzureAI":
+                return match.group(0)
+
+            # Try short key first (AzureAISearchConnectionId), then full (AzureAI:AzureAISearchConnectionId)
+            return str(field_mapping.get(key) or field_mapping.get(full_key) or match.group(0))
+
+        result = pattern.sub(replacer, yaml_str)
+
+        # Safety check for unresolved placeholders
+        unresolved = pattern.findall(result)
+        if unresolved:
+            raise AgentInitializationException(
+                f"Unresolved placeholders in spec: {', '.join(f'${{{key}}}' for key in unresolved)}"
+            )
+
+        return result
+
+    # endregion
+
+    # region Invocation Methods
+
+    @trace_agent_get_response
+    @override
+    async def get_response(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: Kernel | None = None,
+        model: str | None = None,
+        instructions_override: str | None = None,
+        additional_instructions: str | None = None,
+        additional_messages: list[ThreadMessageOptions] | None = None,
+        tools: list[ToolDefinition] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        max_prompt_tokens: int | None = None,
+        max_completion_tokens: int | None = None,
+        truncation_strategy: TruncationObject | None = None,
+        response_format: AgentsApiResponseFormatOption | None = None,
+        parallel_tool_calls: bool | None = None,
+        metadata: dict[str, str] | None = None,
+        polling_options: RunPollingOptions | None = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        **kwargs: Any,
+    ) -> AgentResponseItem[ChatMessageContent]:
+        """Get a response from the agent on a thread.
+
+        Args:
+            messages: The input chat message content either as a string, ChatMessageContent or
+                a list of strings or ChatMessageContent.
+            thread: The thread to use for the agent.
+            arguments: The arguments for the agent.
+            kernel: The kernel to use for the agent.
+            model: The model to use for the agent.
+            instructions_override: Instructions to override the default instructions.
+            additional_instructions: Additional instructions for the agent.
+            additional_messages: Additional messages for the agent.
+            tools: Tools for the agent.
+            temperature: Temperature for the agent.
+            top_p: Top p for the agent.
+            max_prompt_tokens: Maximum prompt tokens for the agent.
+            max_completion_tokens: Maximum completion tokens for the agent.
+            truncation_strategy: Truncation strategy for the agent.
+            response_format: Response format for the agent.
+            parallel_tool_calls: Whether to allow parallel tool calls.
+            metadata: Metadata for the agent.
+            polling_options: The polling options for the agent.
+            function_choice_behavior: The function choice behavior to control which kernel
+                functions are available. Only Auto is supported; other types will raise an error.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            AgentResponseItem[ChatMessageContent]: The response from the agent.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: AzureAIAgentThread(client=self.client),
+            expected_type=AzureAIAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        run_level_params = {
+            "model": model,
+            "instructions_override": instructions_override,
+            "additional_instructions": additional_instructions,
+            "additional_messages": additional_messages,
+            "tools": tools,
+            "temperature": temperature,
+            "top_p": top_p,
+            "max_prompt_tokens": max_prompt_tokens,
+            "max_completion_tokens": max_completion_tokens,
+            "truncation_strategy": truncation_strategy,
+            "response_format": response_format,
+            "parallel_tool_calls": parallel_tool_calls,
+            "polling_options": polling_options,
+            "metadata": metadata,
+        }
+        run_level_params = {k: v for k, v in run_level_params.items() if v is not None}
+
+        response_messages: list[ChatMessageContent] = []
+        async for is_visible, response in AgentThreadActions.invoke(
+            agent=self,
+            thread_id=thread.id,
+            kernel=kernel,
+            arguments=arguments,
+            function_choice_behavior=function_choice_behavior,
+            **run_level_params,  # type: ignore
+        ):
+            if is_visible and response.metadata.get("code") is not True:
+                response.metadata["thread_id"] = thread.id
+                response_messages.append(response)
+
+        if not response_messages:
+            raise AgentInvokeException("No response messages were returned from the agent.")
+        final_message = response_messages[-1]
+        await thread.on_new_message(final_message)
+        return AgentResponseItem(message=final_message, thread=thread)
+
+    @trace_agent_invocation
+    @override
+    async def invoke(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: Kernel | None = None,
+        model: str | None = None,
+        instructions_override: str | None = None,
+        additional_instructions: str | None = None,
+        additional_messages: list[ThreadMessageOptions] | None = None,
+        tools: list[ToolDefinition] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        max_prompt_tokens: int | None = None,
+        max_completion_tokens: int | None = None,
+        truncation_strategy: TruncationObject | None = None,
+        response_format: AgentsApiResponseFormatOption | None = None,
+        parallel_tool_calls: bool | None = None,
+        metadata: dict[str, str] | None = None,
+        polling_options: RunPollingOptions | None = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentResponseItem[ChatMessageContent]]:
+        """Invoke the agent on the specified thread.
+
+        Args:
+            messages: The input chat message content either as a string, ChatMessageContent or
+                a list of strings or ChatMessageContent.
+            thread: The thread to use for the agent.
+            on_intermediate_message: A callback function to handle intermediate steps of the agent's execution.
+            arguments: The arguments for the agent.
+            kernel: The kernel to use for the agent.
+            model: The model to use for the agent.
+            instructions_override: Instructions to override the default instructions.
+            additional_instructions: Additional instructions for the agent.
+            additional_messages: Additional messages for the agent.
+            tools: Tools for the agent.
+            temperature: Temperature for the agent.
+            top_p: Top p for the agent.
+            max_prompt_tokens: Maximum prompt tokens for the agent.
+            max_completion_tokens: Maximum completion tokens for the agent.
+            truncation_strategy: Truncation strategy for the agent.
+            response_format: Response format for the agent.
+            parallel_tool_calls: Whether to allow parallel tool calls.
+            polling_options: The polling options for the agent.
+            metadata: Metadata for the agent.
+            function_choice_behavior: The function choice behavior to control which kernel
+                functions are available. Only Auto is supported; other types will raise an error.
+            **kwargs: Additional keyword arguments.
+
+        Yields:
+            AgentResponseItem[ChatMessageContent]: The response from the agent.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: AzureAIAgentThread(client=self.client),
+            expected_type=AzureAIAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        run_level_params = {
+            "model": model,
+            "instructions_override": instructions_override,
+            "additional_instructions": additional_instructions,
+            "additional_messages": additional_messages,
+            "tools": tools,
+            "temperature": temperature,
+            "top_p": top_p,
+            "max_prompt_tokens": max_prompt_tokens,
+            "max_completion_tokens": max_completion_tokens,
+            "truncation_strategy": truncation_strategy,
+            "response_format": response_format,
+            "parallel_tool_calls": parallel_tool_calls,
+            "metadata": metadata,
+            "polling_options": polling_options,
+        }
+        run_level_params = {k: v for k, v in run_level_params.items() if v is not None}
+
+        async for is_visible, message in AgentThreadActions.invoke(
+            agent=self,
+            thread_id=thread.id,
+            kernel=kernel,
+            arguments=arguments,
+            function_choice_behavior=function_choice_behavior,
+            **run_level_params,  # type: ignore
+        ):
+            message.metadata["thread_id"] = thread.id
+            await thread.on_new_message(message)
+
+            if is_visible:
+                # Only yield visible messages
+                yield AgentResponseItem(message=message, thread=thread)
+            elif on_intermediate_message:
+                # Emit tool-related messages only via callback
+                await on_intermediate_message(message)
+
+    @trace_agent_streaming_invocation
+    @override
+    async def invoke_stream(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        additional_instructions: str | None = None,
+        additional_messages: list[ThreadMessageOptions] | None = None,
+        instructions_override: str | None = None,
+        kernel: Kernel | None = None,
+        model: str | None = None,
+        tools: list[ToolDefinition] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        max_prompt_tokens: int | None = None,
+        max_completion_tokens: int | None = None,
+        truncation_strategy: TruncationObject | None = None,
+        response_format: AgentsApiResponseFormatOption | None = None,
+        parallel_tool_calls: bool | None = None,
+        metadata: dict[str, str] | None = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentResponseItem["StreamingChatMessageContent"]]:
+        """Invoke the agent on the specified thread with a stream of messages.
+
+        Args:
+            messages: The input chat message content either as a string, ChatMessageContent or
+                a list of strings or ChatMessageContent.
+            thread: The thread to use for the agent.
+            on_intermediate_message: A callback function to handle intermediate steps of the
+                                     agent's execution as fully formed messages.
+            arguments: The arguments for the agent.
+            additional_instructions: Additional instructions for the agent.
+            additional_messages: Additional messages for the agent.
+            instructions_override: Instructions to override the default instructions.
+            kernel: The kernel to use for the agent.
+            model: The model to use for the agent.
+            tools: Tools for the agent.
+            temperature: Temperature for the agent.
+            top_p: Top p for the agent.
+            max_prompt_tokens: Maximum prompt tokens for the agent.
+            max_completion_tokens: Maximum completion tokens for the agent.
+            truncation_strategy: Truncation strategy for the agent.
+            response_format: Response format for the agent.
+            parallel_tool_calls: Whether to allow parallel tool calls.
+            metadata: Metadata for the agent.
+            function_choice_behavior: The function choice behavior to control which kernel
+                functions are available. Only Auto is supported; other types will raise an error.
+            **kwargs: Additional keyword arguments.
+
+        Yields:
+            AgentResponseItem[StreamingChatMessageContent]: The response from the agent.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: AzureAIAgentThread(client=self.client),
+            expected_type=AzureAIAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        run_level_params = {
+            "model": model,
+            "instructions_override": instructions_override,
+            "additional_instructions": additional_instructions,
+            "additional_messages": additional_messages,
+            "tools": tools,
+            "temperature": temperature,
+            "top_p": top_p,
+            "max_prompt_tokens": max_prompt_tokens,
+            "max_completion_tokens": max_completion_tokens,
+            "truncation_strategy": truncation_strategy,
+            "response_format": response_format,
+            "parallel_tool_calls": parallel_tool_calls,
+            "metadata": metadata,
+        }
+        run_level_params = {k: v for k, v in run_level_params.items() if v is not None}
+
+        collected_messages: list[ChatMessageContent] | None = [] if on_intermediate_message else None
+
+        start_idx = 0
+        async for message in AgentThreadActions.invoke_stream(
+            agent=self,
+            thread_id=thread.id,
+            output_messages=collected_messages,
+            kernel=kernel,
+            arguments=arguments,
+            function_choice_behavior=function_choice_behavior,
+            **run_level_params,  # type: ignore
+        ):
+            # Before yielding the current streamed message, emit any new full messages first
+            if collected_messages is not None:
+                new_messages = collected_messages[start_idx:]
+                start_idx = len(collected_messages)
+
+                for new_msg in new_messages:
+                    new_msg.metadata["thread_id"] = thread.id
+                    await thread.on_new_message(new_msg)
+                    if on_intermediate_message:
+                        await on_intermediate_message(new_msg)
+
+            # Now yield the current streamed content (StreamingTextContent)
+            message.metadata["thread_id"] = thread.id
+            yield AgentResponseItem(message=message, thread=thread)
+
+    def get_channel_keys(self) -> Iterable[str]:
+        """Get the channel keys.
+
+        Returns:
+            Iterable[str]: The channel keys.
+        """
+        # Distinguish from other channel types.
+        yield f"{AzureAIAgent.__name__}"
+
+        # Distinguish between different agent IDs
+        yield self.id
+
+        # Distinguish between agent names
+        yield self.name
+
+    async def create_channel(self, thread_id: str | None = None) -> AgentChannel:
+        """Create a channel.
+
+        Args:
+            thread_id: The ID of the thread to create the channel for. If not provided
+                a new thread will be created.
+        """
+        thread = AzureAIAgentThread(client=self.client, thread_id=thread_id)
+
+        if thread.id is None:
+            await thread.create()
+
+        assert thread.id is not None  # nosec
+
+        return AzureAIChannel(client=self.client, thread_id=thread.id)

python/semantic_kernel/agents/azure_ai/azure_ai_agent_settings.py

@@ -0,0 +1,28 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import ClassVar
+
+from semantic_kernel.kernel_pydantic import KernelBaseSettings
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class AzureAIAgentSettings(KernelBaseSettings):
+    """Azure AI Agent settings currently used by the AzureAIAgent.
+
+    Args:
+        model_deployment_name: Azure AI Agent (Env var AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME)
+        endpoint: Azure AI Agent Endpoint (Env var AZURE_AI_AGENT_ENDPOINT)
+        api_version: Azure AI Agent API Version (Env var AZURE_AI_AGENT_API_VERSION)
+    """
+
+    env_prefix: ClassVar[str] = "AZURE_AI_AGENT_"
+
+    model_deployment_name: str
+    endpoint: str | None = None
+    agent_id: str | None = None
+    bing_connection_id: str | None = None
+    azure_ai_search_connection_id: str | None = None
+    azure_ai_search_index_name: str | None = None
+    api_version: str | None = None
+    deep_research_model: str | None = None

python/semantic_kernel/agents/azure_ai/azure_ai_agent_utils.py

@@ -0,0 +1,87 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Iterable, Sequence
+from typing import TYPE_CHECKING, Any, ClassVar, TypeVar
+
+from azure.ai.agents.models import (
+    CodeInterpreterTool,
+    FileSearchTool,
+    MessageAttachment,
+    MessageRole,
+    ThreadMessageOptions,
+    ToolDefinition,
+)
+
+from semantic_kernel.contents.file_reference_content import FileReferenceContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.contents import ChatMessageContent
+
+_T = TypeVar("_T", bound="AzureAIAgentUtils")
+
+
+@experimental
+class AzureAIAgentUtils:
+    """AzureAI Agent Utility Methods."""
+
+    tool_metadata: ClassVar[dict[str, Sequence[ToolDefinition]]] = {
+        "file_search": FileSearchTool().definitions,
+        "code_interpreter": CodeInterpreterTool().definitions,
+    }
+
+    @classmethod
+    def get_thread_messages(cls: type[_T], messages: list["ChatMessageContent"]) -> Any:
+        """Get the thread messages for an agent message."""
+        if not messages:
+            return None
+
+        thread_messages: list[ThreadMessageOptions] = []
+
+        for message in messages:
+            if not message.content:
+                continue
+
+            thread_msg = ThreadMessageOptions(
+                content=message.content,
+                role=MessageRole.USER if message.role == AuthorRole.USER else MessageRole.AGENT,
+                attachments=cls.get_attachments(message),
+                metadata=cls.get_metadata(message) if message.metadata else None,
+            )
+            thread_messages.append(thread_msg)
+
+        return thread_messages
+
+    @classmethod
+    def get_metadata(cls: type[_T], message: "ChatMessageContent") -> dict[str, str]:
+        """Get the metadata for an agent message."""
+        return {k: str(v) if v is not None else "" for k, v in (message.metadata or {}).items()}
+
+    @classmethod
+    def get_attachments(cls: type[_T], message: "ChatMessageContent") -> list[MessageAttachment]:
+        """Get the attachments for an agent message.
+
+        Args:
+            message: The ChatMessageContent
+
+        Returns:
+            A list of MessageAttachment
+        """
+        return [
+            MessageAttachment(
+                file_id=file_content.file_id,
+                tools=list(cls._get_tool_definition(file_content.tools)),  # type: ignore
+                data_source=file_content.data_source if file_content.data_source else None,
+            )
+            for file_content in message.items
+            if isinstance(file_content, FileReferenceContent)
+        ]
+
+    @classmethod
+    def _get_tool_definition(cls: type[_T], tools: list[Any]) -> Iterable[ToolDefinition]:
+        if not tools:
+            return
+        for tool in tools:
+            if tool_definition := cls.tool_metadata.get(tool):
+                yield from tool_definition

python/semantic_kernel/agents/azure_ai/azure_ai_channel.py

@@ -0,0 +1,121 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import sys
+from collections.abc import AsyncIterable
+from typing import TYPE_CHECKING
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from semantic_kernel.agents.azure_ai.agent_thread_actions import AgentThreadActions
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.exceptions.agent_exceptions import AgentChatException
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from azure.ai.projects.aio import AIProjectClient
+
+    from semantic_kernel.agents.agent import Agent
+    from semantic_kernel.contents.chat_message_content import ChatMessageContent
+
+
+@experimental
+class AzureAIChannel(AgentChannel):
+    """AzureAI Channel."""
+
+    def __init__(self, client: "AIProjectClient", thread_id: str) -> None:
+        """Initialize the AzureAI Channel.
+
+        Args:
+            client: The AzureAI Project client.
+            thread_id: The thread ID for the channel.
+        """
+        self.client = client
+        self.thread_id = thread_id
+
+    @override
+    async def receive(self, history: list["ChatMessageContent"]) -> None:
+        """Receive the conversation messages.
+
+        Args:
+            history: The conversation messages.
+        """
+        for message in history:
+            await AgentThreadActions.create_message(self.client, self.thread_id, message)
+
+    @override
+    async def invoke(self, agent: "Agent", **kwargs) -> AsyncIterable[tuple[bool, "ChatMessageContent"]]:
+        """Invoke the agent.
+
+        Args:
+            agent: The agent to invoke.
+            kwargs: The keyword arguments.
+
+        Yields:
+            tuple[bool, ChatMessageContent]: The conversation messages.
+        """
+        from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
+
+        if not isinstance(agent, AzureAIAgent):
+            raise AgentChatException(f"Agent is not of the expected type {type(AzureAIAgent)}.")
+
+        async for is_visible, message in AgentThreadActions.invoke(
+            agent=agent,
+            thread_id=self.thread_id,
+            arguments=agent.arguments,
+            kernel=agent.kernel,
+            **kwargs,
+        ):
+            yield is_visible, message
+
+    @override
+    async def invoke_stream(
+        self,
+        agent: "Agent",
+        messages: list["ChatMessageContent"],
+        **kwargs,
+    ) -> AsyncIterable["ChatMessageContent"]:
+        """Invoke the agent stream.
+
+        Args:
+            agent: The agent to invoke.
+            messages: The conversation messages.
+            kwargs: The keyword arguments.
+
+        Yields:
+            tuple[bool, StreamingChatMessageContent]: The conversation messages.
+        """
+        from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
+
+        if not isinstance(agent, AzureAIAgent):
+            raise AgentChatException(f"Agent is not of the expected type {type(AzureAIAgent)}.")
+
+        async for message in AgentThreadActions.invoke_stream(
+            agent=agent,
+            thread_id=self.thread_id,
+            output_messages=messages,
+            arguments=agent.arguments,
+            kernel=agent.kernel,
+            **kwargs,
+        ):
+            yield message
+
+    @override
+    async def get_history(self) -> AsyncIterable["ChatMessageContent"]:
+        """Get the conversation history.
+
+        Yields:
+            ChatMessageContent: The conversation history.
+        """
+        async for message in AgentThreadActions.get_messages(self.client, thread_id=self.thread_id):
+            yield message
+
+    @override
+    async def reset(self) -> None:
+        """Reset the agent's thread."""
+        try:
+            await self.client.agents.threads.delete(thread_id=self.thread_id)
+        except Exception as e:
+            raise AgentChatException(f"Failed to delete thread: {e}")

python/semantic_kernel/agents/bedrock/README.md

@@ -0,0 +1,27 @@
+# Amazon Bedrock AI Agents in Semantic Kernel
+
+## Overview
+
+AWS Bedrock Agents is a managed service that allows users to stand up and run AI agents in the AWS cloud quickly.
+
+## Tools/Functions
+
+Bedrock Agents allow the use of tools via [action groups](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-action-create.html).
+
+The integration of Bedrock Agents with Semantic Kernel allows users to register kernel functions as tools in Bedrock Agents.
+
+## Enable code interpretation
+
+Bedrock Agents can write and execute code via a feature known as [code interpretation](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-code-interpretation.html) similar to what OpenAI also offers.
+
+## Enable user input
+
+Bedrock Agents can [request user input](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-user-input.html) in case of missing information to invoke a tool. When this is enabled, the agent will prompt the user for the missing information. When this is disabled, the agent will guess the missing information.
+
+## Knowledge base
+
+Bedrock Agents can leverage data saved on AWS to perform RAG tasks, this is referred to as the [knowledge base](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-kb-add.html) in AWS.
+
+## Multi-agent
+
+Bedrock Agents support [multi-agent workflows](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-multi-agent-collaboration.html) for more complex tasks. However, it employs a different pattern than what we have in Semantic Kernel, thus this is not supported in the current integration.

python/semantic_kernel/agents/bedrock/action_group_utils.py

@@ -0,0 +1,117 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import Any
+
+from semantic_kernel.connectors.ai.function_call_choice_configuration import FunctionCallChoiceConfiguration
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.function_result_content import FunctionResultContent
+from semantic_kernel.functions.kernel_function_metadata import KernelFunctionMetadata
+from semantic_kernel.functions.kernel_parameter_metadata import KernelParameterMetadata
+
+
+def kernel_function_to_bedrock_function_schema(
+    function_choice_configuration: FunctionCallChoiceConfiguration,
+) -> dict[str, Any]:
+    """Convert the kernel function to bedrock function schema."""
+    return {
+        "functions": [
+            kernel_function_metadata_to_bedrock_function_schema(function_metadata)
+            for function_metadata in function_choice_configuration.available_functions or []
+        ]
+    }
+
+
+def kernel_function_metadata_to_bedrock_function_schema(function_metadata: KernelFunctionMetadata) -> dict[str, Any]:
+    """Convert the kernel function metadata to bedrock function schema."""
+    schema = {
+        "description": function_metadata.description,
+        "name": function_metadata.fully_qualified_name,
+        "parameters": {
+            parameter.name: kernel_function_parameter_to_bedrock_function_parameter(parameter)
+            for parameter in function_metadata.parameters
+        },
+        # This field controls whether user confirmation is required to invoke the function.
+        # If this is set to "ENABLED", the user will be prompted to confirm the function invocation.
+        # Only after the user confirms, the function call request will be issued by the agent.
+        # If the user denies the confirmation, the agent will act as if the function does not exist.
+        # Currently, we do not support this feature, so we set it to "DISABLED".
+        "requireConfirmation": "DISABLED",
+    }
+
+    # Remove None values from the schema
+    return {key: value for key, value in schema.items() if value is not None}
+
+
+def kernel_function_parameter_to_bedrock_function_parameter(parameter: KernelParameterMetadata):
+    """Convert the kernel function parameters to bedrock function parameters."""
+    schema = {
+        "description": parameter.description,
+        "type": kernel_function_parameter_type_to_bedrock_function_parameter_type(parameter.schema_data),
+        "required": parameter.is_required,
+    }
+
+    # Remove None values from the schema
+    return {key: value for key, value in schema.items() if value is not None}
+
+
+# These are the allowed parameter types in bedrock function.
+# https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent-runtime_ParameterDetail.html
+BEDROCK_FUNCTION_ALLOWED_PARAMETER_TYPES = {
+    "string",
+    "number",
+    "integer",
+    "boolean",
+    "array",
+}
+
+
+def kernel_function_parameter_type_to_bedrock_function_parameter_type(schema_data: dict[str, Any] | None) -> str:
+    """Convert the kernel function parameter type to bedrock function parameter type."""
+    if schema_data is None:
+        raise ValueError(
+            "Schema data is required to convert the kernel function parameter type to bedrock function parameter type."
+        )
+
+    type_ = schema_data.get("type")
+    if type_ is None:
+        raise ValueError(
+            "Type is required to convert the kernel function parameter type to bedrock function parameter type."
+        )
+
+    if type_ not in BEDROCK_FUNCTION_ALLOWED_PARAMETER_TYPES:
+        raise ValueError(
+            f"Type {type_} is not allowed in bedrock function parameter type. "
+            f"Allowed types are {BEDROCK_FUNCTION_ALLOWED_PARAMETER_TYPES}."
+        )
+
+    return type_
+
+
+def parse_return_control_payload(return_control_payload: dict[str, Any]) -> list[FunctionCallContent]:
+    """Parse the return control payload to a list of function call contents for the kernel."""
+    return [
+        FunctionCallContent(
+            id=return_control_payload["invocationId"],
+            name=invocation_input["functionInvocationInput"]["function"],
+            arguments={
+                parameter["name"]: parameter["value"]
+                for parameter in invocation_input["functionInvocationInput"]["parameters"]
+            },
+            metadata=invocation_input,
+        )
+        for invocation_input in return_control_payload.get("invocationInputs", [])
+    ]
+
+
+def parse_function_result_contents(function_result_contents: list[FunctionResultContent]) -> list[dict[str, Any]]:
+    """Parse the function result contents to be returned to the agent in the session state."""
+    return [
+        {
+            "functionResult": {
+                "actionGroup": function_result_content.metadata["functionInvocationInput"]["actionGroup"],
+                "function": function_result_content.name,
+                "responseBody": {"TEXT": {"body": str(function_result_content.result)}},
+            }
+        }
+        for function_result_content in function_result_contents
+    ]

python/semantic_kernel/agents/bedrock/bedrock_agent.py

@@ -0,0 +1,746 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import asyncio
+import logging
+import os
+import sys
+from collections.abc import AsyncIterable, Awaitable, Callable
+from functools import partial, reduce
+from typing import Any, ClassVar
+
+from pydantic import ValidationError
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from semantic_kernel.agents import AgentResponseItem, AgentThread
+from semantic_kernel.agents.bedrock.action_group_utils import (
+    parse_function_result_contents,
+    parse_return_control_payload,
+)
+from semantic_kernel.agents.bedrock.bedrock_agent_base import BedrockAgentBase
+from semantic_kernel.agents.bedrock.bedrock_agent_settings import BedrockAgentSettings
+from semantic_kernel.agents.bedrock.models.bedrock_agent_event_type import BedrockAgentEventType
+from semantic_kernel.agents.bedrock.models.bedrock_agent_model import BedrockAgentModel
+from semantic_kernel.agents.bedrock.models.bedrock_agent_status import BedrockAgentStatus
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.agents.channels.bedrock_agent_channel import BedrockAgentChannel
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.contents.binary_content import BinaryContent
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.function_result_content import FunctionResultContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import AgentInitializationException, AgentInvokeException
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.functions.kernel_plugin import KernelPlugin
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.utils.async_utils import run_in_executor
+from semantic_kernel.utils.feature_stage_decorator import experimental
+from semantic_kernel.utils.telemetry.agent_diagnostics.decorators import (
+    trace_agent_get_response,
+    trace_agent_invocation,
+    trace_agent_streaming_invocation,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@experimental
+class BedrockAgentThread(AgentThread):
+    """Bedrock Agent Thread class."""
+
+    def __init__(
+        self,
+        bedrock_runtime_client: Any,
+        session_id: str | None = None,
+    ) -> None:
+        """Initialize the Bedrock Agent Thread.
+
+        The underlying Bedrock session of the thread is created when the thread is started.
+        https://docs.aws.amazon.com/bedrock/latest/userguide/sessions.html
+
+        Args:
+            bedrock_runtime_client: The Bedrock Runtime Client.
+            session_id: The session ID.
+        """
+        super().__init__()
+        self._bedrock_runtime_client = bedrock_runtime_client
+        self._id = session_id
+
+    @override
+    async def _create(self) -> str:
+        """Starts the thread and returns the underlying Bedrock session ID."""
+        response = await run_in_executor(
+            None,
+            partial(
+                self._bedrock_runtime_client.create_session,
+            ),
+        )
+        self._id = response["sessionId"]
+        return self._id  # type: ignore
+
+    @override
+    async def _delete(self) -> None:
+        """Ends the current thread.
+
+        This will only end the underlying Bedrock session but not delete it.
+        """
+        # Must end the session before deleting it.
+        await run_in_executor(
+            None,
+            partial(
+                self._bedrock_runtime_client.end_session,
+                sessionIdentifier=self._id,
+            ),
+        )
+
+    @override
+    async def _on_new_message(self, new_message: str | ChatMessageContent) -> None:
+        """Called when a new message has been contributed to the chat."""
+        raise NotImplementedError(
+            "This method is not implemented for BedrockAgentThread. "
+            "Messages and responses are automatically handled by the Bedrock service."
+        )
+
+
+@experimental
+class BedrockAgent(BedrockAgentBase):
+    """Bedrock Agent.
+
+    Manages the interaction with Amazon Bedrock Agent Service.
+    """
+
+    channel_type: ClassVar[type[AgentChannel]] = BedrockAgentChannel
+
+    def __init__(
+        self,
+        agent_model: BedrockAgentModel | dict[str, Any],
+        *,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        kernel: Kernel | None = None,
+        plugins: list[KernelPlugin | object] | dict[str, KernelPlugin | object] | None = None,
+        arguments: KernelArguments | None = None,
+        bedrock_runtime_client: Any | None = None,
+        bedrock_client: Any | None = None,
+        **kwargs,
+    ) -> None:
+        """Initialize the Bedrock Agent.
+
+        Note that this only creates the agent object and does not create the agent in the service.
+
+        Args:
+            agent_model (BedrockAgentModel | dict[str, Any]): The agent model.
+            function_choice_behavior (FunctionChoiceBehavior, optional): The function choice behavior for accessing
+                the kernel functions and filters.
+            kernel (Kernel, optional): The kernel to use.
+            plugins (list[KernelPlugin | object] | dict[str, KernelPlugin | object], optional): The plugins to use.
+            arguments (KernelArguments, optional): The kernel arguments.
+                Invoke method arguments take precedence over the arguments provided here.
+            bedrock_runtime_client: The Bedrock Runtime Client.
+            bedrock_client: The Bedrock Client.
+            **kwargs: Additional keyword arguments.
+        """
+        args: dict[str, Any] = {
+            "agent_model": agent_model,
+            **kwargs,
+        }
+
+        if function_choice_behavior:
+            args["function_choice_behavior"] = function_choice_behavior
+        if kernel:
+            args["kernel"] = kernel
+        if plugins:
+            args["plugins"] = plugins
+        if arguments:
+            args["arguments"] = arguments
+        if bedrock_runtime_client:
+            args["bedrock_runtime_client"] = bedrock_runtime_client
+        if bedrock_client:
+            args["bedrock_client"] = bedrock_client
+
+        super().__init__(**args)
+
+    # region convenience class methods
+
+    @classmethod
+    async def create_and_prepare_agent(
+        cls,
+        name: str,
+        instructions: str,
+        *,
+        agent_resource_role_arn: str | None = None,
+        foundation_model: str | None = None,
+        bedrock_runtime_client: Any | None = None,
+        bedrock_client: Any | None = None,
+        kernel: Kernel | None = None,
+        plugins: list[KernelPlugin | object] | dict[str, KernelPlugin | object] | None = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        arguments: KernelArguments | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+    ) -> "BedrockAgent":
+        """Create a new agent asynchronously.
+
+        This is a convenience method that creates an instance of BedrockAgent and then creates the agent on the service.
+
+        Args:
+            name (str): The name of the agent.
+            instructions (str, optional): The instructions for the agent.
+            agent_resource_role_arn (str, optional): The ARN of the agent resource role.
+            foundation_model (str, optional): The foundation model.
+            bedrock_runtime_client (Any, optional): The Bedrock Runtime Client.
+            bedrock_client (Any, optional): The Bedrock Client.
+            kernel (Kernel, optional): The kernel to use.
+            plugins (list[KernelPlugin | object] | dict[str, KernelPlugin | object], optional): The plugins to use.
+            function_choice_behavior (FunctionChoiceBehavior, optional): The function choice behavior for accessing
+                the kernel functions and filters. Only FunctionChoiceType.AUTO is supported.
+            arguments (KernelArguments, optional): The kernel arguments.
+            prompt_template_config (PromptTemplateConfig, optional): The prompt template configuration.
+            env_file_path (str, optional): The path to the environment file.
+            env_file_encoding (str, optional): The encoding of the environment file.
+
+        Returns:
+            An instance of BedrockAgent with the created agent.
+        """
+        try:
+            bedrock_agent_settings = BedrockAgentSettings(
+                agent_resource_role_arn=agent_resource_role_arn,
+                foundation_model=foundation_model,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+            )
+        except ValidationError as e:
+            raise AgentInitializationException(f"Failed to initialize the Amazon Bedrock Agent settings: {e}") from e
+
+        import boto3
+        from botocore.exceptions import ClientError
+
+        bedrock_runtime_client = bedrock_runtime_client or boto3.client("bedrock-agent-runtime")
+        bedrock_client = bedrock_client or boto3.client("bedrock-agent")
+
+        try:
+            response = await run_in_executor(
+                None,
+                partial(
+                    bedrock_client.create_agent,
+                    agentName=name,
+                    foundationModel=bedrock_agent_settings.foundation_model,
+                    agentResourceRoleArn=bedrock_agent_settings.agent_resource_role_arn,
+                    instruction=instructions,
+                ),
+            )
+        except ClientError as e:
+            logger.error(f"Failed to create agent {name}.")
+            raise AgentInitializationException(f"Failed to create the Amazon Bedrock Agent: {e}") from e
+
+        bedrock_agent = cls(
+            response["agent"],
+            function_choice_behavior=function_choice_behavior,
+            kernel=kernel,
+            plugins=plugins,
+            arguments=arguments,
+            bedrock_runtime_client=bedrock_runtime_client,
+            bedrock_client=bedrock_client,
+        )
+
+        # The agent will first enter the CREATING status.
+        # When the operation finishes, it will enter the NOT_PREPARED status.
+        # We need to wait for the agent to reach the NOT_PREPARED status before we can prepare it.
+        await bedrock_agent._wait_for_agent_status(BedrockAgentStatus.NOT_PREPARED)
+        await bedrock_agent.prepare_agent_and_wait_until_prepared()
+
+        return bedrock_agent
+
+    # endregion
+
+    @trace_agent_get_response
+    @override
+    async def get_response(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        agent_alias: str | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs,
+    ) -> AgentResponseItem[ChatMessageContent]:
+        """Get a response from the agent.
+
+        Args:
+            messages (str | ChatMessageContent | list[str | ChatMessageContent]): The messages.
+            thread (AgentThread, optional): The thread. This is used to maintain the session state in the service.
+            agent_alias (str, optional): The agent alias.
+            arguments (KernelArguments, optional): The kernel arguments to override the current arguments.
+            kernel (Kernel, optional): The kernel to override the current kernel.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            A chat message content with the response.
+        """
+        if not isinstance(messages, str) and not isinstance(messages, ChatMessageContent):
+            raise AgentInvokeException("Messages must be a string or a ChatMessageContent for BedrockAgent.")
+
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: BedrockAgentThread(bedrock_runtime_client=self.bedrock_runtime_client),
+            expected_type=BedrockAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        kwargs.setdefault("streamingConfigurations", {})["streamFinalResponse"] = False
+        kwargs.setdefault("sessionState", {})
+
+        for _ in range(self.function_choice_behavior.maximum_auto_invoke_attempts):
+            response = await self._invoke_agent(thread.id, messages, agent_alias, **kwargs)
+
+            events: list[dict[str, Any]] = []
+            for event in response.get("completion", []):
+                events.append(event)
+
+            if any(BedrockAgentEventType.RETURN_CONTROL in event for event in events):
+                # Check if there is function call requests. If there are function calls,
+                # parse and invoke them and return the results back to the agent.
+                # Not yielding the function call results back to the user.
+                kwargs["sessionState"].update(
+                    await self._handle_return_control_event(
+                        next(event for event in events if BedrockAgentEventType.RETURN_CONTROL in event),
+                        kernel,
+                        arguments,
+                    )
+                )
+            else:
+                # For the rest of the events, the chunk will become the chat message content.
+                # If there are files or trace, they will be added to the chat message content.
+                file_items: list[BinaryContent] | None = None
+                trace_metadata: dict[str, Any] | None = None
+                chat_message_content: ChatMessageContent | None = None
+                for event in events:
+                    if BedrockAgentEventType.CHUNK in event:
+                        chat_message_content = self._handle_chunk_event(event)
+                    elif BedrockAgentEventType.FILES in event:
+                        file_items = self._handle_files_event(event)
+                    elif BedrockAgentEventType.TRACE in event:
+                        trace_metadata = self._handle_trace_event(event)
+
+                if not chat_message_content or not chat_message_content.content:
+                    raise AgentInvokeException("Chat message content is expected but not found in the response.")
+
+                if file_items:
+                    chat_message_content.items.extend(file_items)
+                if trace_metadata:
+                    chat_message_content.metadata.update({"trace": trace_metadata})
+
+                if not chat_message_content:
+                    raise AgentInvokeException("No response from the agent.")
+
+                chat_message_content.metadata["thread_id"] = thread.id
+                return AgentResponseItem(message=chat_message_content, thread=thread)
+
+        raise AgentInvokeException(
+            "Failed to get a response from the agent. Please consider increasing the auto invoke attempts."
+        )
+
+    @trace_agent_invocation
+    @override
+    async def invoke(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_new_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        agent_alias: str | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs,
+    ) -> AsyncIterable[AgentResponseItem[ChatMessageContent]]:
+        """Invoke an agent.
+
+        Args:
+            messages (str | ChatMessageContent | list[str | ChatMessageContent]): The messages.
+            thread (AgentThread, optional): The thread. This is used to maintain the session state in the service.
+            on_new_message: A callback function to handle intermediate steps of the agent's execution.
+            agent_alias (str, optional): The agent alias.
+            arguments (KernelArguments, optional): The kernel arguments to override the current arguments.
+            kernel (Kernel, optional): The kernel to override the current kernel.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            An async iterable of chat message content.
+        """
+        if not isinstance(messages, str) and not isinstance(messages, ChatMessageContent):
+            raise AgentInvokeException("Messages must be a string or a ChatMessageContent for BedrockAgent.")
+
+        if on_new_message:
+            logger.warning("The on_new_message callback is not supported for BedrockAgent.")
+
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: BedrockAgentThread(bedrock_runtime_client=self.bedrock_runtime_client),
+            expected_type=BedrockAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        kwargs.setdefault("streamingConfigurations", {})["streamFinalResponse"] = False
+        kwargs.setdefault("sessionState", {})
+
+        for _ in range(self.function_choice_behavior.maximum_auto_invoke_attempts):
+            response = await self._invoke_agent(thread.id, messages, agent_alias, **kwargs)
+
+            events: list[dict[str, Any]] = []
+            for event in response.get("completion", []):
+                events.append(event)
+
+            if any(BedrockAgentEventType.RETURN_CONTROL in event for event in events):
+                # Check if there is function call requests. If there are function calls,
+                # parse and invoke them and return the results back to the agent.
+                # Not yielding the function call results back to the user.
+                kwargs["sessionState"].update(
+                    await self._handle_return_control_event(
+                        next(event for event in events if BedrockAgentEventType.RETURN_CONTROL in event),
+                        kernel,
+                        arguments,
+                    )
+                )
+            else:
+                for event in events:
+                    if BedrockAgentEventType.CHUNK in event:
+                        cmc = self._handle_chunk_event(event)
+                        cmc.metadata["thread_id"] = thread.id
+                        yield AgentResponseItem(message=cmc, thread=thread)
+                    elif BedrockAgentEventType.FILES in event:
+                        cmc = ChatMessageContent(
+                            role=AuthorRole.ASSISTANT,
+                            items=self._handle_files_event(event),  # type: ignore
+                            name=self.name,
+                            inner_content=event,
+                            ai_model_id=self.agent_model.foundation_model,
+                        )
+                        cmc.metadata["thread_id"] = thread.id
+                        yield AgentResponseItem(message=cmc, thread=thread)
+                    elif BedrockAgentEventType.TRACE in event:
+                        cmc = ChatMessageContent(
+                            role=AuthorRole.ASSISTANT,
+                            name=self.name,
+                            content="",
+                            inner_content=event,
+                            ai_model_id=self.agent_model.foundation_model,
+                            metadata=self._handle_trace_event(event),
+                        )
+                        cmc.metadata["thread_id"] = thread.id
+                        yield AgentResponseItem(message=cmc, thread=thread)
+
+                return
+
+        raise AgentInvokeException(
+            "Failed to get a response from the agent. Please consider increasing the auto invoke attempts."
+        )
+
+    @trace_agent_streaming_invocation
+    @override
+    async def invoke_stream(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_new_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        agent_alias: str | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs,
+    ) -> AsyncIterable[AgentResponseItem[StreamingChatMessageContent]]:
+        """Invoke an agent with streaming.
+
+        Args:
+            messages (str | ChatMessageContent | list[str | ChatMessageContent]): The messages.
+            thread (AgentThread, optional): The thread. This is used to maintain the session state in the service.
+            on_new_message: A callback function to handle intermediate steps of the
+                            agent's execution as fully formed messages.
+            agent_alias (str, optional): The agent alias.
+            arguments (KernelArguments, optional): The kernel arguments to override the current arguments.
+            kernel (Kernel, optional): The kernel to override the current kernel.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            An async iterable of streaming chat message content
+        """
+        if not isinstance(messages, str) and not isinstance(messages, ChatMessageContent):
+            raise AgentInvokeException("Messages must be a string or a ChatMessageContent for BedrockAgent.")
+
+        if on_new_message:
+            logger.warning("The on_new_message callback is not supported for BedrockAgent.")
+
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: BedrockAgentThread(bedrock_runtime_client=self.bedrock_runtime_client),
+            expected_type=BedrockAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        kwargs.setdefault("streamingConfigurations", {})["streamFinalResponse"] = True
+        kwargs.setdefault("sessionState", {})
+
+        for request_index in range(self.function_choice_behavior.maximum_auto_invoke_attempts):
+            response = await self._invoke_agent(thread.id, messages, agent_alias, **kwargs)
+
+            all_function_call_messages: list[StreamingChatMessageContent] = []
+            for event in response.get("completion", []):
+                if BedrockAgentEventType.CHUNK in event:
+                    scmc = self._handle_streaming_chunk_event(event)
+                    scmc.metadata["thread_id"] = thread.id
+                    yield AgentResponseItem(message=scmc, thread=thread)
+                    continue
+                if BedrockAgentEventType.FILES in event:
+                    scmc = self._handle_streaming_files_event(event)
+                    scmc.metadata["thread_id"] = thread.id
+                    yield AgentResponseItem(message=scmc, thread=thread)
+                    continue
+                if BedrockAgentEventType.TRACE in event:
+                    scmc = self._handle_streaming_trace_event(event)
+                    scmc.metadata["thread_id"] = thread.id
+                    yield AgentResponseItem(message=scmc, thread=thread)
+                    continue
+                if BedrockAgentEventType.RETURN_CONTROL in event:
+                    all_function_call_messages.append(self._handle_streaming_return_control_event(event))
+                    continue
+
+            if not all_function_call_messages:
+                return
+
+            full_message: StreamingChatMessageContent = reduce(lambda x, y: x + y, all_function_call_messages)
+            function_calls = [item for item in full_message.items if isinstance(item, FunctionCallContent)]
+            function_result_contents = await self._handle_function_call_contents(function_calls)
+            kwargs["sessionState"].update({
+                "invocationId": function_calls[0].id,
+                "returnControlInvocationResults": parse_function_result_contents(function_result_contents),
+            })
+
+    # region non streaming Event Handlers
+
+    def _handle_chunk_event(self, event: dict[str, Any]) -> ChatMessageContent:
+        """Create a chat message content."""
+        chunk = event[BedrockAgentEventType.CHUNK]
+        completion = chunk["bytes"].decode()
+
+        return ChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            content=completion,
+            name=self.name,
+            inner_content=event,
+            ai_model_id=self.agent_model.foundation_model,
+            metadata=chunk,
+        )
+
+    async def _handle_return_control_event(
+        self,
+        event: dict[str, Any],
+        kernel: Kernel,
+        kernel_arguments: KernelArguments,
+    ) -> dict[str, Any]:
+        """Handle return control event."""
+        return_control_payload = event[BedrockAgentEventType.RETURN_CONTROL]
+        function_calls = parse_return_control_payload(return_control_payload)
+        if not function_calls:
+            raise AgentInvokeException("Function call is expected but not found in the response.")
+
+        function_result_contents = await self._handle_function_call_contents(function_calls)
+
+        return {
+            "invocationId": function_calls[0].id,
+            "returnControlInvocationResults": parse_function_result_contents(function_result_contents),
+        }
+
+    def _handle_files_event(self, event: dict[str, Any]) -> list[BinaryContent]:
+        """Handle file event."""
+        files_event = event[BedrockAgentEventType.FILES]
+        return [
+            BinaryContent(
+                data=file["bytes"],
+                data_format="base64",
+                mime_type=file["type"],
+                metadata={"name": self._sanitize_filename(file["name"])},
+            )
+            for file in files_event["files"]
+        ]
+
+    def _handle_trace_event(self, event: dict[str, Any]) -> dict[str, Any]:
+        """Handle trace event."""
+        return event[BedrockAgentEventType.TRACE]
+
+    # endregion
+
+    # region streaming Event Handlers
+
+    def _handle_streaming_chunk_event(self, event: dict[str, Any]) -> StreamingChatMessageContent:
+        """Handle streaming chunk event."""
+        chunk = event[BedrockAgentEventType.CHUNK]
+        completion = chunk["bytes"].decode()
+
+        return StreamingChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            choice_index=0,
+            content=completion,
+            name=self.name,
+            inner_content=event,
+            ai_model_id=self.agent_model.foundation_model,
+        )
+
+    def _handle_streaming_return_control_event(self, event: dict[str, Any]) -> StreamingChatMessageContent:
+        """Handle streaming return control event."""
+        return_control_payload = event[BedrockAgentEventType.RETURN_CONTROL]
+        function_calls = parse_return_control_payload(return_control_payload)
+
+        return StreamingChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            choice_index=0,
+            items=function_calls,  # type: ignore
+            name=self.name,
+            inner_content=event,
+            ai_model_id=self.agent_model.foundation_model,
+        )
+
+    def _handle_streaming_files_event(self, event: dict[str, Any]) -> StreamingChatMessageContent:
+        """Handle streaming file event."""
+        files_event = event[BedrockAgentEventType.FILES]
+        items: list[BinaryContent] = [
+            BinaryContent(
+                data=file["bytes"],
+                data_format="base64",
+                mime_type=file["type"],
+                metadata={"name": self._sanitize_filename(file["name"])},
+            )
+            for file in files_event["files"]
+        ]
+
+        return StreamingChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            choice_index=0,
+            items=items,  # type: ignore
+            name=self.name,
+            inner_content=event,
+            ai_model_id=self.agent_model.foundation_model,
+        )
+
+    def _handle_streaming_trace_event(self, event: dict[str, Any]) -> StreamingChatMessageContent:
+        """Handle streaming trace event."""
+        return StreamingChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            choice_index=0,
+            items=[],
+            name=self.name,
+            inner_content=event,
+            ai_model_id=self.agent_model.foundation_model,
+            metadata=event[BedrockAgentEventType.TRACE],
+        )
+
+    # endregion
+
+    async def _handle_function_call_contents(
+        self,
+        function_call_contents: list[FunctionCallContent],
+    ) -> list[FunctionResultContent]:
+        """Handle function call contents."""
+        chat_history = ChatHistory()
+        await asyncio.gather(
+            *[
+                self.kernel.invoke_function_call(
+                    function_call=function_call,
+                    chat_history=chat_history,
+                    arguments=self.arguments,
+                    function_call_count=len(function_call_contents),
+                    function_behavior=self.function_choice_behavior,
+                )
+                for function_call in function_call_contents
+            ],
+        )
+
+        return [
+            item
+            for chat_message in chat_history.messages
+            for item in chat_message.items
+            if isinstance(item, FunctionResultContent)
+        ]
+
+    async def create_channel(self, thread_id: str | None = None) -> AgentChannel:
+        """Create a ChatHistoryChannel.
+
+        Args:
+            chat_history: The chat history for the channel. If None, a new ChatHistory instance will be created.
+            thread_id: The ID of the thread. If None, a new thread will be created.
+
+        Returns:
+            An instance of AgentChannel.
+        """
+        from semantic_kernel.agents.bedrock.bedrock_agent import BedrockAgentThread
+
+        BedrockAgentChannel.model_rebuild()
+
+        thread = BedrockAgentThread(bedrock_runtime_client=self.bedrock_runtime_client, session_id=thread_id)
+
+        if thread.id is None:
+            await thread.create()
+
+        return BedrockAgentChannel(thread=thread)
+
+    @override
+    async def _notify_thread_of_new_message(self, thread, new_message):
+        """Bedrock agent doesn't need to notify the thread of new messages.
+
+        The new message is passed to the agent when invoking the agent.
+        """
+        pass
+
+    @staticmethod
+    def _sanitize_filename(filename: str) -> str:
+        """Sanitize filename to prevent directory traversal attacks.
+
+        Args:
+            filename: The filename to sanitize.
+
+        Returns:
+            The sanitized filename with directory components removed.
+        """
+        # Extract basename to remove any directory traversal attempts
+        # Handle both Unix and Windows path separators
+        sanitized = os.path.basename(filename.replace("\\", "/"))
+        # Remove any remaining path separators or null bytes
+        result = sanitized.replace("/", "").replace("\\", "").replace("\x00", "")
+        if result != filename:
+            logger.warning(
+                f"Filename contained potentially malicious path components and was sanitized: "
+                f"'{filename}' -> '{result}'"
+            )
+        return result

python/semantic_kernel/agents/bedrock/bedrock_agent_base.py

@@ -0,0 +1,381 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+from functools import partial
+from typing import Any, ClassVar
+
+import boto3
+from botocore.exceptions import ClientError
+from pydantic import Field, field_validator
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.bedrock.action_group_utils import kernel_function_to_bedrock_function_schema
+from semantic_kernel.agents.bedrock.models.bedrock_action_group_model import BedrockActionGroupModel
+from semantic_kernel.agents.bedrock.models.bedrock_agent_model import BedrockAgentModel
+from semantic_kernel.agents.bedrock.models.bedrock_agent_status import BedrockAgentStatus
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior, FunctionChoiceType
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.utils.async_utils import run_in_executor
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+logger = logging.getLogger(__name__)
+
+
+@experimental
+class BedrockAgentBase(Agent):
+    """Bedrock Agent Base Class to provide common functionalities for Bedrock Agents."""
+
+    # There is a default alias created by Bedrock for the working draft version of the agent.
+    # https://docs.aws.amazon.com/bedrock/latest/userguide/agents-deploy.html
+    WORKING_DRAFT_AGENT_ALIAS: ClassVar[str] = "TSTALIASID"
+
+    # Amazon Bedrock Clients
+    # Runtime Client: Use for inference
+    bedrock_runtime_client: Any
+    # Client: Use for model management
+    bedrock_client: Any
+    # Function Choice Behavior: this is primarily used to control the behavior of the kernel when
+    # the agent requests functions, and to configure the kernel function action group (i.e. via filters).
+    # When this is None, users won't be able to create a kernel function action groups.
+    function_choice_behavior: FunctionChoiceBehavior = Field(default=FunctionChoiceBehavior.Auto())
+    # Agent Model: stores the agent information
+    agent_model: BedrockAgentModel
+
+    def __init__(
+        self,
+        agent_model: BedrockAgentModel | dict[str, Any],
+        *,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        bedrock_runtime_client: Any | None = None,
+        bedrock_client: Any | None = None,
+        **kwargs,
+    ) -> None:
+        """Initialize the Bedrock Agent Base.
+
+        Args:
+            agent_model: The Bedrock Agent Model.
+            function_choice_behavior: The function choice behavior.
+            bedrock_client: The Bedrock Client.
+            bedrock_runtime_client: The Bedrock Runtime Client.
+            kwargs: Additional keyword arguments.
+        """
+        agent_model = (
+            agent_model if isinstance(agent_model, BedrockAgentModel) else BedrockAgentModel.model_validate(agent_model)
+        )
+
+        args = {
+            "agent_model": agent_model,
+            "id": agent_model.agent_id,
+            "name": agent_model.agent_name,
+            "bedrock_runtime_client": bedrock_runtime_client or boto3.client("bedrock-agent-runtime"),
+            "bedrock_client": bedrock_client or boto3.client("bedrock-agent"),
+            **kwargs,
+        }
+        if function_choice_behavior:
+            args["function_choice_behavior"] = function_choice_behavior
+
+        super().__init__(**args)
+
+    @field_validator("function_choice_behavior", mode="after")
+    @classmethod
+    def validate_function_choice_behavior(
+        cls, function_choice_behavior: FunctionChoiceBehavior | None
+    ) -> FunctionChoiceBehavior | None:
+        """Validate the function choice behavior."""
+        if function_choice_behavior and function_choice_behavior.type_ != FunctionChoiceType.AUTO:
+            # Users cannot specify REQUIRED or NONE for the Bedrock agents.
+            # Please note that the function choice behavior only control if the kernel will automatically
+            # execute the functions the agent requests. It does not control the behavior of the agent.
+            raise ValueError("Only FunctionChoiceType.AUTO is supported.")
+        return function_choice_behavior
+
+    def __repr__(self):
+        """Return the string representation of the Bedrock Agent."""
+        return f"{self.agent_model}"
+
+    # region Agent Management
+
+    async def prepare_agent_and_wait_until_prepared(self) -> None:
+        """Prepare the agent for use."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before preparing it.")
+
+        try:
+            await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.prepare_agent,
+                    agentId=self.agent_model.agent_id,
+                ),
+            )
+
+            # The agent will take some time to enter the PREPARING status after the prepare operation is called.
+            # We need to wait for the agent to reach the PREPARING status before we can proceed, otherwise we
+            # will return immediately if the agent is already in PREPARED status.
+            await self._wait_for_agent_status(BedrockAgentStatus.PREPARING)
+            # The agent will enter the PREPARED status when the preparation is complete.
+            await self._wait_for_agent_status(BedrockAgentStatus.PREPARED)
+        except ClientError as e:
+            logger.error(f"Failed to prepare agent {self.agent_model.agent_id}.")
+            raise e
+
+    async def delete_agent(self, **kwargs) -> None:
+        """Delete an agent asynchronously."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before deleting it.")
+
+        try:
+            await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.delete_agent,
+                    agentId=self.agent_model.agent_id,
+                    **kwargs,
+                ),
+            )
+
+            self.agent_model.agent_id = None
+        except ClientError as e:
+            logger.error(f"Failed to delete agent {self.agent_model.agent_id}.")
+            raise e
+
+    async def _get_agent(self) -> None:
+        """Get an agent."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before getting it.")
+
+        try:
+            response = await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.get_agent,
+                    agentId=self.agent_model.agent_id,
+                ),
+            )
+
+            # Update the agent model
+            self.agent_model = BedrockAgentModel(**response["agent"])
+        except ClientError as e:
+            logger.error(f"Failed to get agent {self.agent_model.agent_id}.")
+            raise e
+
+    async def _wait_for_agent_status(
+        self,
+        status: BedrockAgentStatus,
+        interval: int = 2,
+        max_attempts: int = 5,
+    ) -> None:
+        """Wait for the agent to reach a specific status."""
+        for _ in range(max_attempts):
+            await self._get_agent()
+            if self.agent_model.agent_status == status:
+                return
+
+            await asyncio.sleep(interval)
+
+        raise TimeoutError(
+            f"Agent did not reach status {status} within the specified time."
+            f" Current status: {self.agent_model.agent_status}"
+        )
+
+    # endregion Agent Management
+
+    # region Action Group Management
+    async def create_code_interpreter_action_group(self, **kwargs) -> BedrockActionGroupModel:
+        """Create a code interpreter action group."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before creating an action group for it.")
+
+        try:
+            response = await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.create_agent_action_group,
+                    agentId=self.agent_model.agent_id,
+                    agentVersion=self.agent_model.agent_version or "DRAFT",
+                    actionGroupName=f"{self.agent_model.agent_name}_code_interpreter",
+                    actionGroupState="ENABLED",
+                    parentActionGroupSignature="AMAZON.CodeInterpreter",
+                    **kwargs,
+                ),
+            )
+
+            await self.prepare_agent_and_wait_until_prepared()
+
+            return BedrockActionGroupModel(**response["agentActionGroup"])
+        except ClientError as e:
+            logger.error(f"Failed to create code interpreter action group for agent {self.agent_model.agent_id}.")
+            raise e
+
+    async def create_user_input_action_group(self, **kwargs) -> BedrockActionGroupModel:
+        """Create a user input action group."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before creating an action group for it.")
+
+        try:
+            response = await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.create_agent_action_group,
+                    agentId=self.agent_model.agent_id,
+                    agentVersion=self.agent_model.agent_version or "DRAFT",
+                    actionGroupName=f"{self.agent_model.agent_name}_user_input",
+                    actionGroupState="ENABLED",
+                    parentActionGroupSignature="AMAZON.UserInput",
+                    **kwargs,
+                ),
+            )
+
+            await self.prepare_agent_and_wait_until_prepared()
+
+            return BedrockActionGroupModel(**response["agentActionGroup"])
+        except ClientError as e:
+            logger.error(f"Failed to create user input action group for agent {self.agent_model.agent_id}.")
+            raise e
+
+    async def create_kernel_function_action_group(self, **kwargs) -> BedrockActionGroupModel | None:
+        """Create a kernel function action group."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before creating an action group for it.")
+
+        function_call_choice_config = self.function_choice_behavior.get_config(self.kernel)
+        if not function_call_choice_config.available_functions:
+            logger.warning("No available functions. Skipping kernel function action group creation.")
+            return None
+
+        try:
+            response = await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.create_agent_action_group,
+                    agentId=self.agent_model.agent_id,
+                    agentVersion=self.agent_model.agent_version or "DRAFT",
+                    actionGroupName=f"{self.agent_model.agent_name}_kernel_function",
+                    actionGroupState="ENABLED",
+                    actionGroupExecutor={"customControl": "RETURN_CONTROL"},
+                    functionSchema=kernel_function_to_bedrock_function_schema(function_call_choice_config),
+                    **kwargs,
+                ),
+            )
+
+            await self.prepare_agent_and_wait_until_prepared()
+
+            return BedrockActionGroupModel(**response["agentActionGroup"])
+        except ClientError as e:
+            logger.error(f"Failed to create kernel function action group for agent {self.agent_model.agent_id}.")
+            raise e
+
+    # endregion Action Group Management
+
+    # region Knowledge Base Management
+
+    async def associate_agent_knowledge_base(self, knowledge_base_id: str, **kwargs) -> dict[str, Any]:
+        """Associate an agent with a knowledge base."""
+        if not self.agent_model.agent_id:
+            raise ValueError(
+                "Agent does not exist. Please create the agent before associating it with a knowledge base."
+            )
+
+        try:
+            response = await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.associate_agent_knowledge_base,
+                    agentId=self.agent_model.agent_id,
+                    agentVersion=self.agent_model.agent_version,
+                    knowledgeBaseId=knowledge_base_id,
+                    **kwargs,
+                ),
+            )
+
+            await self.prepare_agent_and_wait_until_prepared()
+
+            return response
+        except ClientError as e:
+            logger.error(
+                f"Failed to associate agent {self.agent_model.agent_id} with knowledge base {knowledge_base_id}."
+            )
+            raise e
+
+    async def disassociate_agent_knowledge_base(self, knowledge_base_id: str, **kwargs) -> None:
+        """Disassociate an agent with a knowledge base."""
+        if not self.agent_model.agent_id:
+            raise ValueError(
+                "Agent does not exist. Please create the agent before disassociating it with a knowledge base."
+            )
+
+        try:
+            response = await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.disassociate_agent_knowledge_base,
+                    agentId=self.agent_model.agent_id,
+                    agentVersion=self.agent_model.agent_version,
+                    knowledgeBaseId=knowledge_base_id,
+                    **kwargs,
+                ),
+            )
+
+            await self.prepare_agent_and_wait_until_prepared()
+
+            return response
+        except ClientError as e:
+            logger.error(
+                f"Failed to disassociate agent {self.agent_model.agent_id} with knowledge base {knowledge_base_id}."
+            )
+            raise e
+
+    async def list_associated_agent_knowledge_bases(self, **kwargs) -> dict[str, Any]:
+        """List associated knowledge bases with an agent."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before listing associated knowledge bases.")
+
+        try:
+            return await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_client.list_agent_knowledge_bases,
+                    agentId=self.agent_model.agent_id,
+                    agentVersion=self.agent_model.agent_version,
+                    **kwargs,
+                ),
+            )
+        except ClientError as e:
+            logger.error(f"Failed to list associated knowledge bases for agent {self.agent_model.agent_id}.")
+            raise e
+
+    # endregion Knowledge Base Management
+
+    async def _invoke_agent(
+        self,
+        thread_id: str,
+        message: str | ChatMessageContent,
+        agent_alias: str | None = None,
+        **kwargs,
+    ) -> dict[str, Any]:
+        """Invoke an agent."""
+        if not self.agent_model.agent_id:
+            raise ValueError("Agent does not exist. Please create the agent before invoking it.")
+
+        if isinstance(message, ChatMessageContent) and message.role != AuthorRole.USER:
+            raise ValueError("Only user messages are supported for invoking a Bedrock agent.")
+
+        agent_alias = agent_alias or self.WORKING_DRAFT_AGENT_ALIAS
+
+        try:
+            return await run_in_executor(
+                None,
+                partial(
+                    self.bedrock_runtime_client.invoke_agent,
+                    agentAliasId=agent_alias,
+                    agentId=self.agent_model.agent_id,
+                    sessionId=thread_id,
+                    inputText=message if isinstance(message, str) else message.content,
+                    **kwargs,
+                ),
+            )
+        except ClientError as e:
+            logger.error(f"Failed to invoke agent {self.agent_model.agent_id}.")
+            raise e

python/semantic_kernel/agents/bedrock/bedrock_agent_settings.py

@@ -0,0 +1,32 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import ClassVar
+
+from semantic_kernel.kernel_pydantic import KernelBaseSettings
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class BedrockAgentSettings(KernelBaseSettings):
+    """Amazon Bedrock Agent service settings.
+
+    The settings are first loaded from environment variables with
+    the prefix 'BEDROCK_AGENT_'.
+    If the environment variables are not found, the settings can
+    be loaded from a .env file with the encoding 'utf-8'.
+    If the settings are not found in the .env file, the settings
+    are ignored; however, validation will fail alerting that the
+    settings are missing.
+
+    Optional settings for prefix 'BEDROCK_' are:
+        - agent_resource_role_arn: str - The Amazon Bedrock agent resource role ARN.
+            https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
+            (Env var BEDROCK_AGENT_AGENT_RESOURCE_ROLE_ARN)
+        - foundation_model: str - The Amazon Bedrock foundation model ID to use.
+            (Env var BEDROCK_AGENT_FOUNDATION_MODEL)
+    """
+
+    env_prefix: ClassVar[str] = "BEDROCK_AGENT_"
+
+    agent_resource_role_arn: str
+    foundation_model: str

python/semantic_kernel/agents/bedrock/models/bedrock_action_group_model.py

@@ -0,0 +1,21 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from pydantic import ConfigDict, Field
+
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class BedrockActionGroupModel(KernelBaseModel):
+    """Bedrock Action Group Model.
+
+    Model field definitions for the Amazon Bedrock Action Group Service:
+    https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent/client/create_agent_action_group.html
+    """
+
+    # This model_config will merge with the KernelBaseModel.model_config
+    model_config = ConfigDict(extra="allow")
+
+    action_group_id: str = Field(..., alias="actionGroupId", description="The unique identifier of the action group.")
+    action_group_name: str = Field(..., alias="actionGroupName", description="The name of the action group.")

python/semantic_kernel/agents/bedrock/models/bedrock_agent_event_type.py

@@ -0,0 +1,19 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from enum import Enum
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class BedrockAgentEventType(str, Enum):
+    """Bedrock Agent Event Type."""
+
+    # Contains the text response from the agent.
+    CHUNK = "chunk"
+    # Contains the trace information (reasoning process) from the agent.
+    TRACE = "trace"
+    # Contains the function call requests from the agent.
+    RETURN_CONTROL = "returnControl"
+    # Contains the files generated by the agent using the code interpreter.
+    FILES = "files"

python/semantic_kernel/agents/bedrock/models/bedrock_agent_model.py

@@ -0,0 +1,24 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from pydantic import ConfigDict, Field
+
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class BedrockAgentModel(KernelBaseModel):
+    """Bedrock Agent Model.
+
+    Model field definitions for the Amazon Bedrock Agent Service:
+    https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent/client/create_agent.html
+    """
+
+    # This model_config will merge with the KernelBaseModel.model_config
+    model_config = ConfigDict(extra="allow")
+
+    agent_id: str | None = Field(default=None, alias="agentId", description="The unique identifier of the agent.")
+    agent_name: str | None = Field(default=None, alias="agentName", description="The name of the agent.")
+    agent_version: str | None = Field(default=None, alias="agentVersion", description="The version of the agent.")
+    foundation_model: str | None = Field(default=None, alias="foundationModel", description="The foundation model.")
+    agent_status: str | None = Field(default=None, alias="agentStatus", description="The status of the agent.")

python/semantic_kernel/agents/bedrock/models/bedrock_agent_status.py

@@ -0,0 +1,23 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+from enum import Enum
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class BedrockAgentStatus(str, Enum):
+    """Bedrock Agent Status.
+
+    https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_PrepareAgent.html#API_agent_PrepareAgent_ResponseElements
+    """
+
+    CREATING = "CREATING"
+    PREPARING = "PREPARING"
+    PREPARED = "PREPARED"
+    NOT_PREPARED = "NOT_PREPARED"
+    DELETING = "DELETING"
+    FAILED = "FAILED"
+    VERSIONING = "VERSIONING"
+    UPDATING = "UPDATING"

python/semantic_kernel/agents/channels/agent_channel.py

@@ -0,0 +1,85 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from abc import ABC, abstractmethod
+from collections.abc import AsyncIterable
+from typing import TYPE_CHECKING, Any
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.agents.agent import Agent
+    from semantic_kernel.contents.chat_message_content import ChatMessageContent
+
+
+@experimental
+class AgentChannel(ABC):
+    """Defines the communication protocol for a particular Agent type.
+
+    An agent provides it own AgentChannel via CreateChannel.
+    """
+
+    @abstractmethod
+    async def receive(
+        self,
+        history: list["ChatMessageContent"],
+    ) -> None:
+        """Receive the conversation messages.
+
+        Used when joining a conversation and also during each agent interaction.
+
+        Args:
+            history: The history of messages in the conversation.
+        """
+        ...
+
+    @abstractmethod
+    def invoke(
+        self,
+        agent: "Agent",
+        **kwargs: Any,
+    ) -> AsyncIterable[tuple[bool, "ChatMessageContent"]]:
+        """Perform a discrete incremental interaction between a single Agent and AgentChat.
+
+        Args:
+            agent: The agent to interact with.
+            kwargs: The keyword arguments.
+
+        Returns:
+            An async iterable of a bool, ChatMessageContent.
+        """
+        ...
+
+    @abstractmethod
+    def invoke_stream(
+        self,
+        agent: "Agent",
+        messages: "list[ChatMessageContent]",
+        **kwargs: Any,
+    ) -> AsyncIterable["ChatMessageContent"]:
+        """Perform a discrete incremental stream interaction between a single Agent and AgentChat.
+
+        Args:
+            agent: The agent to interact with.
+            messages: The history of messages in the conversation.
+            kwargs: The keyword arguments.
+
+        Returns:
+            An async iterable ChatMessageContent.
+        """
+        ...
+
+    @abstractmethod
+    def get_history(
+        self,
+    ) -> AsyncIterable["ChatMessageContent"]:
+        """Retrieve the message history specific to this channel.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        ...
+
+    @abstractmethod
+    async def reset(self) -> None:
+        """Reset any persistent state associated with the channel."""
+        ...

python/semantic_kernel/agents/channels/bedrock_agent_channel.py

@@ -0,0 +1,217 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+from collections.abc import AsyncIterable
+from typing import TYPE_CHECKING, Any, ClassVar
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import AgentChatException
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.agents.bedrock.bedrock_agent import BedrockAgentThread
+
+logger = logging.getLogger(__name__)
+
+
+@experimental
+class BedrockAgentChannel(AgentChannel, ChatHistory):
+    """An AgentChannel for a BedrockAgent that is based on a ChatHistory.
+
+    The chat history will override the session state when invoking the agent.
+
+    This channel allows Bedrock agents to interact with other types of agents in Semantic Kernel in an AgentGroupChat.
+    However, since Bedrock agents require the chat history to alternate between user and agent messages, this channel
+    will preprocess the chat history to ensure that it meets the requirements of the Bedrock agent. When an invalid
+    pattern is detected, the channel will insert a placeholder user or assistant message to ensure that the chat history
+    alternates between user and agent messages.
+    """
+
+    thread: "BedrockAgentThread"
+    MESSAGE_PLACEHOLDER: ClassVar[str] = "[SILENCE]"
+
+    @override
+    async def invoke(self, agent: "Agent", **kwargs: Any) -> AsyncIterable[tuple[bool, ChatMessageContent]]:
+        """Perform a discrete incremental interaction between a single Agent and AgentChat.
+
+        Args:
+            agent: The agent to interact with.
+            kwargs: Additional keyword arguments.
+
+        Returns:
+            An async iterable of ChatMessageContent with a boolean indicating if the
+            message should be visible external to the agent.
+        """
+        from semantic_kernel.agents.bedrock.bedrock_agent import BedrockAgent
+
+        if not isinstance(agent, BedrockAgent):
+            raise AgentChatException(f"Agent is not of the expected type {type(BedrockAgent)}.")
+        if not self.messages:
+            # This is not supposed to happen, as the channel won't get invoked
+            # before it has received messages. This is just extra safety.
+            raise AgentChatException("No chat history available.")
+
+        # Preprocess chat history
+        await self._ensure_history_alternates()
+        await self._ensure_last_message_is_user()
+
+        async for response in agent.invoke(
+            messages=self.messages[-1].content,
+            thread=self.thread,
+            sessionState=await self._parse_chat_history_to_session_state(),
+        ):
+            # All messages from Bedrock agents are user facing, i.e., function calls are not returned as messages
+            self.messages.append(response.message)
+            yield True, response.message
+
+    @override
+    async def invoke_stream(
+        self,
+        agent: "Agent",
+        messages: list[ChatMessageContent],
+        **kwargs: Any,
+    ) -> AsyncIterable[ChatMessageContent]:
+        """Perform a streaming interaction between a single Agent and AgentChat.
+
+        Args:
+            agent: The agent to interact with.
+            messages: The history of messages in the conversation.
+            kwargs: Additional keyword arguments.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        from semantic_kernel.agents.bedrock.bedrock_agent import BedrockAgent
+
+        if not isinstance(agent, BedrockAgent):
+            raise AgentChatException(f"Agent is not of the expected type {type(BedrockAgent)}.")
+        if not self.messages:
+            raise AgentChatException("No chat history available.")
+
+        # Preprocess chat history
+        await self._ensure_history_alternates()
+        await self._ensure_last_message_is_user()
+
+        full_message: list[StreamingChatMessageContent] = []
+        async for response_chunk in agent.invoke_stream(
+            messages=self.messages[-1].content,
+            thread=self.thread,
+            sessionState=await self._parse_chat_history_to_session_state(),
+        ):
+            yield response_chunk.message
+            full_message.append(response_chunk.message)
+
+        messages.append(
+            ChatMessageContent(
+                role=AuthorRole.ASSISTANT,
+                content="".join([message.content for message in full_message]),
+                name=agent.name,
+                inner_content=full_message,
+                ai_model_id=agent.agent_model.foundation_model,
+            )
+        )
+
+    @override
+    async def receive(
+        self,
+        history: list[ChatMessageContent],
+    ) -> None:
+        """Receive the conversation messages.
+
+        Bedrock requires the chat history to alternate between user and agent messages.
+        Thus, when receiving the history, the message sequence will be mutated by inserting
+        empty agent or user messages as needed.
+
+        Args:
+            history: The history of messages in the conversation.
+        """
+        for incoming_message in history:
+            if not self.messages or self.messages[-1].role != incoming_message.role:
+                self.messages.append(incoming_message)
+            else:
+                self.messages.append(
+                    ChatMessageContent(
+                        role=AuthorRole.ASSISTANT if incoming_message.role == AuthorRole.USER else AuthorRole.USER,
+                        content=self.MESSAGE_PLACEHOLDER,
+                    )
+                )
+                self.messages.append(incoming_message)
+
+    @override
+    async def get_history(  # type: ignore
+        self,
+    ) -> AsyncIterable[ChatMessageContent]:
+        """Retrieve the message history specific to this channel.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        for message in reversed(self.messages):
+            yield message
+
+    @override
+    async def reset(self) -> None:
+        """Reset the channel state."""
+        self.messages.clear()
+
+    # region chat history preprocessing and parsing
+
+    async def _ensure_history_alternates(self):
+        """Ensure that the chat history alternates between user and agent messages."""
+        if not self.messages or len(self.messages) == 1:
+            return
+
+        current_index = 1
+        while current_index < len(self.messages):
+            if self.messages[current_index].role == self.messages[current_index - 1].role:
+                self.messages.insert(
+                    current_index,
+                    ChatMessageContent(
+                        role=AuthorRole.ASSISTANT
+                        if self.messages[current_index].role == AuthorRole.USER
+                        else AuthorRole.USER,
+                        content=self.MESSAGE_PLACEHOLDER,
+                    ),
+                )
+                current_index += 2
+            else:
+                current_index += 1
+
+    async def _ensure_last_message_is_user(self):
+        """Ensure that the last message in the chat history is a user message."""
+        if self.messages and self.messages[-1].role == AuthorRole.ASSISTANT:
+            self.messages.append(
+                ChatMessageContent(
+                    role=AuthorRole.USER,
+                    content=self.MESSAGE_PLACEHOLDER,
+                )
+            )
+
+    async def _parse_chat_history_to_session_state(self) -> dict[str, Any]:
+        """Parse the chat history to a session state."""
+        session_state: dict[str, Any] = {"conversationHistory": {"messages": []}}
+        if len(self.messages) > 1:
+            # We don't take the last message as it needs to be sent separately in another parameter
+            for message in self.messages[:-1]:
+                if message.role not in [AuthorRole.USER, AuthorRole.ASSISTANT]:
+                    logger.debug(f"Skipping message with unsupported role: {message}")
+                    continue
+                session_state["conversationHistory"]["messages"].append({
+                    "content": [{"text": message.content}],
+                    "role": message.role.value,
+                })
+
+        return session_state
+
+    # endregion

python/semantic_kernel/agents/channels/chat_history_channel.py

@@ -0,0 +1,171 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import sys
+from collections import deque
+from collections.abc import AsyncIterable
+from copy import deepcopy
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from typing import TYPE_CHECKING, Any, ClassVar, Deque
+
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.contents import ChatMessageContent
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.function_result_content import FunctionResultContent
+from semantic_kernel.contents.image_content import ImageContent
+from semantic_kernel.contents.streaming_text_content import StreamingTextContent
+from semantic_kernel.contents.text_content import TextContent
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.agents.agent import Agent
+    from semantic_kernel.agents.chat_completion.chat_completion_agent import ChatHistoryAgentThread
+    from semantic_kernel.contents.chat_history import ChatHistory
+    from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+
+
+@experimental
+class ChatHistoryChannel(AgentChannel, ChatHistory):
+    """An AgentChannel specialization for that acts upon a ChatHistoryHandler."""
+
+    thread: "ChatHistoryAgentThread"
+
+    ALLOWED_CONTENT_TYPES: ClassVar[tuple[type, ...]] = (
+        ImageContent,
+        FunctionCallContent,
+        FunctionResultContent,
+        StreamingTextContent,
+        TextContent,
+    )
+
+    @override
+    async def invoke(
+        self,
+        agent: "Agent",
+        **kwargs: Any,
+    ) -> AsyncIterable[tuple[bool, ChatMessageContent]]:
+        """Perform a discrete incremental interaction between a single Agent and AgentChat.
+
+        Args:
+            agent: The agent to interact with.
+            kwargs: The keyword arguments.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        message_count = len(self.messages)
+        mutated_history = set()
+        message_queue: Deque[ChatMessageContent] = deque()
+
+        async for response in agent.invoke(
+            messages=self.messages[-1],
+            thread=self.thread,
+        ):
+            # Capture all messages that have been included in the mutated history.
+            for message_index in range(message_count, len(self.messages)):
+                mutated_message = self.messages[message_index]
+                mutated_history.add(mutated_message)
+                message_queue.append(mutated_message)
+
+            # Update the message count pointer to reflect the current history.
+            message_count = len(self.messages)
+
+            # Avoid duplicating any message included in the mutated history and also returned by the enumeration result.
+            if response.message not in mutated_history:
+                self.messages.append(response.message)
+                message_queue.append(response.message)
+
+            # Dequeue the next message to yield.
+            yield_message = message_queue.popleft()
+            yield (
+                self._is_message_visible(message=yield_message, message_queue_count=len(message_queue)),
+                yield_message,
+            )
+
+        # Dequeue any remaining messages to yield.
+        while message_queue:
+            yield_message = message_queue.popleft()
+            yield (
+                self._is_message_visible(message=yield_message, message_queue_count=len(message_queue)),
+                yield_message,
+            )
+
+    @override
+    async def invoke_stream(
+        self, agent: "Agent", messages: list[ChatMessageContent], **kwargs: Any
+    ) -> AsyncIterable["StreamingChatMessageContent"]:
+        """Perform a discrete incremental stream interaction between a single Agent and AgentChat.
+
+        Args:
+            agent: The agent to interact with.
+            messages: The history of messages in the conversation.
+            kwargs: The keyword arguments
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        message_count = len(self.messages)
+
+        async for response_message in agent.invoke_stream(
+            messages=self.messages[-1],
+            thread=self.thread,
+        ):
+            if response_message.message.content:
+                yield response_message.message
+
+        for message_index in range(message_count, len(self.messages)):
+            messages.append(self.messages[message_index])
+
+    def _is_message_visible(self, message: ChatMessageContent, message_queue_count: int) -> bool:
+        """Determine if a message is visible to the user."""
+        return (
+            not any(isinstance(item, (FunctionCallContent, FunctionResultContent)) for item in message.items)
+            or message_queue_count == 0
+        )
+
+    @override
+    async def receive(
+        self,
+        history: list[ChatMessageContent],
+    ) -> None:
+        """Receive the conversation messages.
+
+        Do not include messages that only contain file references.
+
+        Args:
+            history: The history of messages in the conversation.
+        """
+        filtered_history: list[ChatMessageContent] = []
+        for message in history:
+            new_message = deepcopy(message)
+            if new_message.items is None:
+                new_message.items = []
+            allowed_items = [item for item in new_message.items if isinstance(item, self.ALLOWED_CONTENT_TYPES)]
+            if not allowed_items:
+                continue
+            new_message.items.clear()
+            new_message.items.extend(allowed_items)
+            filtered_history.append(new_message)
+        self.messages.extend(filtered_history)
+
+    @override
+    async def get_history(  # type: ignore
+        self,
+    ) -> AsyncIterable[ChatMessageContent]:
+        """Retrieve the message history specific to this channel.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        for message in reversed(self.messages):
+            yield message
+
+    @override
+    async def reset(self) -> None:
+        """Reset the channel state."""
+        self.messages.clear()

python/semantic_kernel/agents/channels/open_ai_assistant_channel.py

@@ -0,0 +1,121 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import sys
+from collections.abc import AsyncIterable
+from typing import TYPE_CHECKING, Any
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from openai import AsyncOpenAI
+
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.agents.open_ai.assistant_content_generation import create_chat_message, generate_message_content
+from semantic_kernel.agents.open_ai.assistant_thread_actions import AssistantThreadActions
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.exceptions.agent_exceptions import AgentChatException
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.agents.agent import Agent
+
+
+@experimental
+class OpenAIAssistantChannel(AgentChannel):
+    """OpenAI Assistant Channel."""
+
+    def __init__(self, client: AsyncOpenAI, thread_id: str) -> None:
+        """Initialize the OpenAI Assistant Channel."""
+        self.client = client
+        self.thread_id = thread_id
+
+    @override
+    async def receive(self, history: list["ChatMessageContent"]) -> None:
+        """Receive the conversation messages.
+
+        Args:
+            history: The conversation messages.
+        """
+        for message in history:
+            if any(isinstance(item, FunctionCallContent) for item in message.items):
+                continue
+            await create_chat_message(self.client, self.thread_id, message)
+
+    @override
+    async def invoke(self, agent: "Agent", **kwargs: Any) -> AsyncIterable[tuple[bool, "ChatMessageContent"]]:
+        """Invoke the agent.
+
+        Args:
+            agent: The agent to invoke.
+            kwargs: The keyword arguments.
+
+        Yields:
+            tuple[bool, ChatMessageContent]: The conversation messages.
+        """
+        from semantic_kernel.agents.open_ai.openai_assistant_agent import OpenAIAssistantAgent
+
+        if not isinstance(agent, OpenAIAssistantAgent):
+            raise AgentChatException(f"Agent is not of the expected type {type(OpenAIAssistantAgent)}.")
+
+        async for is_visible, message in AssistantThreadActions.invoke(agent=agent, thread_id=self.thread_id, **kwargs):
+            yield is_visible, message
+
+    @override
+    async def invoke_stream(
+        self, agent: "Agent", messages: list[ChatMessageContent], **kwargs: Any
+    ) -> AsyncIterable["ChatMessageContent"]:
+        """Invoke the agent stream.
+
+        Args:
+            agent: The agent to invoke.
+            messages: The conversation messages.
+            kwargs: The keyword arguments.
+
+        Yields:
+            tuple[bool, StreamingChatMessageContent]: The conversation messages.
+        """
+        from semantic_kernel.agents.open_ai.openai_assistant_agent import OpenAIAssistantAgent
+
+        if not isinstance(agent, OpenAIAssistantAgent):
+            raise AgentChatException(f"Agent is not of the expected type {type(OpenAIAssistantAgent)}.")
+
+        async for message in AssistantThreadActions.invoke_stream(
+            agent=agent, thread_id=self.thread_id, output_messages=messages, **kwargs
+        ):
+            yield message
+
+    @override
+    async def get_history(self) -> AsyncIterable["ChatMessageContent"]:
+        """Get the conversation history.
+
+        Yields:
+            ChatMessageContent: The conversation history.
+        """
+        agent_names: dict[str, Any] = {}
+
+        thread_messages = await self.client.beta.threads.messages.list(
+            thread_id=self.thread_id, limit=100, order="desc"
+        )
+        for message in thread_messages.data:
+            assistant_name = None
+            if message.assistant_id and message.assistant_id not in agent_names:
+                agent = await self.client.beta.assistants.retrieve(message.assistant_id)
+                if agent.name:
+                    agent_names[message.assistant_id] = agent.name
+            assistant_name = agent_names.get(message.assistant_id) if message.assistant_id else message.assistant_id
+
+            content: ChatMessageContent = generate_message_content(str(assistant_name), message)
+
+            if len(content.items) > 0:
+                yield content
+
+    @override
+    async def reset(self) -> None:
+        """Reset the agent's thread."""
+        try:
+            await self.client.beta.threads.delete(thread_id=self.thread_id)
+        except Exception as e:
+            raise AgentChatException(f"Failed to delete thread: {e}")

python/semantic_kernel/agents/chat_completion/chat_completion_agent.py

@@ -0,0 +1,612 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+import uuid
+from collections.abc import AsyncGenerator, AsyncIterable, Awaitable, Callable
+from typing import TYPE_CHECKING, Any, ClassVar
+
+from pydantic import Field, model_validator
+
+from semantic_kernel.agents import Agent, AgentResponseItem, AgentThread, DeclarativeSpecMixin, register_agent_type
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.agents.channels.chat_history_channel import ChatHistoryChannel
+from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.function_result_content import FunctionResultContent
+from semantic_kernel.contents.history_reducer.chat_history_reducer import ChatHistoryReducer
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions import KernelServiceNotFoundError
+from semantic_kernel.exceptions.agent_exceptions import (
+    AgentInitializationException,
+    AgentInvokeException,
+    AgentThreadOperationException,
+)
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.functions.kernel_function import TEMPLATE_FORMAT_MAP
+from semantic_kernel.functions.kernel_plugin import KernelPlugin
+from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
+from semantic_kernel.utils.telemetry.agent_diagnostics.decorators import (
+    trace_agent_get_response,
+    trace_agent_invocation,
+    trace_agent_streaming_invocation,
+)
+
+if TYPE_CHECKING:
+    from semantic_kernel.kernel import Kernel
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+class ChatHistoryAgentThread(AgentThread):
+    """Chat History Agent Thread class."""
+
+    def __init__(self, chat_history: ChatHistory | None = None, thread_id: str | None = None) -> None:
+        """Initialize the ChatCompletionAgent Thread.
+
+        Args:
+            chat_history: The chat history for the thread. If None, a new ChatHistory instance will be created.
+            thread_id: The ID of the thread. If None, a new thread will be created.
+        """
+        super().__init__()
+
+        self._chat_history = chat_history if chat_history is not None else ChatHistory()
+        self._id: str = thread_id or f"thread_{uuid.uuid4().hex}"
+        self._is_deleted = False
+
+    def __len__(self) -> int:
+        """Returns the length of the chat history."""
+        return len(self._chat_history)
+
+    @override
+    async def _create(self) -> str:
+        """Starts the thread and returns its ID."""
+        return self._id
+
+    @override
+    async def _delete(self) -> None:
+        """Ends the current thread."""
+        self._chat_history.clear()
+
+    @override
+    async def _on_new_message(self, new_message: str | ChatMessageContent) -> None:
+        """Called when a new message has been contributed to the chat."""
+        if isinstance(new_message, str):
+            new_message = ChatMessageContent(role=AuthorRole.USER, content=new_message)
+
+        if (
+            not new_message.metadata
+            or "thread_id" not in new_message.metadata
+            or new_message.metadata["thread_id"] != self._id
+        ):
+            self._chat_history.add_message(new_message)
+
+    async def get_messages(self) -> AsyncIterable[ChatMessageContent]:
+        """Retrieve the current chat history.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        if self._is_deleted:
+            raise AgentThreadOperationException("Cannot retrieve chat history, since the thread has been deleted.")
+        if self._id is None:
+            await self.create()
+        for message in self._chat_history.messages:
+            yield message
+
+    async def reduce(self) -> ChatHistory | None:
+        """Reduce the chat history to a smaller size."""
+        if self._id is None:
+            raise AgentThreadOperationException("Cannot reduce chat history, since the thread is not currently active.")
+        if not isinstance(self._chat_history, ChatHistoryReducer):
+            return None
+        return await self._chat_history.reduce()
+
+
+@register_agent_type("chat_completion_agent")
+class ChatCompletionAgent(DeclarativeSpecMixin, Agent):
+    """A Chat Completion Agent based on ChatCompletionClientBase."""
+
+    function_choice_behavior: FunctionChoiceBehavior | None = Field(
+        default_factory=lambda: FunctionChoiceBehavior.Auto()
+    )
+    channel_type: ClassVar[type[AgentChannel] | None] = ChatHistoryChannel
+    service: ChatCompletionClientBase | None = Field(default=None, exclude=True)
+
+    def __init__(
+        self,
+        *,
+        arguments: KernelArguments | None = None,
+        description: str | None = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        id: str | None = None,
+        instructions: str | None = None,
+        kernel: "Kernel | None" = None,
+        name: str | None = None,
+        plugins: list[KernelPlugin | object] | dict[str, KernelPlugin | object] | None = None,
+        prompt_template_config: PromptTemplateConfig | None = None,
+        service: ChatCompletionClientBase | None = None,
+    ) -> None:
+        """Initialize a new instance of ChatCompletionAgent.
+
+        Args:
+            arguments: The kernel arguments for the agent. Invoke method arguments take precedence over
+                the arguments provided here.
+            description: The description of the agent.
+            function_choice_behavior: The function choice behavior to determine how and which plugins are
+                advertised to the model.
+            kernel: The kernel instance. If both a kernel and a service are provided, the service will take precedence
+                if they share the same service_id or ai_model_id. Otherwise if separate, the first AI service
+                registered on the kernel will be used.
+            id: The unique identifier for the agent. If not provided,
+                a unique GUID will be generated.
+            instructions: The instructions for the agent.
+            name: The name of the agent.
+            plugins: The plugins for the agent. If plugins are included along with a kernel, any plugins
+                that already exist in the kernel will be overwritten.
+            prompt_template_config: The prompt template configuration for the agent.
+            service: The chat completion service instance. If a kernel is provided with the same service_id or
+                `ai_model_id`, the service will take precedence.
+        """
+        args: dict[str, Any] = {
+            "description": description,
+        }
+        if name is not None:
+            args["name"] = name
+        if id is not None:
+            args["id"] = id
+        if kernel is not None:
+            args["kernel"] = kernel
+        if arguments is not None:
+            args["arguments"] = arguments
+
+        if instructions and prompt_template_config and instructions != prompt_template_config.template:
+            logger.info(
+                f"Both `instructions` ({instructions}) and `prompt_template_config` "
+                f"({prompt_template_config.template}) were provided. Using template in `prompt_template_config` "
+                "and ignoring `instructions`."
+            )
+
+        if plugins is not None:
+            args["plugins"] = plugins
+
+        if function_choice_behavior is not None:
+            args["function_choice_behavior"] = function_choice_behavior
+
+        if service is not None:
+            args["service"] = service
+
+        if instructions is not None:
+            args["instructions"] = instructions
+        if prompt_template_config is not None:
+            args["prompt_template"] = TEMPLATE_FORMAT_MAP[prompt_template_config.template_format](
+                prompt_template_config=prompt_template_config
+            )
+            if prompt_template_config.template is not None:
+                # Use the template from the prompt_template_config if it is provided
+                args["instructions"] = prompt_template_config.template
+        super().__init__(**args)
+
+    @model_validator(mode="after")
+    def configure_service(self) -> "ChatCompletionAgent":
+        """Configure the service used by the ChatCompletionAgent."""
+        if self.service is None:
+            return self
+        if not isinstance(self.service, ChatCompletionClientBase):
+            raise AgentInitializationException(
+                f"Service provided for ChatCompletionAgent is not an instance of ChatCompletionClientBase. "
+                f"Service: {type(self.service)}"
+            )
+        self.kernel.add_service(self.service, overwrite=True)
+        return self
+
+    async def create_channel(
+        self, chat_history: ChatHistory | None = None, thread_id: str | None = None
+    ) -> AgentChannel:
+        """Create a ChatHistoryChannel.
+
+        Args:
+            chat_history: The chat history for the channel. If None, a new ChatHistory instance will be created.
+            thread_id: The ID of the thread. If None, a new thread will be created.
+
+        Returns:
+            An instance of AgentChannel.
+        """
+        from semantic_kernel.agents.chat_completion.chat_completion_agent import ChatHistoryAgentThread
+
+        ChatHistoryChannel.model_rebuild()
+
+        thread = ChatHistoryAgentThread(chat_history=chat_history, thread_id=thread_id)
+
+        if thread.id is None:
+            await thread.create()
+
+        messages = [message async for message in thread.get_messages()]
+
+        return ChatHistoryChannel(messages=messages, thread=thread)
+
+    # region Declarative Spec
+
+    @override
+    @classmethod
+    async def _from_dict(
+        cls,
+        data: dict,
+        *,
+        kernel: "Kernel | None" = None,
+        plugins: list[KernelPlugin | object] | dict[str, KernelPlugin | object] | None = None,
+        **kwargs,
+    ) -> "ChatCompletionAgent":
+        # Returns the normalized spec fields and a kernel configured with plugins, if present.
+        fields, kernel = cls._normalize_spec_fields(data, kernel=kernel, plugins=plugins, **kwargs)
+
+        if "service" in kwargs:
+            fields["service"] = kwargs["service"]
+
+        if "function_choice_behavior" in kwargs:
+            fields["function_choice_behavior"] = kwargs["function_choice_behavior"]
+
+        # Handle arguments from kwargs, merging with any arguments from _normalize_spec_fields
+        if "arguments" in kwargs and kwargs["arguments"] is not None:
+            incoming_args = kwargs["arguments"]
+            if fields.get("arguments") is not None:
+                # Use KernelArguments' built-in merge operator, with incoming_args taking precedence
+                fields["arguments"] = fields["arguments"] | incoming_args
+            else:
+                fields["arguments"] = incoming_args
+
+        return cls(**fields, kernel=kernel)
+
+    # endregion
+
+    # region Invocation Methods
+
+    @trace_agent_get_response
+    @override
+    async def get_response(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs: Any,
+    ) -> AgentResponseItem[ChatMessageContent]:
+        """Get a response from the agent.
+
+        Args:
+            messages: The input chat message content either as a string, ChatMessageContent or
+                a list of strings or ChatMessageContent.
+            thread: The thread to use for agent invocation.
+            arguments: The kernel arguments.
+            kernel: The kernel instance.
+            kwargs: The keyword arguments.
+
+        Returns:
+            An AgentResponseItem of type ChatMessageContent.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: ChatHistoryAgentThread(),
+            expected_type=ChatHistoryAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        chat_history = ChatHistory()
+        async for message in thread.get_messages():
+            chat_history.add_message(message)
+
+        responses: list[ChatMessageContent] = []
+        async for response in self._inner_invoke(
+            thread,
+            chat_history,
+            None,
+            arguments,
+            kernel,
+            **kwargs,
+        ):
+            responses.append(response)
+
+        if not responses:
+            raise AgentInvokeException("No response from agent.")
+
+        return AgentResponseItem(message=responses[-1], thread=thread)
+
+    @trace_agent_invocation
+    @override
+    async def invoke(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentResponseItem[ChatMessageContent]]:
+        """Invoke the chat history handler.
+
+        Args:
+            messages: The input chat message content either as a string, ChatMessageContent or
+                a list of strings or ChatMessageContent.
+            thread: The thread to use for agent invocation.
+            on_intermediate_message: A callback function to handle intermediate steps of the agent's execution.
+            arguments: The kernel arguments.
+            kernel: The kernel instance.
+            kwargs: The keyword arguments.
+
+        Returns:
+            An async iterable of AgentResponseItem of type ChatMessageContent.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: ChatHistoryAgentThread(),
+            expected_type=ChatHistoryAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        chat_history = ChatHistory()
+        async for message in thread.get_messages():
+            chat_history.add_message(message)
+
+        async for response in self._inner_invoke(
+            thread,
+            chat_history,
+            on_intermediate_message,
+            arguments,
+            kernel,
+            **kwargs,
+        ):
+            yield AgentResponseItem(message=response, thread=thread)
+
+    @trace_agent_streaming_invocation
+    @override
+    async def invoke_stream(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentResponseItem[StreamingChatMessageContent]]:
+        """Invoke the chat history handler in streaming mode.
+
+        Args:
+            messages: The chat message content either as a string, ChatMessageContent or
+                a list of str or ChatMessageContent.
+            thread: The thread to use for agent invocation.
+            on_intermediate_message: A callback function to handle intermediate steps of the
+                                     agent's execution as fully formed messages.
+            arguments: The kernel arguments.
+            kernel: The kernel instance.
+            kwargs: The keyword arguments.
+
+        Returns:
+            An async generator of AgentResponseItem of type StreamingChatMessageContent.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: ChatHistoryAgentThread(),
+            expected_type=ChatHistoryAgentThread,
+        )
+        assert thread.id is not None  # nosec
+
+        chat_history = ChatHistory()
+        async for message in thread.get_messages():
+            chat_history.add_message(message)
+
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        chat_completion_service, settings = await self._get_chat_completion_service_and_settings(
+            kernel=kernel, arguments=arguments
+        )
+
+        # If the user hasn't provided a function choice behavior, use the agent's default.
+        if settings.function_choice_behavior is None:
+            settings.function_choice_behavior = self.function_choice_behavior
+
+        agent_chat_history = await self._prepare_agent_chat_history(
+            history=chat_history,
+            kernel=kernel,
+            arguments=arguments,
+        )
+
+        message_count_before_completion = len(agent_chat_history)
+
+        logger.debug(f"[{type(self).__name__}] Invoking {type(chat_completion_service).__name__}.")
+
+        responses: AsyncGenerator[list[StreamingChatMessageContent], Any] = (
+            chat_completion_service.get_streaming_chat_message_contents(
+                chat_history=agent_chat_history,
+                settings=settings,
+                kernel=kernel,
+                arguments=arguments,
+            )
+        )
+
+        logger.debug(
+            f"[{type(self).__name__}] Invoked {type(chat_completion_service).__name__} "
+            f"with message count: {message_count_before_completion}."
+        )
+
+        role = None
+        response_builder: list[str] = []
+        start_idx = len(agent_chat_history)
+
+        async for response_list in responses:
+            for response in response_list:
+                role = response.role
+                response.name = self.name
+                response_builder.append(response.content)
+
+                if (
+                    role == AuthorRole.ASSISTANT
+                    and (response.items or response.metadata.get("usage"))
+                    and not any(
+                        isinstance(item, (FunctionCallContent, FunctionResultContent)) for item in response.items
+                    )
+                ):
+                    yield AgentResponseItem(message=response, thread=thread)
+
+            # Drain newly added tool messages since last index to maintain
+            # correct order and avoid duplicates
+            new_messages = await self._drain_mutated_messages(
+                agent_chat_history,
+                start_idx,
+                thread,
+            )
+            # resets start_idx to the latest length of agent_chat_history.
+            start_idx = len(agent_chat_history)
+
+            if on_intermediate_message:
+                for message in new_messages:
+                    await on_intermediate_message(message)
+
+        if role != AuthorRole.TOOL:
+            # Tool messages will be automatically added to the chat history by the auto function invocation loop
+            # if it's the response (i.e. terminated by a filter), thus we need to avoid notifying the thread about
+            # them multiple times.
+            await thread.on_new_message(
+                ChatMessageContent(
+                    role=role if role else AuthorRole.ASSISTANT, content="".join(response_builder), name=self.name
+                )
+            )
+
+    # endregion
+
+    # region Helper Methods
+
+    async def _inner_invoke(
+        self,
+        thread: ChatHistoryAgentThread,
+        history: ChatHistory,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[ChatMessageContent]:
+        """Helper method to invoke the agent with a chat history in non-streaming mode."""
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        chat_completion_service, settings = await self._get_chat_completion_service_and_settings(
+            kernel=kernel, arguments=arguments
+        )
+
+        # If the user hasn't provided a function choice behavior, use the agent's default.
+        if settings.function_choice_behavior is None:
+            settings.function_choice_behavior = self.function_choice_behavior
+
+        agent_chat_history = await self._prepare_agent_chat_history(
+            history=history,
+            kernel=kernel,
+            arguments=arguments,
+        )
+        start_idx = len(agent_chat_history)
+
+        message_count_before_completion = len(agent_chat_history)
+
+        logger.debug(f"[{type(self).__name__}] Invoking {type(chat_completion_service).__name__}.")
+
+        responses = await chat_completion_service.get_chat_message_contents(
+            chat_history=agent_chat_history,
+            settings=settings,
+            kernel=kernel,
+            arguments=arguments,
+        )
+
+        logger.debug(
+            f"[{type(self).__name__}] Invoked {type(chat_completion_service).__name__} "
+            f"with message count: {message_count_before_completion}."
+        )
+
+        # Drain newly added tool messages since last index to maintain
+        # correct order and avoid duplicates
+        new_msgs = await self._drain_mutated_messages(
+            agent_chat_history,
+            start_idx,
+            thread,
+        )
+
+        if on_intermediate_message:
+            for msg in new_msgs:
+                await on_intermediate_message(msg)
+
+        for response in responses:
+            response.name = self.name
+            if response.role != AuthorRole.TOOL:
+                # Tool messages will be automatically added to the chat history by the auto function invocation loop
+                # if it's the response (i.e. terminated by a filter),, thus we need to avoid notifying the thread about
+                # them multiple times.
+                await thread.on_new_message(response)
+            yield response
+
+    async def _prepare_agent_chat_history(
+        self, history: ChatHistory, kernel: "Kernel", arguments: KernelArguments
+    ) -> ChatHistory:
+        """Prepare the agent chat history from the input history by adding the formatted instructions."""
+        formatted_instructions = await self.format_instructions(kernel, arguments)
+        messages = []
+        if formatted_instructions:
+            messages.append(ChatMessageContent(role=AuthorRole.SYSTEM, content=formatted_instructions, name=self.name))
+        if history.messages:
+            messages.extend(history.messages)
+
+        return ChatHistory(messages=messages)
+
+    async def _get_chat_completion_service_and_settings(
+        self, kernel: "Kernel", arguments: KernelArguments
+    ) -> tuple[ChatCompletionClientBase, PromptExecutionSettings]:
+        """Get the chat completion service and settings."""
+        chat_completion_service, settings = kernel.select_ai_service(arguments=arguments, type=ChatCompletionClientBase)
+
+        if not chat_completion_service:
+            raise KernelServiceNotFoundError(
+                "Chat completion service not found. Check your service or kernel configuration."
+            )
+
+        assert isinstance(chat_completion_service, ChatCompletionClientBase)  # nosec
+        assert settings is not None  # nosec
+
+        return chat_completion_service, settings
+
+    async def _drain_mutated_messages(
+        self,
+        history: ChatHistory,
+        start: int,
+        thread: ChatHistoryAgentThread,
+    ) -> list[ChatMessageContent]:
+        """Return messages appended to history after start and push them to thread."""
+        drained: list[ChatMessageContent] = []
+        for i in range(start, len(history)):
+            msg: ChatMessageContent = history[i]  # type: ignore
+            msg.name = self.name
+            await thread.on_new_message(msg)
+            drained.append(msg)
+        return drained

python/semantic_kernel/agents/copilot_studio/copilot_studio_agent.py

@@ -0,0 +1,660 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+from collections.abc import AsyncIterable, Awaitable, Callable, Sequence
+from os import environ, path
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, ClassVar, Literal
+
+from microsoft_agents.activity import ActivityTypes
+from microsoft_agents.copilotstudio.client import AgentType, CopilotClient, PowerPlatformCloud
+from msal import ConfidentialClientApplication, PublicClientApplication
+from msal_extensions import FilePersistence, PersistedTokenCache, build_encrypted_persistence
+from pydantic import ValidationError
+
+from semantic_kernel.agents import Agent
+from semantic_kernel.agents.agent import AgentResponseItem, AgentThread
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.agents.copilot_studio.copilot_studio_agent_settings import (
+    CopilotStudioAgentAuthMode,
+    CopilotStudioAgentSettings,
+)
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import (
+    AgentInitializationException,
+    AgentThreadInitializationException,
+    AgentThreadOperationException,
+)
+from semantic_kernel.functions import KernelArguments
+from semantic_kernel.functions.kernel_function import TEMPLATE_FORMAT_MAP
+from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
+from semantic_kernel.utils.feature_stage_decorator import experimental
+from semantic_kernel.utils.naming import generate_random_ascii_name
+from semantic_kernel.utils.telemetry.agent_diagnostics.decorators import (
+    trace_agent_get_response,
+    trace_agent_invocation,
+    trace_agent_streaming_invocation,
+)
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:  # pragma: no cover
+    from typing_extensions import override
+
+if TYPE_CHECKING:  # pragma: no cover
+    from semantic_kernel.kernel import Kernel
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+# region Token Factory
+
+
+@experimental
+def _log_auth_failure(result: dict[str, Any]) -> None:
+    category = result.get("error", "unknown_error")
+    corr_id = result.get("correlation_id", "n/a")[:8]  # Only log the first 8 characters
+    logger.error(f"Copilot auth failure, category={category}, corr_id={corr_id}")
+
+
+@experimental
+class _CopilotStudioAgentTokenFactory:
+    """A CopilotStudioAgentTokenFactory to handle authentication for the Copilot Studio agent."""
+
+    def __init__(
+        self,
+        *,
+        settings: CopilotStudioAgentSettings,
+        cache_path: str,
+        mode: CopilotStudioAgentAuthMode,
+        client_secret: str | None = None,
+        client_certificate: Path | None = None,
+        user_assertion: str | None = None,
+        scopes: Sequence[str] | None = None,
+    ) -> None:
+        self.settings = settings
+        self.cache = self._get_msal_token_cache(cache_path)
+        self.mode = mode
+        self.client_secret = client_secret
+        self.client_cert_path = client_certificate
+        self.user_assertion = user_assertion
+        self.scopes = scopes or ["https://api.powerplatform.com/.default"]
+
+    @staticmethod
+    def _get_msal_token_cache(cache_path: str, fallback_to_plaintext=True) -> PersistedTokenCache:
+        """Get the MSAL token cache."""
+        persistence = None
+
+        # Note: This stores both encrypted persistence and plaintext persistence
+        # into same location, therefore their data would likely override with each other.
+        try:
+            persistence = build_encrypted_persistence(cache_path)
+        except Exception:  # pylint: disable=bare-except
+            # On Linux, encryption exception will be raised during initialization.
+            # On Windows and macOS, they won't be detected here,
+            # but will be raised during their load() or save().
+            if not fallback_to_plaintext:
+                raise
+            logging.warning("Encryption unavailable. Opting in to plain text.")
+            persistence = FilePersistence(cache_path)
+
+        return PersistedTokenCache(persistence)
+
+    def acquire(self) -> str:
+        """Return a valid bearer token or raise AgentInitializationException."""
+        if self.mode is CopilotStudioAgentAuthMode.SERVICE:
+            # SERVICE auth wiring is present but not yet supported end-to-end.
+            logger.warning("SERVICE authentication mode is not yet supported; falling back to error.")
+            raise AgentInitializationException(
+                "Copilot Studio SERVICE authentication is not available yet. Please use INTERACTIVE mode instead."
+            )
+
+        match self.mode:
+            case CopilotStudioAgentAuthMode.SERVICE:
+                return self._acquire_service_token()  # unreachable until the guard is removed
+            case _:
+                return self._acquire_interactive_token()
+
+    def _new_confidential_client(self, **extra_kwargs) -> ConfidentialClientApplication:
+        return ConfidentialClientApplication(
+            client_id=self.settings.app_client_id,
+            authority=f"https://login.microsoftonline.com/{self.settings.tenant_id}",
+            token_cache=self.cache,
+            **extra_kwargs,
+        )
+
+    def _acquire_service_token(self) -> str:
+        if not self.client_secret and not self.client_cert_path:
+            raise AgentInitializationException(
+                "client_secret *or* client_certificate is required for service-to-service auth."
+            )
+
+        kwargs: dict[str, Any] = {}
+        if self.client_secret:
+            kwargs["client_credential"] = self.client_secret
+        else:  # certificate
+            if not self.client_cert_path:
+                raise AgentInitializationException(
+                    "If no client_secret is provided, a client_certificate is required for service-to-service auth."
+                )
+            kwargs["client_credential"] = {
+                "private_key": Path(self.client_cert_path).read_text(),
+                "thumbprint": self._cert_thumbprint(self.client_cert_path),
+            }
+
+        app = self._new_confidential_client(**kwargs)
+
+        # proactive caching
+        result = app.acquire_token_silent(self.scopes, account=None) or app.acquire_token_for_client(scopes=self.scopes)
+
+        return self._unwrap(result)
+
+    # interactive
+    def _acquire_interactive_token(self) -> str:
+        app = PublicClientApplication(
+            self.settings.app_client_id,
+            authority=f"https://login.microsoftonline.com/{self.settings.tenant_id}",
+            token_cache=self.cache,
+        )
+        accounts = app.get_accounts()
+        result = (
+            app.acquire_token_silent(self.scopes, account=accounts[0])
+            if accounts
+            else app.acquire_token_interactive(self.scopes)
+        )
+        return self._unwrap(result)
+
+    @staticmethod
+    def _unwrap(result: dict[str, Any]) -> str:
+        if "access_token" in result:
+            return result["access_token"]
+        _log_auth_failure(result)
+        raise AgentInitializationException("Authentication failed; see logs for category and correlation code.")
+
+    @staticmethod
+    def _cert_thumbprint(cert_path: Path) -> str:
+        import hashlib
+        import ssl
+
+        pem_bytes = Path(cert_path).read_bytes()
+        der_bytes = ssl.PEM_cert_to_DER_cert(pem_bytes.decode())
+        return hashlib.sha1(der_bytes, usedforsecurity=False).hexdigest().upper()
+
+
+# endregion
+
+
+# region CopilotStudioAgentThread
+
+
+@experimental
+class CopilotStudioAgentThread(AgentThread):
+    """The Copilot Studio Agent Thread."""
+
+    def __init__(
+        self,
+        client: CopilotClient,
+        conversation_id: str | None = None,
+    ) -> None:
+        """Initializes a new instance of the CopilotStudioAgentThread class.
+
+        Args:
+            client: The Copilot Client.
+            conversation_id: The conversation ID. This is the Copilot Studio conversation ID.
+        """
+        super().__init__()
+        if client is None:
+            raise AgentThreadInitializationException("CopilotClient cannot be None")
+
+        self._client = client
+        self._conversation_id = conversation_id  # Copilot Studio conversation ID
+
+    @property
+    def conversation_id(self) -> str | None:
+        """Get the conversation ID."""
+        return self._conversation_id
+
+    @conversation_id.setter
+    def conversation_id(self, value: str | None) -> None:
+        """Set the conversation ID."""
+        self._conversation_id = value
+
+    @override
+    @property
+    def id(self) -> str | None:
+        """Get the thread ID."""
+        return self.conversation_id
+
+    @override
+    async def _create(self) -> str:
+        # Creation is deferred to CopilotStudioAgent._ensure_conversation.
+        if self._is_deleted:
+            raise AgentThreadOperationException("Cannot create a thread that has been deleted.")
+        return ""
+
+    @override
+    async def _delete(self) -> None:
+        if self._is_deleted:
+            return
+        if self.conversation_id is None:
+            raise AgentThreadOperationException("Cannot delete the thread, since it has not been created.")
+        self._conversation_id = None
+        self._is_deleted = True
+
+    @override
+    async def _on_new_message(self, new_message: ChatMessageContent) -> None:
+        raise NotImplementedError(
+            "This method is not implemented for CopilotStudioAgent. "
+            "Messages and responses are automatically handled by the Copilot Agent."
+        )
+
+
+@experimental
+class CopilotStudioAgent(Agent):
+    """Semantic Kernel abstraction over a Copilot Studio Agent."""
+
+    client: CopilotClient
+    channel_type: ClassVar[type[AgentChannel] | None] = None
+
+    def __init__(
+        self,
+        *,
+        client: CopilotClient | None = None,
+        arguments: KernelArguments | None = None,
+        description: str | None = None,
+        id: str | None = None,
+        instructions: str | None = None,
+        kernel: "Kernel | None" = None,
+        name: str | None = None,
+        prompt_template_config: PromptTemplateConfig | None = None,
+    ) -> None:
+        """Initializes a new instance of the CopilotStudioAgent class.
+
+        Args:
+            client: The Copilot Client
+            arguments: The Kernel Arguments to use at the agent-level.
+            description: The description of the agent.
+            id: The unique identifier for the agent. If not provided,
+                a unique GUID will be generated.
+            instructions: The instructions for the agent.
+            kernel: The kernel instance.
+            name: The name of the agent.
+            prompt_template_config: The prompt template configuration for the agent.
+        """
+        if client is None:
+            client = self.create_client()
+
+        args: dict[str, Any] = {
+            "client": client,
+            "name": name or f"copilot_agent_{generate_random_ascii_name(6)}",
+            "description": description,
+        }
+        if id is not None:
+            args["id"] = id
+        if instructions is not None:
+            args["instructions"] = instructions
+        if kernel is not None:
+            args["kernel"] = kernel
+        if arguments is not None:
+            args["arguments"] = arguments
+
+        if instructions and prompt_template_config and instructions != prompt_template_config.template:
+            logger.info(
+                "Both `instructions` and `prompt_template_config` supplied   "
+                "using the template inside `prompt_template_config`."
+            )
+
+        if prompt_template_config:
+            args["prompt_template"] = TEMPLATE_FORMAT_MAP[prompt_template_config.template_format](
+                prompt_template_config=prompt_template_config
+            )
+            # Overrides raw instructions if template contains the final string
+            if prompt_template_config.template:
+                args["instructions"] = prompt_template_config.template
+
+        super().__init__(**args)
+
+    def model_post_init(self, __ctx: Any) -> None:
+        """Post-initialization hook for the model."""
+        super().model_post_init(__ctx)
+        if self.kernel.plugins:
+            logger.warning("Plugins are not supported by CopilotStudioAgent; any kernel plugins will be ignored.")
+
+    @staticmethod
+    def create_client(
+        *,
+        auth_mode: CopilotStudioAgentAuthMode | Literal["interactive", "service"] | None = None,
+        agent_identifier: str | None = None,
+        app_client_id: str | None = None,
+        client_secret: str | None = None,
+        client_certificate: str | None = None,
+        cloud: PowerPlatformCloud | None = None,
+        copilot_agent_type: AgentType | None = None,
+        custom_power_platform_cloud: str | None = None,
+        env_file_encoding: str | None = None,
+        env_file_path: str | None = None,
+        environment_id: str | None = None,
+        tenant_id: str | None = None,
+        user_assertion: str | None = None,
+    ) -> CopilotClient:
+        """Create the Copilot Studio Agent Client.
+
+        Args:
+            auth_mode: The authentication mode. This can be either `interactive` or `service`.
+            agent_identifier: The agent identifier. This is the `Schema Name` of the agent from the
+                Copilot Studio Advanced Metadata settings.
+            app_client_id: The app client ID. This is the app ID of the app registration configured in Entra.
+            client_secret: The client secret. This is the secret of the app registration.
+            client_certificate: The client certificate. This is the certificate of the app registration.
+            cloud: The cloud environment.
+            copilot_agent_type: The type of Copilot agent.
+            custom_power_platform_cloud: The custom Power Platform cloud.
+            env_file_path: The path to the environment file.
+            env_file_encoding: The encoding of the environment file.
+            environment_id: The environment ID. This is from the Copilot Studio Advanced Metadata settings.
+            tenant_id: The tenant ID. This is the tenant ID related to the app registration.
+            user_assertion: The user assertion. This is the token used for on-behalf-of authentication.
+
+        Returns:
+            CopilotClient: The Copilot client.
+        """
+        if auth_mode is not None and isinstance(auth_mode, str):
+            auth_mode = CopilotStudioAgentAuthMode(auth_mode)
+
+        try:
+            connection_settings = CopilotStudioAgentSettings(
+                app_client_id=app_client_id,
+                tenant_id=tenant_id,
+                environment_id=environment_id,
+                agent_identifier=agent_identifier,
+                cloud=cloud,
+                type=copilot_agent_type,
+                custom_power_platform_cloud=custom_power_platform_cloud,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+                client_secret=client_secret,
+                client_certificate=client_certificate,
+                user_assertion=user_assertion,
+                auth_mode=auth_mode,
+            )
+        except ValidationError as exc:
+            raise AgentInitializationException(f"Failed to create Copilot Studio Agent settings: {exc}") from exc
+
+        missing_params = [name for name in ("app_client_id", "tenant_id") if not getattr(connection_settings, name)]
+        if missing_params:
+            raise AgentInitializationException(f"Missing required configuration field(s): {', '.join(missing_params)}")
+
+        cache_file = environ.get("TOKEN_CACHE_PATH_INTERACTIVE") or path.join(
+            path.dirname(__file__), "bin", "token_cache_interactive.bin"
+        )
+
+        token = _CopilotStudioAgentTokenFactory(
+            settings=connection_settings,
+            cache_path=cache_file,
+            mode=connection_settings.auth_mode,
+            client_secret=connection_settings.client_secret.get_secret_value()
+            if connection_settings.client_secret
+            else None,
+            client_certificate=Path(client_certificate) if client_certificate else None,
+            user_assertion=user_assertion,
+        ).acquire()
+
+        return CopilotClient(connection_settings, token)
+
+    # region Invocation Methods
+
+    @trace_agent_get_response
+    @override
+    async def get_response(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs,
+    ) -> AgentResponseItem[ChatMessageContent]:
+        """Get a response from the agent.
+
+        Args:
+            messages: The messages to send to the agent.
+            thread: The thread to use for the agent.
+            arguments: The arguments to pass to the agent. These take precedence over the agent-defined args.
+            kernel: The kernel to use for the agent. This kernel takes precedence over the agent-defined kernel.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            A chat message content and thread with the response.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: CopilotStudioAgentThread(self.client),
+            expected_type=CopilotStudioAgentThread,
+        )
+        if not isinstance(thread, CopilotStudioAgentThread):
+            raise AgentThreadOperationException("The thread is not a Copilot Studio Agent thread.")
+
+        normalized_messages = self._normalize_messages(messages)
+
+        responses: list[ChatMessageContent] = []
+        async for response in self._inner_invoke(
+            thread=thread,
+            messages=normalized_messages,
+            on_intermediate_message=None,
+            arguments=arguments,
+            kernel=kernel,
+            **kwargs,
+        ):
+            responses.append(response)
+
+        return AgentResponseItem(message=responses[-1], thread=thread)
+
+    @trace_agent_invocation
+    @override
+    async def invoke(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs,
+    ) -> AsyncIterable[AgentResponseItem[ChatMessageContent]]:
+        """Invoke the agent.
+
+        Args:
+            messages: The messages to send to the agent.
+            thread: The thread to use for the agent.
+            on_intermediate_message: A callback function to call with each intermediate message.
+            arguments: The arguments to pass to the agent.
+            kernel: The kernel to use for the agent.
+            **kwargs: Additional keyword arguments.
+
+        Yields:
+            A chat message content and thread with the response.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: CopilotStudioAgentThread(self.client),
+            expected_type=CopilotStudioAgentThread,
+        )
+        if not isinstance(thread, CopilotStudioAgentThread):
+            raise AgentThreadOperationException("The thread is not a Copilot Studio Agent thread.")
+
+        normalized_messages = self._normalize_messages(messages)
+
+        async for response in self._inner_invoke(
+            thread=thread,
+            messages=normalized_messages,
+            on_intermediate_message=on_intermediate_message,
+            arguments=arguments,
+            kernel=kernel,
+            **kwargs,
+        ):
+            yield AgentResponseItem(message=response, thread=thread)
+
+    @trace_agent_streaming_invocation
+    @override
+    async def invoke_stream(
+        self,
+        messages: str | ChatMessageContent | list[str | ChatMessageContent] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentResponseItem[StreamingChatMessageContent]]:
+        """Invoke the agent and stream the response.
+
+        Note: this is a “pseudo-streaming” implementation.
+
+        We're internally delegating to the real async generator `_inner_invoke`.
+        Each complete ChatMessageContent is wrapped in exactly one
+        StreamingChatMessageContent chunk, so downstream consumers can iterate
+        without change.  The stream yields at least once; callers still receive
+        on_intermediate callbacks in real time.
+
+        Args:
+            messages: The messages to send to the agent.
+            thread: The thread to use for the agent.
+            on_intermediate_message: A callback function to call with each intermediate message.
+            arguments: The arguments to pass to the agent.
+            kernel: The kernel to use for the agent.
+            **kwargs: Additional keyword arguments.
+
+        Yields:
+            A chat message content and thread with the response.
+        """
+        thread = await self._ensure_thread_exists_with_messages(
+            messages=messages,
+            thread=thread,
+            construct_thread=lambda: CopilotStudioAgentThread(self.client),
+            expected_type=CopilotStudioAgentThread,
+        )
+        if not isinstance(thread, CopilotStudioAgentThread):
+            raise AgentThreadOperationException("The thread is not a Copilot Studio Agent thread.")
+
+        normalized_messages = self._normalize_messages(messages)
+
+        responses: list[ChatMessageContent] = []
+        async for resp in self._inner_invoke(
+            thread=thread,
+            messages=normalized_messages,
+            on_intermediate_message=on_intermediate_message,
+            arguments=arguments,
+            kernel=kernel,
+            **kwargs,
+        ):
+            responses.append(resp)
+
+        for i, resp in enumerate(responses):
+            stream_msg = self._to_streaming(resp, index=i)
+            yield AgentResponseItem(message=stream_msg, thread=thread)
+
+    # endregion
+
+    # region Helper Methods
+
+    async def _inner_invoke(
+        self,
+        thread: CopilotStudioAgentThread,
+        messages: list[str] | None = None,
+        on_intermediate_message: Callable[[ChatMessageContent], Awaitable[None]] | None = None,
+        arguments: KernelArguments | None = None,
+        kernel: "Kernel | None" = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[ChatMessageContent]:
+        if arguments is None:
+            arguments = KernelArguments(**kwargs)
+        else:
+            arguments.update(kwargs)
+
+        kernel = kernel or self.kernel
+        arguments = self._merge_arguments(arguments)
+
+        prompt_parts: list[str] = []
+
+        formatted_instructions = await self.format_instructions(kernel, arguments)
+        if formatted_instructions:
+            prompt_parts.append(formatted_instructions)
+
+        if messages:
+            prompt_parts.extend(messages)
+
+        final_prompt: str = "\n".join(prompt_parts).strip()
+
+        await self._ensure_conversation(thread)
+
+        async for activity in self.client.ask_question(question=final_prompt, conversation_id=thread.id):
+            if activity.type == ActivityTypes.message:
+                if (
+                    activity.text_format == "markdown"
+                    and activity.suggested_actions
+                    and activity.suggested_actions.actions
+                ):
+                    for action in activity.suggested_actions.actions:
+                        if on_intermediate_message:
+                            await on_intermediate_message(
+                                ChatMessageContent(role=AuthorRole.ASSISTANT, name=self.name, content=action.text)
+                            )
+                yield ChatMessageContent(role=AuthorRole.ASSISTANT, name=self.name, content=activity.text)
+
+    async def _ensure_conversation(self, thread: CopilotStudioAgentThread) -> None:
+        """Guarantee that `thread.conversation_id` is populated."""
+        if thread.id:
+            return
+
+        async for act in self.client.start_conversation():
+            conversation_id = getattr(getattr(act, "conversation", None), "id", None)
+            if conversation_id:
+                thread.conversation_id = conversation_id
+                return
+
+        # If we reach this point, the service misbehaved, so throw
+        raise AgentThreadOperationException("Copilot Studio did not return a conversation ID.")
+
+    @staticmethod
+    def _normalize_messages(messages: str | ChatMessageContent | list[str | ChatMessageContent] | None) -> list[str]:
+        """Return a flat list[str] irrespective of the caller-supplied type."""
+        if messages is None:
+            return []
+
+        if isinstance(messages, (str, ChatMessageContent)):
+            messages = [messages]
+
+        normalized: list[str] = []
+        for m in messages:
+            normalized.append(m.content if isinstance(m, ChatMessageContent) else str(m))
+        return normalized
+
+    @staticmethod
+    def _to_streaming(
+        msg: ChatMessageContent,
+        *,
+        index: int,
+    ) -> StreamingChatMessageContent:
+        """Wrap a complete ChatMessageContent in a StreamingChatMessageContent."""
+        return StreamingChatMessageContent(
+            role=msg.role,
+            name=msg.name,
+            content=msg.content,
+            choice_index=index,
+            metadata=msg.metadata,
+        )
+
+    @override
+    async def _notify_thread_of_new_message(self, thread, new_message):
+        """Copilot Studio Agent doesn't need to notify the thread of new messages.
+
+        The new message is passed to the agent when invoking the agent.
+        """
+        pass
+
+    # endregion

python/semantic_kernel/agents/copilot_studio/copilot_studio_agent_settings.py

@@ -0,0 +1,40 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from enum import Enum
+from typing import ClassVar
+
+from microsoft_agents.copilotstudio.client import (
+    AgentType,
+    PowerPlatformCloud,
+)
+from pydantic import Field, SecretStr
+
+from semantic_kernel.kernel_pydantic import KernelBaseSettings
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class CopilotStudioAgentAuthMode(str, Enum):
+    """The Copilot Studio agent authentication mode."""
+
+    INTERACTIVE = "interactive"  # user authentication
+    SERVICE = "service"  # client-credentials (app secret/cert)
+
+
+@experimental
+class CopilotStudioAgentSettings(KernelBaseSettings):
+    """Copilot Studio Agent settings currently used by the CopilotStudioAgent."""
+
+    env_prefix: ClassVar[str] = "COPILOT_STUDIO_AGENT_"
+
+    app_client_id: str | None = None
+    tenant_id: str | None = None
+    environment_id: str | None = None
+    agent_identifier: str | None = None
+    cloud: PowerPlatformCloud = Field(default=PowerPlatformCloud.UNKNOWN)
+    copilot_agent_type: AgentType = Field(default=AgentType.PUBLISHED)
+    custom_power_platform_cloud: str | None = None
+    client_secret: SecretStr | None = None
+    client_certificate: str | None = None
+    user_assertion: str | None = None
+    auth_mode: CopilotStudioAgentAuthMode = Field(default=CopilotStudioAgentAuthMode.INTERACTIVE)

python/semantic_kernel/agents/group_chat/agent_chat.py

@@ -0,0 +1,197 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+import threading
+from collections.abc import AsyncIterable
+
+from pydantic import Field, PrivateAttr
+
+from semantic_kernel.agents import Agent
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.agents.group_chat.agent_chat_utils import KeyEncoder
+from semantic_kernel.agents.group_chat.broadcast_queue import BroadcastQueue, ChannelReference
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import AgentChatException
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class AgentChat(KernelBaseModel):
+    """A base class chat interface for agents."""
+
+    broadcast_queue: BroadcastQueue = Field(default_factory=BroadcastQueue)
+    agent_channels: dict[str, AgentChannel] = Field(default_factory=dict)
+    channel_map: dict[Agent, str] = Field(default_factory=dict)
+    history: ChatHistory = Field(default_factory=ChatHistory)
+
+    _lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
+    _is_active: bool = False
+
+    @property
+    def is_active(self) -> bool:
+        """Indicates whether the agent is currently active."""
+        return self._is_active
+
+    def set_activity_or_throw(self):
+        """Set the activity signal or throw an exception if another agent is active."""
+        with self._lock:
+            if self._is_active:
+                raise Exception("Unable to proceed while another agent is active.")
+            self._is_active = True
+
+    def clear_activity_signal(self):
+        """Clear the activity signal."""
+        with self._lock:
+            self._is_active = False
+
+    def invoke(self, agent: Agent | None = None, is_joining: bool = True) -> AsyncIterable[ChatMessageContent]:
+        """Invoke the agent asynchronously."""
+        raise NotImplementedError("Subclasses should implement this method")
+
+    async def get_messages_in_descending_order(self) -> AsyncIterable[ChatMessageContent]:
+        """Get messages in descending order asynchronously."""
+        for index in range(len(self.history.messages) - 1, -1, -1):
+            yield self.history.messages[index]
+            await asyncio.sleep(0)  # Yield control to the event loop
+
+    async def get_chat_messages(self, agent: "Agent | None" = None) -> AsyncIterable[ChatMessageContent]:
+        """Get chat messages asynchronously."""
+        self.set_activity_or_throw()
+
+        logger.info("Getting chat messages")
+
+        messages: AsyncIterable[ChatMessageContent] | None = None
+        try:
+            if agent is None:
+                messages = self.get_messages_in_descending_order()
+            else:
+                channel_key = self._get_agent_hash(agent)
+                channel = await self._synchronize_channel(channel_key)
+                if channel is not None:
+                    messages = channel.get_history()
+            if messages is not None:
+                async for message in messages:
+                    yield message
+        finally:
+            self.clear_activity_signal()
+
+    async def _synchronize_channel(self, channel_key: str) -> AgentChannel | None:
+        """Synchronize a channel."""
+        channel = self.agent_channels.get(channel_key, None)
+        if channel:
+            await self.broadcast_queue.ensure_synchronized(ChannelReference(channel=channel, hash=channel_key))
+        return channel
+
+    def _get_agent_hash(self, agent: Agent):
+        """Get the hash of an agent."""
+        hash_value = self.channel_map.get(agent, None)
+        if hash_value is None:
+            hash_value = KeyEncoder.generate_hash(agent.get_channel_keys())
+            self.channel_map[agent] = hash_value
+
+        return hash_value
+
+    async def add_chat_message(self, message: str | ChatMessageContent) -> None:
+        """Add a chat message."""
+        if isinstance(message, str):
+            message = ChatMessageContent(role=AuthorRole.USER, content=message)
+
+        await self.add_chat_messages([message])
+
+    async def add_chat_messages(self, messages: list[ChatMessageContent]) -> None:
+        """Add chat messages."""
+        self.set_activity_or_throw()
+
+        for message in messages:
+            if message.role == AuthorRole.SYSTEM:
+                error_message = "System messages cannot be added to the chat history."
+                logger.error(error_message)
+                raise AgentChatException(error_message)
+
+        logger.info(f"Adding `{len(messages)}` agent chat messages")
+
+        try:
+            self.history.messages.extend(messages)
+
+            # Broadcast message to other channels (in parallel)
+            # Note: Able to queue messages without synchronizing channels.
+            channel_refs = [ChannelReference(channel=channel, hash=key) for key, channel in self.agent_channels.items()]
+            await self.broadcast_queue.enqueue(channel_refs, messages)
+        finally:
+            self.clear_activity_signal()
+
+    async def _get_or_create_channel(self, agent: Agent) -> AgentChannel:
+        """Get or create a channel."""
+        channel_key = self._get_agent_hash(agent)
+        channel = await self._synchronize_channel(channel_key)
+        if channel is None:
+            channel = await agent.create_channel()
+            self.agent_channels[channel_key] = channel
+
+            if len(self.history.messages) > 0:
+                await channel.receive(self.history.messages)
+        return channel
+
+    async def invoke_agent(self, agent: Agent) -> AsyncIterable[ChatMessageContent]:
+        """Invoke an agent asynchronously."""
+        self.set_activity_or_throw()
+        logger.info(f"Invoking agent {agent.name}")
+        try:
+            channel: AgentChannel = await self._get_or_create_channel(agent)
+            messages: list[ChatMessageContent] = []
+
+            async for is_visible, message in channel.invoke(agent):
+                messages.append(message)
+                self.history.messages.append(message)
+                if is_visible:
+                    yield message
+
+            # Broadcast message to other channels (in parallel)
+            # Note: Able to queue messages without synchronizing channels.
+            channel_refs = [
+                ChannelReference(channel=ch, hash=key) for key, ch in self.agent_channels.items() if ch != channel
+            ]
+            await self.broadcast_queue.enqueue(channel_refs, messages)
+        finally:
+            self.clear_activity_signal()
+
+    async def invoke_agent_stream(self, agent: Agent) -> AsyncIterable[ChatMessageContent]:
+        """Invoke an agent stream asynchronously."""
+        self.set_activity_or_throw()
+        logger.info(f"Invoking agent {agent.name}")
+        try:
+            channel: AgentChannel = await self._get_or_create_channel(agent)
+            messages: list[ChatMessageContent] = []
+
+            async for message in channel.invoke_stream(agent, messages):
+                yield message
+
+            for message in messages:
+                self.history.messages.append(message)
+
+            # Broadcast message to other channels (in parallel)
+            # Note: Able to queue messages without synchronizing channels.
+            channel_refs = [
+                ChannelReference(channel=ch, hash=key) for key, ch in self.agent_channels.items() if ch != channel
+            ]
+            await self.broadcast_queue.enqueue(channel_refs, messages)
+        finally:
+            self.clear_activity_signal()
+
+    async def reset(self) -> None:
+        """Reset the agent chat."""
+        self.set_activity_or_throw()
+
+        try:
+            await asyncio.gather(*(channel.reset() for channel in self.agent_channels.values()))
+            self.agent_channels.clear()
+            self.channel_map.clear()
+            self.history.messages.clear()
+        finally:
+            self.clear_activity_signal()

python/semantic_kernel/agents/group_chat/agent_chat_utils.py

@@ -0,0 +1,33 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import base64
+import hashlib
+from collections.abc import Iterable
+
+from semantic_kernel.exceptions.agent_exceptions import AgentExecutionException
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class KeyEncoder:
+    """A class for encoding keys."""
+
+    @staticmethod
+    def generate_hash(keys: Iterable[str]) -> str:
+        """Generate a hash from a list of keys.
+
+        Args:
+            keys: A list of keys to generate the hash from.
+
+        Returns:
+            str: The generated hash.
+
+        Raises:
+            AgentExecutionException: If the keys are empty
+        """
+        if not keys:
+            raise AgentExecutionException("Channel Keys must not be empty. Unable to generate channel hash.")
+        joined_keys = ":".join(keys)
+        buffer = joined_keys.encode("utf-8")
+        sha256_hash = hashlib.sha256(buffer).digest()
+        return base64.b64encode(sha256_hash).decode("utf-8")

python/semantic_kernel/agents/group_chat/agent_group_chat.py

@@ -0,0 +1,228 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+from collections.abc import AsyncIterable
+from copy import deepcopy
+from typing import TYPE_CHECKING, Any, cast
+
+from pydantic import Field
+
+from semantic_kernel.agents import Agent, AgentChat
+from semantic_kernel.agents.strategies import (
+    DefaultTerminationStrategy,
+    SequentialSelectionStrategy,
+)
+from semantic_kernel.agents.strategies.selection.selection_strategy import SelectionStrategy
+from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.history_reducer.chat_history_reducer import ChatHistoryReducer
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import AgentChatException
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.contents.chat_history import ChatHistory
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class AgentGroupChat(AgentChat):
+    """An agent chat that supports multi-turn interactions."""
+
+    agent_ids: set[str]
+    agents: list[Agent] = Field(default_factory=list)
+
+    is_complete: bool = False
+    termination_strategy: TerminationStrategy = Field(
+        default_factory=DefaultTerminationStrategy,
+        description="The termination strategy to use. The default strategy never terminates and has a max iterations of 5.",  # noqa: E501
+    )
+    selection_strategy: SelectionStrategy = Field(default_factory=SequentialSelectionStrategy)
+
+    def __init__(
+        self,
+        agents: list[Agent] | None = None,
+        termination_strategy: TerminationStrategy | None = None,
+        selection_strategy: SelectionStrategy | None = None,
+        chat_history: "ChatHistory | None" = None,
+    ) -> None:
+        """Initialize a new instance of AgentGroupChat.
+
+        Args:
+            agents: The agents to add to the group chat.
+            termination_strategy: The termination strategy to use.
+            selection_strategy: The selection strategy
+            chat_history: The chat history.
+        """
+        agent_ids = {agent.id for agent in agents} if agents else set()
+
+        if agents is None:
+            agents = []
+
+        args: dict[str, Any] = {
+            "agents": agents,
+            "agent_ids": agent_ids,
+        }
+
+        if termination_strategy is not None:
+            args["termination_strategy"] = termination_strategy
+        if selection_strategy is not None:
+            args["selection_strategy"] = selection_strategy
+        if chat_history is not None:
+            args["history"] = chat_history
+
+        super().__init__(**args)
+
+    def add_agent(self, agent: Agent) -> None:
+        """Add an agent to the group chat.
+
+        Args:
+            agent: The agent to add.
+        """
+        if agent.id not in self.agent_ids:
+            self.agent_ids.add(agent.id)
+            self.agents.append(agent)
+
+    async def invoke_single_turn(self, agent: Agent) -> AsyncIterable[ChatMessageContent]:
+        """Invoke the agent chat for a single turn.
+
+        Args:
+            agent: The agent to invoke.
+
+        Yields:
+            The chat message.
+        """
+        async for message in self.invoke(agent, is_joining=True):
+            if message.role == AuthorRole.ASSISTANT:
+                task = self.termination_strategy.should_terminate(agent, self.history.messages)
+                self.is_complete = await task
+            yield message
+
+    async def invoke_stream_single_turn(self, agent: Agent) -> AsyncIterable[ChatMessageContent]:
+        """Invoke the agent chat for a single turn.
+
+        Args:
+            agent: The agent to invoke.
+
+        Yields:
+            The chat message.
+        """
+        async for message in self.invoke_stream(agent, is_joining=True):
+            yield message
+
+        self.is_complete = await self.termination_strategy.should_terminate(agent, self.history.messages)
+
+    async def invoke(self, agent: Agent | None = None, is_joining: bool = True) -> AsyncIterable[ChatMessageContent]:
+        """Invoke the agent chat asynchronously.
+
+        Handles both group interactions and single agent interactions based on the provided arguments.
+
+        Args:
+            agent: The agent to invoke. If not provided, the method processes all agents in the chat.
+            is_joining: Controls whether the agent joins the chat. Defaults to True.
+
+        Yields:
+            The chat message.
+        """
+        if agent is not None:
+            if is_joining:
+                self.add_agent(agent)
+
+            async for message in super().invoke_agent(agent):
+                if message.role == AuthorRole.ASSISTANT:
+                    task = self.termination_strategy.should_terminate(agent, self.history.messages)
+                    self.is_complete = await task
+                yield message
+
+            return
+
+        if not self.agents:
+            raise AgentChatException("No agents are available")
+
+        if self.is_complete:
+            if not self.termination_strategy.automatic_reset:
+                raise AgentChatException("Chat is already complete")
+
+            self.is_complete = False
+
+        for _ in range(self.termination_strategy.maximum_iterations):
+            try:
+                selected_agent = await self.selection_strategy.next(self.agents, self.history.messages)
+            except Exception as ex:
+                logger.error(f"Failed to select agent: {ex}")
+                raise AgentChatException("Failed to select agent") from ex
+
+            async for message in super().invoke_agent(selected_agent):
+                if message.role == AuthorRole.ASSISTANT:
+                    task = self.termination_strategy.should_terminate(selected_agent, self.history.messages)
+                    self.is_complete = await task
+                yield message
+
+            if self.is_complete:
+                break
+
+    async def invoke_stream(
+        self, agent: Agent | None = None, is_joining: bool = True
+    ) -> AsyncIterable[ChatMessageContent]:
+        """Invoke the agent chat stream asynchronously.
+
+        Handles both group interactions and single agent interactions based on the provided arguments.
+
+        Args:
+            agent: The agent to invoke. If not provided, the method processes all agents in the chat.
+            is_joining: Controls whether the agent joins the chat. Defaults to True.
+
+        Yields:
+            The chat message.
+        """
+        if agent is not None:
+            if is_joining:
+                self.add_agent(agent)
+
+            async for message in super().invoke_agent_stream(agent):
+                if message.role == AuthorRole.ASSISTANT:
+                    task = self.termination_strategy.should_terminate(agent, self.history.messages)
+                    self.is_complete = await task
+                yield message
+
+            return
+
+        if not self.agents:
+            raise AgentChatException("No agents are available")
+
+        if self.is_complete:
+            if not self.termination_strategy.automatic_reset:
+                raise AgentChatException("Chat is already complete")
+
+            self.is_complete = False
+
+        for _ in range(self.termination_strategy.maximum_iterations):
+            try:
+                selected_agent = await self.selection_strategy.next(self.agents, self.history.messages)
+            except Exception as ex:
+                logger.error(f"Failed to select agent: {ex}")
+                raise AgentChatException("Failed to select agent") from ex
+
+            async for message in super().invoke_agent_stream(selected_agent):
+                yield message
+
+            self.is_complete = await self.termination_strategy.should_terminate(selected_agent, self.history.messages)
+
+            if self.is_complete:
+                break
+
+    async def reduce_history(self) -> bool:
+        """Perform the reduction on the provided history, returning True if reduction occurred."""
+        if not isinstance(self.history, ChatHistoryReducer):
+            return False
+
+        result = await self.history.reduce()
+        if result is None:
+            return False
+
+        reducer = cast(ChatHistoryReducer, result)
+        reduced_history = deepcopy(reducer.messages)
+        await self.reset()
+        await self.add_chat_messages(reduced_history)
+        return True

python/semantic_kernel/agents/group_chat/broadcast_queue.py

@@ -0,0 +1,129 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import asyncio
+from collections import deque
+from dataclasses import dataclass, field
+from typing import Any
+
+from pydantic import Field, SkipValidation, ValidationError, model_validator
+
+from semantic_kernel.agents.channels.agent_channel import AgentChannel
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class QueueReference(KernelBaseModel):
+    """Utility class to associate a queue with its specific lock."""
+
+    queue: deque = Field(default_factory=deque)
+    queue_lock: SkipValidation[asyncio.Lock] = Field(default_factory=asyncio.Lock, exclude=True)
+    receive_task: SkipValidation[asyncio.Task | None] = None
+    receive_failure: Exception | None = None
+
+    @property
+    def is_empty(self):
+        """Check if the queue is empty."""
+        return len(self.queue) == 0
+
+    @model_validator(mode="before")
+    def validate_receive_task(cls, values: Any):
+        """Validate the receive task."""
+        if isinstance(values, dict):
+            receive_task = values.get("receive_task")
+            if receive_task is not None and not isinstance(receive_task, asyncio.Task):
+                raise ValidationError("receive_task must be an instance of asyncio.Task or None")
+        return values
+
+
+@experimental
+@dataclass
+class ChannelReference:
+    """Tracks a channel along with its hashed key."""
+
+    hash: str
+    channel: AgentChannel = field(default_factory=AgentChannel)
+
+
+@experimental
+class BroadcastQueue(KernelBaseModel):
+    """A queue for broadcasting messages to listeners."""
+
+    queues: dict[str, QueueReference] = Field(default_factory=dict)
+    block_duration: float = 0.1
+
+    async def enqueue(self, channel_refs: list[ChannelReference], messages: list[ChatMessageContent]) -> None:
+        """Enqueue a set of messages for a given channel.
+
+        Args:
+            channel_refs: The channel references.
+            messages: The messages to broadcast.
+        """
+        for channel_ref in channel_refs:
+            if channel_ref.hash not in self.queues:
+                self.queues[channel_ref.hash] = QueueReference()
+
+            queue_ref = self.queues[channel_ref.hash]
+
+            async with queue_ref.queue_lock:
+                queue_ref.queue.append(messages)
+
+                if not queue_ref.receive_task or queue_ref.receive_task.done():
+                    queue_ref.receive_task = asyncio.create_task(self.receive(channel_ref, queue_ref))
+
+    async def ensure_synchronized(self, channel_ref: ChannelReference) -> None:
+        """Blocks until a channel-queue is not in a receive state to ensure that channel history is complete.
+
+        Args:
+            channel_ref: The channel reference.
+        """
+        if channel_ref.hash not in self.queues:
+            return
+
+        queue_ref = self.queues[channel_ref.hash]
+
+        while True:
+            async with queue_ref.queue_lock:
+                is_empty = queue_ref.is_empty
+
+                if queue_ref.receive_failure is not None:
+                    failure = queue_ref.receive_failure
+                    queue_ref.receive_failure = None
+                    raise Exception(
+                        f"Unexpected failure broadcasting to channel: {type(channel_ref.channel)}, failure: {failure}"
+                    ) from failure
+
+                if not is_empty and (not queue_ref.receive_task or queue_ref.receive_task.done()):
+                    queue_ref.receive_task = asyncio.create_task(self.receive(channel_ref, queue_ref))
+
+            if is_empty:
+                break
+
+            await asyncio.sleep(self.block_duration)
+
+    async def receive(self, channel_ref: ChannelReference, queue_ref: QueueReference) -> None:
+        """Processes the specified queue with the provided channel, until the queue is empty.
+
+        Args:
+            channel_ref: The channel reference.
+            queue_ref: The queue reference.
+        """
+        while True:
+            async with queue_ref.queue_lock:
+                if queue_ref.is_empty:
+                    break
+
+                messages = queue_ref.queue[0]
+            try:
+                await channel_ref.channel.receive(messages)
+            except Exception as e:
+                queue_ref.receive_failure = e
+
+            async with queue_ref.queue_lock:
+                if not queue_ref.is_empty:
+                    queue_ref.queue.popleft()
+
+                if queue_ref.receive_failure is not None or queue_ref.is_empty:
+                    break

python/semantic_kernel/agents/open_ai/assistant_content_generation.py

@@ -0,0 +1,566 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any
+
+from openai import AsyncOpenAI
+from openai.types.beta.threads.file_citation_annotation import FileCitationAnnotation
+from openai.types.beta.threads.file_citation_delta_annotation import FileCitationDeltaAnnotation
+from openai.types.beta.threads.file_path_annotation import FilePathAnnotation
+from openai.types.beta.threads.file_path_delta_annotation import FilePathDeltaAnnotation
+from openai.types.beta.threads.image_file_content_block import ImageFileContentBlock
+from openai.types.beta.threads.image_file_delta_block import ImageFileDeltaBlock
+from openai.types.beta.threads.message_delta_event import MessageDeltaEvent
+from openai.types.beta.threads.runs import CodeInterpreterLogs
+from openai.types.beta.threads.runs.code_interpreter_tool_call import CodeInterpreterOutputImage
+from openai.types.beta.threads.text_content_block import TextContentBlock
+from openai.types.beta.threads.text_delta_block import TextDeltaBlock
+
+from semantic_kernel.contents.annotation_content import AnnotationContent
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.file_reference_content import FileReferenceContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.function_result_content import FunctionResultContent
+from semantic_kernel.contents.image_content import ImageContent
+from semantic_kernel.contents.streaming_annotation_content import StreamingAnnotationContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.streaming_file_reference_content import StreamingFileReferenceContent
+from semantic_kernel.contents.streaming_text_content import StreamingTextContent
+from semantic_kernel.contents.text_content import TextContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import AgentExecutionException
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from openai.types.beta.threads.message import Message
+    from openai.types.beta.threads.run import Run
+    from openai.types.beta.threads.runs import RunStep
+    from openai.types.beta.threads.runs.tool_call import ToolCall
+    from openai.types.beta.threads.runs.tool_calls_step_details import ToolCallsStepDetails
+
+
+###################################################################
+# The methods in this file are used with OpenAIAssistantAgent     #
+# related code. They are used to create chat messages, or         #
+# generate message content.                                       #
+###################################################################
+
+
+@experimental
+async def create_chat_message(
+    client: AsyncOpenAI,
+    thread_id: str,
+    message: "ChatMessageContent",
+    allowed_message_roles: Sequence[str] | None = None,
+) -> "Message":
+    """Class method to add a chat message, callable from class or instance.
+
+    Args:
+        client: The client to use for creating the message.
+        thread_id: The thread id.
+        message: The chat message.
+        allowed_message_roles: The allowed message roles.
+            Defaults to [AuthorRole.USER, AuthorRole.ASSISTANT] if None.
+            Providing an empty list will disallow all message roles.
+
+    Returns:
+        Message: The message.
+    """
+    # Set the default allowed message roles if not provided
+    if allowed_message_roles is None:
+        allowed_message_roles = [AuthorRole.USER, AuthorRole.ASSISTANT]
+    if message.role.value not in allowed_message_roles and message.role != AuthorRole.TOOL:
+        raise AgentExecutionException(
+            f"Invalid message role `{message.role.value}`. Allowed roles are {allowed_message_roles}."
+        )
+
+    message_contents: list[dict[str, Any]] = get_message_contents(message=message)
+
+    return await client.beta.threads.messages.create(
+        thread_id=thread_id,
+        role="assistant" if message.role == AuthorRole.TOOL else message.role.value,  # type: ignore
+        content=message_contents,  # type: ignore
+    )
+
+
+@experimental
+def get_message_contents(message: "ChatMessageContent") -> list[dict[str, Any]]:
+    """Get the message contents.
+
+    Args:
+        message: The message.
+    """
+    contents: list[dict[str, Any]] = []
+    for content in message.items:
+        match content:
+            case TextContent():
+                # Make sure text is a string
+                final_text = content.text
+                if not isinstance(final_text, str):
+                    if isinstance(final_text, (list, tuple)):
+                        final_text = " ".join(map(str, final_text))
+                    else:
+                        final_text = str(final_text)
+
+                contents.append({"type": "text", "text": final_text})
+
+            case ImageContent():
+                if content.uri:
+                    contents.append(content.to_dict())
+
+            case FileReferenceContent():
+                contents.append({
+                    "type": "image_file",
+                    "image_file": {"file_id": content.file_id},
+                })
+
+            case FunctionResultContent():
+                final_result = content.result
+                match final_result:
+                    case str():
+                        contents.append({"type": "text", "text": final_result})
+                    case list() | tuple():
+                        contents.append({"type": "text", "text": " ".join(map(str, final_result))})
+                    case _:
+                        contents.append({"type": "text", "text": str(final_result)})
+
+    return contents
+
+
+@experimental
+def generate_message_content(
+    assistant_name: str, message: "Message", completed_step: "RunStep | None" = None
+) -> ChatMessageContent:
+    """Generate message content."""
+    role = AuthorRole(message.role)
+
+    metadata = (
+        {
+            "created_at": completed_step.created_at,
+            "message_id": message.id,  # message needs to be defined in context
+            "step_id": completed_step.id,
+            "run_id": completed_step.run_id,
+            "thread_id": completed_step.thread_id,
+            "assistant_id": completed_step.assistant_id,
+            "usage": completed_step.usage,
+        }
+        if completed_step is not None
+        else None
+    )
+
+    content: ChatMessageContent = ChatMessageContent(role=role, name=assistant_name, metadata=metadata)  # type: ignore
+
+    for item_content in message.content:
+        if item_content.type == "text":
+            assert isinstance(item_content, TextContentBlock)  # nosec
+            content.items.append(
+                TextContent(
+                    text=item_content.text.value,
+                )
+            )
+            for annotation in item_content.text.annotations:
+                content.items.append(generate_annotation_content(annotation))
+        elif item_content.type == "image_file":
+            assert isinstance(item_content, ImageFileContentBlock)  # nosec
+            content.items.append(
+                FileReferenceContent(
+                    file_id=item_content.image_file.file_id,
+                )
+            )
+    return content
+
+
+@experimental
+def generate_streaming_message_content(
+    assistant_name: str,
+    message_delta_event: "MessageDeltaEvent",
+    completed_step: "RunStep | None" = None,
+) -> StreamingChatMessageContent:
+    """Generate streaming message content from a MessageDeltaEvent."""
+    delta = message_delta_event.delta
+
+    metadata = (
+        {
+            "created_at": completed_step.created_at,
+            "message_id": message_delta_event.id,  # message needs to be defined in context
+            "step_id": completed_step.id,
+            "run_id": completed_step.run_id,
+            "thread_id": completed_step.thread_id,
+            "assistant_id": completed_step.assistant_id,
+            "usage": completed_step.usage,
+        }
+        if completed_step is not None
+        else None
+    )
+
+    # Determine the role
+    role = AuthorRole(delta.role) if delta.role is not None else AuthorRole("assistant")
+
+    items: list[StreamingTextContent | StreamingAnnotationContent | StreamingFileReferenceContent] = []
+
+    # Process each content block in the delta
+    for delta_block in delta.content or []:
+        if delta_block.type == "text":
+            assert isinstance(delta_block, TextDeltaBlock)  # nosec
+            if delta_block.text and delta_block.text.value:  # Ensure text is not None
+                text_value = delta_block.text.value
+                items.append(
+                    StreamingTextContent(
+                        text=text_value,
+                        choice_index=delta_block.index,
+                    )
+                )
+                # Process annotations if any
+                if delta_block.text.annotations:
+                    for annotation in delta_block.text.annotations or []:
+                        if isinstance(annotation, (FileCitationDeltaAnnotation, FilePathDeltaAnnotation)):
+                            items.append(generate_streaming_annotation_content(annotation))
+        elif delta_block.type == "image_file":
+            assert isinstance(delta_block, ImageFileDeltaBlock)  # nosec
+            if delta_block.image_file and delta_block.image_file.file_id:
+                file_id = delta_block.image_file.file_id
+                items.append(
+                    StreamingFileReferenceContent(
+                        file_id=file_id,
+                    )
+                )
+
+    return StreamingChatMessageContent(role=role, name=assistant_name, items=items, choice_index=0, metadata=metadata)  # type: ignore
+
+
+@experimental
+def generate_final_streaming_message_content(
+    assistant_name: str,
+    message: "Message",
+    completed_step: "RunStep | None" = None,
+) -> StreamingChatMessageContent:
+    """Generate streaming message content from a MessageDeltaEvent."""
+    metadata = (
+        {
+            "created_at": completed_step.created_at,
+            "message_id": message.id,  # message needs to be defined in context
+            "step_id": completed_step.id,
+            "run_id": completed_step.run_id,
+            "thread_id": completed_step.thread_id,
+            "assistant_id": completed_step.assistant_id,
+            "usage": completed_step.usage,
+        }
+        if completed_step is not None
+        else None
+    )
+
+    # Determine the role
+    role = AuthorRole(message.role) if message.role is not None else AuthorRole("assistant")
+
+    items: list[StreamingTextContent | StreamingAnnotationContent | StreamingFileReferenceContent] = []
+
+    # Process each content block in the delta
+    for item_content in message.content:
+        if item_content.type == "text":
+            assert isinstance(item_content, TextContentBlock)  # nosec
+            items.append(StreamingTextContent(text=item_content.text.value, choice_index=0))
+            for annotation in item_content.text.annotations:
+                items.append(generate_streaming_annotation_content(annotation))
+        elif item_content.type == "image_file":
+            assert isinstance(item_content, ImageFileContentBlock)  # nosec
+            items.append(
+                StreamingFileReferenceContent(
+                    file_id=item_content.image_file.file_id,
+                )
+            )
+
+    return StreamingChatMessageContent(role=role, name=assistant_name, items=items, choice_index=0, metadata=metadata)  # type: ignore
+
+
+@experimental
+def merge_function_results(messages: list["ChatMessageContent"], name: str) -> "ChatMessageContent":
+    """Combine multiple function result content types to one chat message content type.
+
+    This method combines the FunctionResultContent items from separate ChatMessageContent messages,
+    and is used in the event that the `context.terminate = True` condition is met.
+
+    Args:
+        messages: The list of chat messages.
+        name: The name of the agent.
+
+    Returns:
+        list[ChatMessageContent]: The combined chat message content.
+    """
+    from semantic_kernel.contents.chat_message_content import ChatMessageContent
+    from semantic_kernel.contents.function_result_content import FunctionResultContent
+
+    items: list[Any] = []
+    for message in messages:
+        items.extend([item for item in message.items if isinstance(item, FunctionResultContent)])
+    return ChatMessageContent(
+        role=AuthorRole.TOOL,
+        items=items,
+        name=name,
+    )
+
+
+@experimental
+def merge_streaming_function_results(
+    messages: list["ChatMessageContent | StreamingChatMessageContent"],
+    name: str,
+    ai_model_id: str | None = None,
+    function_invoke_attempt: int | None = None,
+) -> "StreamingChatMessageContent":
+    """Combine multiple streaming function result content types to one streaming chat message content type.
+
+    This method combines the FunctionResultContent items from separate StreamingChatMessageContent messages,
+    and is used in the event that the `context.terminate = True` condition is met.
+
+    Args:
+        messages: The list of streaming chat message content types.
+        name: The name of the agent.
+        ai_model_id: The AI model ID.
+        function_invoke_attempt: The function invoke attempt.
+
+    Returns:
+        The combined streaming chat message content type.
+    """
+    from semantic_kernel.contents.function_result_content import FunctionResultContent
+    from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+
+    items: list[Any] = []
+    for message in messages:
+        items.extend([item for item in message.items if isinstance(item, FunctionResultContent)])
+
+    return StreamingChatMessageContent(
+        name=name,
+        role=AuthorRole.TOOL,
+        items=items,
+        choice_index=0,
+        ai_model_id=ai_model_id,
+        function_invoke_attempt=function_invoke_attempt,
+    )
+
+
+@experimental
+def generate_function_call_content(agent_name: str, fccs: list[FunctionCallContent]) -> ChatMessageContent:
+    """Generate function call content.
+
+    Args:
+        agent_name: The agent name.
+        fccs: The function call contents.
+
+    Returns:
+        ChatMessageContent: The chat message content containing the function call content as the items.
+    """
+    return ChatMessageContent(role=AuthorRole.ASSISTANT, name=agent_name, items=fccs)  # type: ignore
+
+
+@experimental
+def generate_function_result_content(
+    agent_name: str, function_step: FunctionCallContent, tool_call: "ToolCall"
+) -> ChatMessageContent:
+    """Generate function result content."""
+    function_call_content: ChatMessageContent = ChatMessageContent(role=AuthorRole.TOOL, name=agent_name)  # type: ignore
+    function_call_content.items.append(
+        FunctionResultContent(
+            function_name=function_step.function_name,
+            plugin_name=function_step.plugin_name,
+            id=function_step.id,
+            result=tool_call.function.output,  # type: ignore
+        )
+    )
+    return function_call_content
+
+
+@experimental
+def get_function_call_contents(run: "Run", function_steps: dict[str, FunctionCallContent]) -> list[FunctionCallContent]:
+    """Extract function call contents from the run.
+
+    Args:
+        run: The run.
+        function_steps: The function steps
+
+    Returns:
+        The list of function call contents.
+    """
+    function_call_contents: list[FunctionCallContent] = []
+    required_action = getattr(run, "required_action", None)
+    if not required_action or not getattr(required_action, "submit_tool_outputs", False):
+        return function_call_contents
+    for tool in required_action.submit_tool_outputs.tool_calls:
+        fcc = FunctionCallContent(
+            id=tool.id,
+            index=getattr(tool, "index", None),
+            name=tool.function.name,
+            arguments=tool.function.arguments,
+        )
+        function_call_contents.append(fcc)
+        function_steps[tool.id] = fcc
+    return function_call_contents
+
+
+@experimental
+def generate_code_interpreter_content(agent_name: str, code: str) -> "ChatMessageContent":
+    """Generate code interpreter content.
+
+    Args:
+        agent_name: The agent name.
+        code: The code.
+
+    Returns:
+        ChatMessageContent: The chat message content.
+    """
+    return ChatMessageContent(
+        role=AuthorRole.ASSISTANT,
+        content=code,
+        name=agent_name,
+        metadata={"code": True},
+    )
+
+
+@experimental
+def generate_streaming_function_content(
+    agent_name: str, step_details: "ToolCallsStepDetails"
+) -> "StreamingChatMessageContent":
+    """Generate streaming function content.
+
+    Args:
+        agent_name: The agent name.
+        step_details: The function step.
+
+    Returns:
+        StreamingChatMessageContent: The chat message content.
+    """
+    items: list[FunctionCallContent] = []
+
+    for tool in step_details.tool_calls:
+        if tool.type == "function":
+            items.append(
+                FunctionCallContent(
+                    id=tool.id,
+                    index=getattr(tool, "index", None),
+                    name=tool.function.name,
+                    arguments=tool.function.arguments,
+                )
+            )
+
+    return (
+        StreamingChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            name=agent_name,
+            items=items,  # type: ignore
+            choice_index=0,
+        )
+        if len(items) > 0
+        else None
+    )
+
+
+@experimental
+def generate_streaming_code_interpreter_content(
+    agent_name: str, step_details: "ToolCallsStepDetails"
+) -> "StreamingChatMessageContent | None":
+    """Generate code interpreter content.
+
+    Args:
+        agent_name: The agent name.
+        step_details: The current step details.
+
+    Returns:
+        StreamingChatMessageContent: The chat message content.
+    """
+    items: list[StreamingTextContent | StreamingFileReferenceContent] = []
+
+    metadata: dict[str, bool] = {}
+    for index, tool in enumerate(step_details.tool_calls):
+        if tool.type == "code_interpreter":
+            if tool.code_interpreter.input:
+                items.append(
+                    StreamingTextContent(
+                        choice_index=index,
+                        text=tool.code_interpreter.input,
+                    )
+                )
+                metadata["code"] = True
+            if tool.code_interpreter.outputs:
+                for output in tool.code_interpreter.outputs:
+                    if isinstance(output, CodeInterpreterOutputImage) and output.image.file_id:
+                        items.append(
+                            StreamingFileReferenceContent(
+                                file_id=output.image.file_id,
+                            )
+                        )
+                    if isinstance(output, CodeInterpreterLogs) and output.logs:
+                        items.append(
+                            StreamingTextContent(
+                                choice_index=index,
+                                text=output.logs,
+                            )
+                        )
+
+    return (
+        StreamingChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            name=agent_name,
+            items=items,  # type: ignore
+            choice_index=0,
+            metadata=metadata if metadata else None,
+        )
+        if len(items) > 0
+        else None
+    )
+
+
+@experimental
+def generate_annotation_content(annotation: FileCitationAnnotation | FilePathAnnotation) -> AnnotationContent:
+    """Generate annotation content."""
+    file_id = None
+    match annotation:
+        case FilePathAnnotation():
+            file_id = annotation.file_path.file_id
+        case FileCitationAnnotation():
+            file_id = annotation.file_citation.file_id
+
+    return AnnotationContent(
+        file_id=file_id,
+        quote=annotation.text,
+        start_index=annotation.start_index,
+        end_index=annotation.end_index,
+    )
+
+
+@experimental
+def generate_streaming_annotation_content(
+    annotation: FileCitationAnnotation | FilePathAnnotation | FilePathDeltaAnnotation | FileCitationDeltaAnnotation,
+) -> StreamingAnnotationContent:
+    """Generate streaming annotation content."""
+    file_id = None
+    match annotation:
+        case FilePathAnnotation():
+            file_id = annotation.file_path.file_id
+        case FileCitationAnnotation():
+            file_id = annotation.file_citation.file_id
+        case FilePathDeltaAnnotation():
+            file_id = annotation.file_path.file_id if annotation.file_path is not None else None
+        case FileCitationDeltaAnnotation():
+            file_id = annotation.file_citation.file_id if annotation.file_citation is not None else None
+
+    return StreamingAnnotationContent(
+        file_id=file_id,
+        quote=annotation.text,
+        start_index=annotation.start_index,
+        end_index=annotation.end_index,
+    )
+
+
+@experimental
+def generate_function_call_streaming_content(
+    agent_name: str,
+    fccs: list[FunctionCallContent],
+) -> StreamingChatMessageContent:
+    """Generate function call content.
+
+    Args:
+        agent_name: The agent name.
+        fccs: The function call contents.
+
+    Returns:
+        StreamingChatMessageContent: The chat message content containing the function call content as the items.
+    """
+    return StreamingChatMessageContent(role=AuthorRole.ASSISTANT, choice_index=0, name=agent_name, items=fccs)  # type: ignore

python/semantic_kernel/agents/open_ai/assistant_thread_actions.py

@@ -0,0 +1,950 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+from collections.abc import AsyncIterable, Iterable, Sequence
+from typing import TYPE_CHECKING, Any, ClassVar, Literal, TypeVar, cast
+
+from openai import AsyncOpenAI
+from openai._types import Omit, omit
+from openai.types.beta.code_interpreter_tool import CodeInterpreterTool
+from openai.types.beta.file_search_tool import FileSearchTool
+from openai.types.beta.threads.run_create_params import AdditionalMessage, AdditionalMessageAttachment
+from openai.types.beta.threads.runs import (
+    MessageCreationStepDetails,
+    RunStep,
+    RunStepDeltaEvent,
+    ToolCallDeltaObject,
+    ToolCallsStepDetails,
+)
+
+from semantic_kernel.agents.open_ai.assistant_content_generation import (
+    generate_code_interpreter_content,
+    generate_final_streaming_message_content,
+    generate_function_call_content,
+    generate_function_call_streaming_content,
+    generate_function_result_content,
+    generate_message_content,
+    generate_streaming_code_interpreter_content,
+    generate_streaming_message_content,
+    get_function_call_contents,
+    get_message_contents,
+    merge_streaming_function_results,
+)
+from semantic_kernel.agents.open_ai.function_action_result import FunctionActionResult
+from semantic_kernel.agents.open_ai.run_polling_options import RunPollingOptions
+from semantic_kernel.connectors.ai.function_calling_utils import kernel_function_metadata_to_function_call_format
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.function_choice_type import FunctionChoiceType
+from semantic_kernel.contents.file_reference_content import FileReferenceContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.streaming_file_reference_content import StreamingFileReferenceContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions.agent_exceptions import AgentExecutionException, AgentInvokeException
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.utils.feature_stage_decorator import release_candidate
+
+if TYPE_CHECKING:
+    from openai import AsyncOpenAI
+    from openai.types.beta.assistant_response_format_option_param import AssistantResponseFormatOptionParam
+    from openai.types.beta.assistant_tool_param import AssistantToolParam
+    from openai.types.beta.threads.message import Message
+    from openai.types.beta.threads.run import Run
+    from openai.types.beta.threads.run_create_params import AdditionalMessageAttachmentTool, TruncationStrategy
+
+    from semantic_kernel.agents.open_ai.openai_assistant_agent import OpenAIAssistantAgent
+    from semantic_kernel.contents.chat_history import ChatHistory
+    from semantic_kernel.contents.chat_message_content import ChatMessageContent
+    from semantic_kernel.contents.function_call_content import FunctionCallContent
+    from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+    from semantic_kernel.filters.auto_function_invocation.auto_function_invocation_context import (
+        AutoFunctionInvocationContext,
+    )
+    from semantic_kernel.kernel import Kernel
+
+_T = TypeVar("_T", bound="AssistantThreadActions")
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@release_candidate
+class AssistantThreadActions:
+    """Assistant Thread Actions class."""
+
+    polling_status: ClassVar[list[str]] = ["queued", "in_progress", "cancelling"]
+    error_message_states: ClassVar[list[str]] = ["failed", "cancelled", "expired", "incomplete"]
+
+    tool_metadata: ClassVar[dict[str, Sequence[Any]]] = {
+        "file_search": [{"type": "file_search"}],
+        "code_interpreter": [{"type": "code_interpreter"}],
+    }
+
+    # region Messaging Handling Methods
+
+    @classmethod
+    async def create_message(
+        cls: type[_T],
+        client: "AsyncOpenAI",
+        thread_id: str,
+        message: "str | ChatMessageContent",
+        allowed_message_roles: Sequence[str] | None = None,
+        **kwargs: Any,
+    ) -> "Message | None":
+        """Create a message in the thread.
+
+        Args:
+            client: The client to use to create the message.
+            thread_id: The ID of the thread to create the message in.
+            message: The message to create.
+            allowed_message_roles: The allowed message roles.
+                Defaults to [AuthorRole.USER, AuthorRole.ASSISTANT] if None.
+                Providing an empty list will disallow all message roles.
+            kwargs: Additional keyword arguments.
+
+        Returns:
+            The created message.
+        """
+        from semantic_kernel.contents.chat_message_content import ChatMessageContent
+
+        if isinstance(message, str):
+            message = ChatMessageContent(role=AuthorRole.USER, content=message)
+
+        if any(isinstance(item, FunctionCallContent) for item in message.items):
+            return None
+
+        # Set the default allowed message roles if not provided
+        if allowed_message_roles is None:
+            allowed_message_roles = [AuthorRole.USER, AuthorRole.ASSISTANT]
+        if message.role.value not in allowed_message_roles and message.role != AuthorRole.TOOL:
+            raise AgentExecutionException(
+                f"Invalid message role `{message.role.value}`. Allowed roles are {allowed_message_roles}."
+            )
+
+        message_contents: list[dict[str, Any]] = get_message_contents(message=message)
+
+        return await client.beta.threads.messages.create(
+            thread_id=thread_id,
+            role="assistant" if message.role == AuthorRole.TOOL else message.role.value,  # type: ignore
+            content=message_contents,  # type: ignore
+            **kwargs,
+        )
+
+    # endregion
+
+    # region Invocation Methods
+
+    @classmethod
+    async def invoke(
+        cls: type[_T],
+        *,
+        agent: "OpenAIAssistantAgent",
+        thread_id: str,
+        additional_instructions: str | None = None,
+        additional_messages: "list[ChatMessageContent] | None" = None,
+        arguments: KernelArguments | None = None,
+        instructions_override: str | None = None,
+        kernel: "Kernel | None" = None,
+        max_completion_tokens: int | None = None,
+        max_prompt_tokens: int | None = None,
+        metadata: dict[str, str] | None = None,
+        model: str | None = None,
+        parallel_tool_calls: bool | None = None,
+        reasoning_effort: Literal["low", "medium", "high"] | None = None,
+        response_format: "AssistantResponseFormatOptionParam | None" = None,
+        tools: "list[AssistantToolParam] | None" = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        truncation_strategy: "TruncationStrategy | None" = None,
+        polling_options: RunPollingOptions | None = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[tuple[bool, "ChatMessageContent"]]:
+        """Invoke the assistant.
+
+        Args:
+            agent: The assistant agent.
+            thread_id: The thread ID.
+            arguments: The kernel arguments.
+            kernel: The kernel.
+            instructions_override: The instructions override.
+            additional_instructions: The additional instructions.
+            additional_messages: The additional messages.
+            max_completion_tokens: The maximum completion tokens.
+            max_prompt_tokens: The maximum prompt tokens.
+            metadata: The metadata.
+            model: The model.
+            parallel_tool_calls: The parallel tool calls.
+            reasoning_effort: The reasoning effort.
+            response_format: The response format.
+            tools: The SDK-level tools (e.g. CodeInterpreter, FileSearch). When provided,
+                overrides the tools from the agent definition. Does not affect kernel function availability;
+                use function_choice_behavior for that.
+            temperature: The temperature.
+            top_p: The top p.
+            truncation_strategy: The truncation strategy.
+            polling_options: The polling options defined at the run-level. These will override the agent-level
+                polling options.
+            function_choice_behavior: Controls which kernel functions are allowed to execute during this run.
+                Use FunctionChoiceBehavior.Auto(filters={"included_functions": [...]}) to restrict to specific
+                functions. Only Auto is supported; other types will raise an error.
+            kwargs: Additional keyword arguments.
+
+        Returns:
+            An async iterable of tuple of the visibility of the message and the chat message content.
+        """
+        arguments = KernelArguments() if arguments is None else KernelArguments(**arguments, **kwargs)
+        kernel = kernel or agent.kernel
+
+        cls._validate_function_choice_behavior(function_choice_behavior)
+
+        tools = cls._get_tools(
+            agent=agent, kernel=kernel, tools_override=tools, function_choice_behavior=function_choice_behavior
+        )  # type: ignore
+
+        base_instructions = await agent.format_instructions(kernel=kernel, arguments=arguments)
+
+        merged_instructions: str = ""
+        if instructions_override is not None:
+            merged_instructions = instructions_override
+        elif base_instructions and additional_instructions:
+            merged_instructions = f"{base_instructions}\n\n{additional_instructions}"
+        else:
+            merged_instructions = base_instructions or additional_instructions or ""
+
+        # form run options
+        run_options = cls._generate_options(
+            agent=agent,
+            model=model,
+            response_format=response_format,
+            temperature=temperature,
+            top_p=top_p,
+            metadata=metadata,
+            parallel_tool_calls_enabled=parallel_tool_calls,
+            truncation_message_count=truncation_strategy,
+            max_completion_tokens=max_completion_tokens,
+            max_prompt_tokens=max_prompt_tokens,
+            additional_messages=additional_messages,
+            reasoning_effort=reasoning_effort,
+        )
+
+        run_options = {k: v for k, v in run_options.items() if v is not None}
+
+        run = await agent.client.beta.threads.runs.create(
+            assistant_id=agent.id,
+            thread_id=thread_id,
+            instructions=merged_instructions or agent.instructions,
+            tools=tools,  # type: ignore
+            **run_options,
+        )
+
+        processed_step_ids = set()
+        function_steps: dict[str, "FunctionCallContent"] = {}
+
+        while run.status != "completed":
+            run = await cls._poll_run_status(
+                agent=agent, run=run, thread_id=thread_id, polling_options=polling_options or agent.polling_options
+            )
+
+            if run.status in cls.error_message_states:
+                error_message = ""
+                if run.last_error and run.last_error.message:
+                    error_message = run.last_error.message
+                incomplete_details = ""
+                if run.incomplete_details:
+                    incomplete_details = str(run.incomplete_details.reason)
+                raise AgentInvokeException(
+                    f"Run failed with status: `{run.status}` for agent `{agent.name}` and thread `{thread_id}` "
+                    f"with error: {error_message} or incomplete details: {incomplete_details}"
+                )
+
+            # Check if function calling required
+            if run.status == "requires_action":
+                logger.debug(f"Run [{run.id}] requires action for agent `{agent.name}` and thread `{thread_id}`")
+                fccs = get_function_call_contents(run, function_steps)
+                if fccs:
+                    logger.debug(
+                        f"Yielding `generate_function_call_content` for agent `{agent.name}` and "
+                        f"thread `{thread_id}`, visibility False"
+                    )
+                    yield False, generate_function_call_content(agent_name=agent.name, fccs=fccs)
+
+                    from semantic_kernel.contents.chat_history import ChatHistory
+
+                    chat_history = ChatHistory()
+                    _ = await cls._invoke_function_calls(
+                        kernel=kernel,
+                        fccs=fccs,
+                        chat_history=chat_history,
+                        arguments=arguments,
+                        function_choice_behavior=function_choice_behavior,
+                    )
+
+                    tool_outputs = cls._format_tool_outputs(fccs, chat_history)
+                    await agent.client.beta.threads.runs.submit_tool_outputs(
+                        run_id=run.id,
+                        thread_id=thread_id,
+                        tool_outputs=tool_outputs,  # type: ignore
+                    )
+                    logger.debug(f"Submitted tool outputs for agent `{agent.name}` and thread `{thread_id}`")
+                    continue
+
+            steps_response = await agent.client.beta.threads.runs.steps.list(run_id=run.id, thread_id=thread_id)
+            logger.debug(f"Called for steps_response for run [{run.id}] agent `{agent.name}` and thread `{thread_id}`")
+            steps: list[RunStep] = steps_response.data
+
+            def sort_key(step: RunStep):
+                # Put tool_calls first, then message_creation
+                # If multiple steps share a type, break ties by completed_at
+                return (0 if step.type == "tool_calls" else 1, step.completed_at)
+
+            completed_steps_to_process = sorted(
+                [s for s in steps if s.completed_at is not None and s.id not in processed_step_ids], key=sort_key
+            )
+
+            logger.debug(
+                f"Completed steps to process for run [{run.id}] agent `{agent.name}` and thread `{thread_id}` "
+                f"with length `{len(completed_steps_to_process)}`"
+            )
+
+            message_count = 0
+            for completed_step in completed_steps_to_process:
+                if completed_step.type == "tool_calls":
+                    logger.debug(
+                        f"Entering step type tool_calls for run [{run.id}], agent `{agent.name}` and "
+                        f"thread `{thread_id}`"
+                    )
+                    assert hasattr(completed_step.step_details, "tool_calls")  # nosec
+                    tool_call_details = cast(ToolCallsStepDetails, completed_step.step_details)
+                    for tool_call in tool_call_details.tool_calls:
+                        is_visible = False
+                        content: "ChatMessageContent | None" = None
+                        if tool_call.type == "code_interpreter":
+                            logger.debug(
+                                f"Entering step type tool_calls for run [{run.id}], [code_interpreter] for "
+                                f"agent `{agent.name}` and thread `{thread_id}`"
+                            )
+                            content = generate_code_interpreter_content(
+                                agent.name,
+                                tool_call.code_interpreter.input,  # type: ignore
+                            )
+                            is_visible = True
+                        elif tool_call.type == "function":
+                            logger.debug(
+                                f"Entering step type tool_calls for run [{run.id}], [function] for agent "
+                                f"`{agent.name}` and thread `{thread_id}`"
+                            )
+                            function_step = function_steps.get(tool_call.id)
+                            assert function_step is not None  # nosec
+                            content = generate_function_result_content(
+                                agent_name=agent.name, function_step=function_step, tool_call=tool_call
+                            )
+
+                        if content:
+                            message_count += 1
+                            logger.debug(
+                                f"Yielding tool_message for run [{run.id}], agent `{agent.name}` and thread "
+                                f"`{thread_id}` and message count `{message_count}`, is_visible `{is_visible}`"
+                            )
+                            yield is_visible, content
+                elif completed_step.type == "message_creation":
+                    logger.debug(
+                        f"Entering step type message_creation for run [{run.id}], agent `{agent.name}` and "
+                        f"thread `{thread_id}`"
+                    )
+                    message = await cls._retrieve_message(
+                        agent=agent,
+                        thread_id=thread_id,
+                        message_id=completed_step.step_details.message_creation.message_id,  # type: ignore
+                    )
+                    if message:
+                        content = generate_message_content(agent.name, message, completed_step)
+                        if content and len(content.items) > 0:
+                            message_count += 1
+                            logger.debug(
+                                f"Yielding message_creation for run [{run.id}], agent `{agent.name}` and "
+                                f"thread `{thread_id}` and message count `{message_count}`, is_visible `{True}`"
+                            )
+                            yield True, content
+                processed_step_ids.add(completed_step.id)
+
+    @classmethod
+    async def invoke_stream(
+        cls: type[_T],
+        *,
+        agent: "OpenAIAssistantAgent",
+        thread_id: str,
+        additional_instructions: str | None = None,
+        additional_messages: "list[ChatMessageContent] | None" = None,
+        arguments: KernelArguments | None = None,
+        instructions_override: str | None = None,
+        kernel: "Kernel | None" = None,
+        max_completion_tokens: int | None = None,
+        max_prompt_tokens: int | None = None,
+        metadata: dict[str, str] | None = None,
+        model: str | None = None,
+        output_messages: list["ChatMessageContent"] | None = None,
+        parallel_tool_calls: bool | None = None,
+        reasoning_effort: Literal["low", "medium", "high"] | None = None,
+        response_format: "AssistantResponseFormatOptionParam | None" = None,
+        tools: "list[AssistantToolParam] | None" = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        truncation_strategy: "TruncationStrategy | None" = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable["StreamingChatMessageContent"]:
+        """Invoke the assistant.
+
+        Args:
+            agent: The assistant agent.
+            thread_id: The thread ID.
+            arguments: The kernel arguments.
+            kernel: The kernel.
+            instructions_override: The instructions override.
+            additional_instructions: The additional instructions.
+            additional_messages: The additional messages.
+            max_completion_tokens: The maximum completion tokens.
+            max_prompt_tokens: The maximum prompt tokens.
+            messages: The messages that act as a receiver for completed messages.
+            metadata: The metadata.
+            model: The model.
+            output_messages: The output messages received from the agent. These are full content messages
+                formed from the streamed chunks.
+            parallel_tool_calls: The parallel tool calls.
+            reasoning_effort: The reasoning effort.
+            response_format: The response format.
+            tools: The SDK-level tools (e.g. CodeInterpreter, FileSearch). When provided,
+                overrides the tools from the agent definition. Does not affect kernel function availability;
+                use function_choice_behavior for that.
+            temperature: The temperature.
+            top_p: The top p.
+            truncation_strategy: The truncation strategy.
+            function_choice_behavior: Controls which kernel functions are allowed to execute during this run.
+                Use FunctionChoiceBehavior.Auto(filters={"included_functions": [...]}) to restrict to specific
+                functions. Only Auto is supported; other types will raise an error.
+            kwargs: Additional keyword arguments.
+
+        Returns:
+            An async iterable of StreamingChatMessageContent.
+        """
+        arguments = KernelArguments() if arguments is None else KernelArguments(**arguments, **kwargs)
+        kernel = kernel or agent.kernel
+
+        cls._validate_function_choice_behavior(function_choice_behavior)
+
+        tools = cls._get_tools(
+            agent=agent, kernel=kernel, tools_override=tools, function_choice_behavior=function_choice_behavior
+        )  # type: ignore
+
+        base_instructions = await agent.format_instructions(kernel=kernel, arguments=arguments)
+
+        merged_instructions: str = ""
+        if instructions_override is not None:
+            merged_instructions = instructions_override
+        elif base_instructions and additional_instructions:
+            merged_instructions = f"{base_instructions}\n\n{additional_instructions}"
+        else:
+            merged_instructions = base_instructions or additional_instructions or ""
+
+        # form run options
+        run_options = cls._generate_options(
+            agent=agent,
+            model=model,
+            response_format=response_format,
+            temperature=temperature,
+            top_p=top_p,
+            metadata=metadata,
+            parallel_tool_calls_enabled=parallel_tool_calls,
+            truncation_message_count=truncation_strategy,
+            max_completion_tokens=max_completion_tokens,
+            max_prompt_tokens=max_prompt_tokens,
+            additional_messages=additional_messages,
+            reasoning_effort=reasoning_effort,
+        )
+
+        run_options = {k: v for k, v in run_options.items() if v is not None}
+
+        stream = agent.client.beta.threads.runs.stream(
+            assistant_id=agent.id,
+            thread_id=thread_id,
+            instructions=merged_instructions or agent.instructions,
+            tools=tools,  # type: ignore
+            **run_options,
+        )
+
+        function_steps: dict[str, "FunctionCallContent"] = {}
+        active_messages: dict[str, RunStep] = {}
+
+        while True:
+            async with stream as response_stream:
+                async for event in response_stream:
+                    if event.event == "thread.run.created":
+                        run = event.data
+                        logger.info(f"Assistant run created with ID: {run.id}")
+                    elif event.event == "thread.run.in_progress":
+                        run = event.data
+                        logger.info(f"Assistant run in progress with ID: {run.id}")
+                    elif event.event == "thread.message.delta":
+                        content = generate_streaming_message_content(agent.name, event.data)
+                        yield content
+                    elif event.event == "thread.run.step.completed":
+                        step_completed = cast(RunStep, event.data)
+                        logger.info(f"Run step completed with ID: {event.data.id}")
+                        if isinstance(step_completed.step_details, MessageCreationStepDetails):
+                            message_id = step_completed.step_details.message_creation.message_id
+                            if message_id not in active_messages:
+                                active_messages[message_id] = event.data
+                    elif event.event == "thread.run.step.delta":
+                        run_step_event: RunStepDeltaEvent = event.data
+                        details = run_step_event.delta.step_details
+                        if not details:
+                            continue
+                        step_details = event.data.delta.step_details
+                        if isinstance(details, ToolCallDeltaObject) and details.tool_calls:
+                            for tool_call in details.tool_calls:
+                                tool_content = None
+                                content_is_visible = False
+                                # Function Calling-related content is emitted as a single message
+                                # via the `on_intermediate_message` callback.
+                                if tool_call.type == "code_interpreter":
+                                    tool_content = generate_streaming_code_interpreter_content(agent.name, step_details)
+                                    content_is_visible = True
+                                if tool_content:
+                                    if output_messages is not None and not content_is_visible:
+                                        output_messages.append(tool_content)
+                                    if content_is_visible:
+                                        yield tool_content
+                    elif event.event == "thread.run.requires_action":
+                        run = event.data
+                        action_result = await cls._handle_streaming_requires_action(
+                            agent.name,
+                            kernel,
+                            run,
+                            function_steps,
+                            arguments,
+                            function_choice_behavior=function_choice_behavior,
+                        )
+                        if action_result is None:
+                            raise AgentInvokeException(
+                                f"Function call required but no function steps found for agent `{agent.name}` "
+                                f"thread: {thread_id}."
+                            )
+                        for content in (
+                            action_result.function_call_streaming_content,
+                            action_result.function_result_streaming_content,
+                        ):
+                            if content and output_messages is not None:
+                                output_messages.append(content)
+
+                        stream = agent.client.beta.threads.runs.submit_tool_outputs_stream(
+                            run_id=run.id,
+                            thread_id=thread_id,
+                            tool_outputs=action_result.tool_outputs,  # type: ignore
+                        )
+                        break
+                    elif event.event == "thread.run.completed":
+                        run = event.data
+                        logger.info(f"Run completed with ID: {run.id}")
+                        if len(active_messages) > 0:
+                            for id in active_messages:
+                                step: RunStep = active_messages[id]
+                                message = await cls._retrieve_message(
+                                    agent=agent,
+                                    thread_id=thread_id,
+                                    message_id=id,  # type: ignore
+                                )
+
+                                if message and message.content:
+                                    content = generate_final_streaming_message_content(agent.name, message, step)
+                                    if output_messages is not None:
+                                        output_messages.append(content)
+                        return
+                    elif event.event == "thread.run.failed":
+                        run = event.data  # type: ignore
+                        error_message = ""
+                        if run.last_error and run.last_error.message:
+                            error_message = run.last_error.message
+                        raise AgentInvokeException(
+                            f"Run failed with status: `{run.status}` for agent `{agent.name}` and thread `{thread_id}` "
+                            f"with error: {error_message}"
+                        )
+                else:
+                    # If the inner loop completes without encountering a 'break', exit the outer loop
+                    break
+
+    @classmethod
+    async def _handle_streaming_requires_action(
+        cls: type[_T],
+        agent_name: str,
+        kernel: "Kernel",
+        run: "Run",
+        function_steps: dict[str, "FunctionCallContent"],
+        arguments: KernelArguments,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+        **kwargs: Any,
+    ) -> FunctionActionResult | None:
+        """Handle the requires action event for a streaming run."""
+        fccs = get_function_call_contents(run, function_steps)
+        if fccs:
+            function_call_streaming_content = generate_function_call_streaming_content(agent_name=agent_name, fccs=fccs)
+            from semantic_kernel.contents.chat_history import ChatHistory
+
+            chat_history = ChatHistory() if kwargs.get("chat_history") is None else kwargs["chat_history"]
+            results = await cls._invoke_function_calls(
+                kernel=kernel,
+                fccs=fccs,
+                chat_history=chat_history,
+                arguments=arguments,
+                function_choice_behavior=function_choice_behavior,
+            )
+
+            function_result_streaming_content = merge_streaming_function_results(
+                messages=chat_history.messages[-len(results) :],
+                name=agent_name,
+            )
+            tool_outputs = cls._format_tool_outputs(fccs, chat_history)
+            return FunctionActionResult(
+                function_call_streaming_content,
+                function_result_streaming_content,
+                tool_outputs,
+            )
+        return None
+
+    # endregion
+
+    @classmethod
+    async def get_messages(
+        cls: type[_T],
+        client: AsyncOpenAI,
+        thread_id: str,
+        sort_order: Literal["asc", "desc"] | None = None,
+    ) -> AsyncIterable["ChatMessageContent"]:
+        """Get messages from the thread.
+
+        Args:
+            client: The client to use to get the messages.
+            thread_id: The ID of the thread to get the messages from.
+            sort_order: The sort order of the messages.
+
+        Returns:
+            An async iterable of ChatMessageContent.
+        """
+        agent_names: dict[str, Any] = {}
+        last_id: str | Omit = omit
+
+        while True:
+            messages = await client.beta.threads.messages.list(
+                thread_id=thread_id,
+                order=sort_order,  # type: ignore
+                after=last_id,
+            )
+
+            if not messages:
+                break
+
+            for message in messages.data:
+                last_id = message.id
+
+                if message.assistant_id and message.assistant_id.strip() not in agent_names:
+                    agent = await client.beta.assistants.retrieve(message.assistant_id)
+                    if agent.name and agent.name.strip():
+                        agent_names[agent.id] = agent.name
+
+                assistant_name = agent_names.get(message.assistant_id or "", None) or message.assistant_id or message.id
+                content = generate_message_content(str(assistant_name), message)
+
+                if len(content.items) > 0:
+                    yield content
+
+            if not messages.has_more:
+                break
+
+    @classmethod
+    async def _retrieve_message(
+        cls: type[_T], agent: "OpenAIAssistantAgent", thread_id: str, message_id: str
+    ) -> "Message | None":
+        """Retrieve a message from a thread."""
+        message: "Message | None" = None
+        count = 0
+        max_retries = 3
+        while count < max_retries:
+            try:
+                message = await agent.client.beta.threads.messages.retrieve(thread_id=thread_id, message_id=message_id)
+                break
+            except Exception as ex:
+                logger.error(f"Failed to retrieve message {message_id} from thread {thread_id}: {ex}")
+                count += 1
+                if count >= max_retries:
+                    logger.error(
+                        f"Max retries reached. Unable to retrieve message {message_id} from thread {thread_id}."
+                    )
+                    break
+                backoff_time: float = agent.polling_options.message_synchronization_delay.total_seconds() * (2**count)
+                await asyncio.sleep(backoff_time)
+        return message
+
+    @classmethod
+    async def _invoke_function_calls(
+        cls: type[_T],
+        kernel: "Kernel",
+        fccs: list["FunctionCallContent"],
+        chat_history: "ChatHistory",
+        arguments: KernelArguments,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+    ) -> list["AutoFunctionInvocationContext | None"]:
+        """Invoke the function calls."""
+        return await asyncio.gather(
+            *[
+                kernel.invoke_function_call(
+                    function_call=function_call,
+                    chat_history=chat_history,
+                    arguments=arguments,
+                    function_behavior=function_choice_behavior,
+                )
+                for function_call in fccs
+            ],
+        )
+
+    @classmethod
+    def _format_tool_outputs(
+        cls: type[_T], fccs: list["FunctionCallContent"], chat_history: "ChatHistory"
+    ) -> list[dict[str, str]]:
+        """Format the tool outputs for submission."""
+        from semantic_kernel.contents.function_result_content import FunctionResultContent
+
+        tool_call_lookup = {
+            tool_call.id: tool_call
+            for message in chat_history.messages
+            for tool_call in message.items
+            if isinstance(tool_call, FunctionResultContent) and tool_call.id is not None
+        }
+        return [
+            {"tool_call_id": fcc.id, "output": str(tool_call_lookup[fcc.id].result)}
+            for fcc in fccs
+            if fcc.id in tool_call_lookup
+        ]
+
+    @classmethod
+    async def _poll_run_status(
+        cls: type[_T], agent: "OpenAIAssistantAgent", run: "Run", thread_id: str, polling_options: RunPollingOptions
+    ) -> "Run":
+        """Poll the run status."""
+        logger.info(f"Polling run status: {run.id}, threadId: {thread_id}")
+
+        try:
+            run = await asyncio.wait_for(
+                cls._poll_loop(agent, run, thread_id, polling_options),
+                timeout=polling_options.run_polling_timeout.total_seconds(),
+            )
+        except asyncio.TimeoutError:
+            timeout_duration = polling_options.run_polling_timeout
+            error_message = f"Polling timed out for run id: `{run.id}` and thread id: `{thread_id}` after waiting {timeout_duration}."  # noqa: E501
+            logger.error(error_message)
+            raise AgentInvokeException(error_message)
+
+        logger.info(f"Polled run status: {run.status}, {run.id}, threadId: {thread_id}")
+        return run
+
+    @classmethod
+    async def _poll_loop(
+        cls: type[_T], agent: "OpenAIAssistantAgent", run: "Run", thread_id: str, polling_options: RunPollingOptions
+    ) -> "Run":
+        """Internal polling loop."""
+        count = 0
+        while True:
+            await asyncio.sleep(polling_options.get_polling_interval(count).total_seconds())
+            count += 1
+
+            try:
+                run = await agent.client.beta.threads.runs.retrieve(run.id, thread_id=thread_id)
+            except Exception as e:
+                logging.warning(f"Failed to retrieve run for run id: `{run.id}` and thread id: `{thread_id}`: {e}")
+                # Retry anyway
+
+            if run.status not in cls.polling_status:
+                break
+
+        return run
+
+    @classmethod
+    def _merge_options(
+        cls: type[_T],
+        *,
+        agent: "OpenAIAssistantAgent",
+        model: str | None = None,
+        response_format: "AssistantResponseFormatOptionParam | None" = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        metadata: dict[str, str] | None = None,
+        **kwargs: Any,
+    ) -> dict[str, Any]:
+        """Merge run-time options with the agent-level options.
+
+        Run-level parameters take precedence.
+        """
+        return {
+            "model": model if model is not None else agent.definition.model,
+            "response_format": response_format if response_format is not None else None,
+            "temperature": temperature if temperature is not None else agent.definition.temperature,
+            "top_p": top_p if top_p is not None else agent.definition.top_p,
+            "metadata": metadata if metadata is not None else agent.definition.metadata,
+            **kwargs,
+        }
+
+    @classmethod
+    def _generate_options(cls: type[_T], **kwargs: Any) -> dict[str, Any]:
+        """Generate a dictionary of options that can be passed directly to create_run."""
+        merged = cls._merge_options(**kwargs)
+        agent = kwargs.get("agent")
+        trunc_count = merged.get("truncation_message_count", None)
+        max_completion_tokens = merged.get("max_completion_tokens", None)
+        max_prompt_tokens = merged.get("max_prompt_tokens", None)
+        parallel_tool_calls = merged.get("parallel_tool_calls_enabled", None)
+        additional_messages = cls._translate_additional_messages(agent, merged.get("additional_messages", None))
+        return {
+            "model": merged.get("model"),
+            "top_p": merged.get("top_p"),
+            "response_format": merged.get("response_format"),
+            "temperature": merged.get("temperature"),
+            "truncation_strategy": trunc_count,
+            "metadata": merged.get("metadata"),
+            "max_completion_tokens": max_completion_tokens,
+            "max_prompt_tokens": max_prompt_tokens,
+            "parallel_tool_calls": parallel_tool_calls,
+            "additional_messages": additional_messages,
+        }
+
+    @classmethod
+    def _translate_additional_messages(
+        cls: type[_T], agent, messages: "list[ChatMessageContent] | None"
+    ) -> list[AdditionalMessage] | None:
+        """Translate additional messages to the required format."""
+        if not messages:
+            return None
+        return cls._form_additional_messages(messages)
+
+    @classmethod
+    def _form_additional_messages(
+        cls: type[_T], messages: list["ChatMessageContent"]
+    ) -> list[AdditionalMessage] | None:
+        """Form the additional messages for the specified thread."""
+        if not messages:
+            return None
+
+        additional_messages = []
+        for message in messages:
+            if not message.content:
+                continue
+
+            message_with_all: AdditionalMessage = {
+                "content": message.content,
+                "role": "assistant" if message.role == AuthorRole.ASSISTANT else "user",
+                "attachments": cls._get_attachments(message) if message.items else None,
+                "metadata": cls._get_metadata(message) if message.metadata else None,
+            }
+            additional_messages.append(message_with_all)
+        return additional_messages
+
+    @classmethod
+    def _get_attachments(cls: type[_T], message: "ChatMessageContent") -> list[AdditionalMessageAttachment]:
+        return [
+            AdditionalMessageAttachment(
+                file_id=file_content.file_id,
+                tools=list(cls._get_tool_definition(file_content.tools)),  # type: ignore
+                data_source=file_content.data_source if file_content.data_source else None,
+            )
+            for file_content in message.items
+            if isinstance(file_content, (FileReferenceContent, StreamingFileReferenceContent))
+            and file_content.file_id is not None
+        ]
+
+    @classmethod
+    def _get_metadata(cls: type[_T], message: "ChatMessageContent") -> dict[str, str]:
+        """Get the metadata for an agent message."""
+        return {k: str(v) if v is not None else "" for k, v in (message.metadata or {}).items()}
+
+    @classmethod
+    def _get_tool_definition(cls: type[_T], tools: list[Any]) -> Iterable["AdditionalMessageAttachmentTool"]:
+        if not tools:
+            return
+        for tool in tools:
+            if tool_definition := cls.tool_metadata.get(tool):
+                yield from tool_definition
+
+    @staticmethod
+    def _validate_function_choice_behavior(
+        function_choice_behavior: FunctionChoiceBehavior | None,
+    ) -> None:
+        """Validate the function choice behavior is compatible with agent invocations."""
+        if function_choice_behavior is None:
+            return
+        if function_choice_behavior.type_ != FunctionChoiceType.AUTO:
+            raise AgentInvokeException(
+                f"FunctionChoiceBehavior with type '{function_choice_behavior.type_}' is not supported for agent "
+                "invocations. Use FunctionChoiceBehavior.Auto(filters=...) to control which kernel functions "
+                "are available."
+            )
+        if not function_choice_behavior.auto_invoke_kernel_functions:
+            raise AgentInvokeException(
+                "FunctionChoiceBehavior.Auto(auto_invoke=False) is not supported for agent invocations. "
+                "The agent run loop manages tool invocation; disabling auto_invoke is not compatible."
+            )
+        valid_filter_keys: set[str] = {
+            "excluded_plugins",
+            "included_plugins",
+            "excluded_functions",
+            "included_functions",
+        }
+        if function_choice_behavior.filters is not None:
+            if not function_choice_behavior.filters:
+                raise AgentInvokeException(
+                    "FunctionChoiceBehavior filters must not be empty. Provide at least one filter key "
+                    f"from {sorted(valid_filter_keys)}, or omit filters entirely to include all "
+                    "kernel functions."
+                )
+            unknown_keys = {str(k) for k in function_choice_behavior.filters} - valid_filter_keys
+            if unknown_keys:
+                raise AgentInvokeException(
+                    f"Unknown filter key(s): {sorted(unknown_keys)}. "
+                    f"Valid filter keys are: {sorted(valid_filter_keys)}."
+                )
+
+    @classmethod
+    def _get_tools(
+        cls: type[_T],
+        agent: "OpenAIAssistantAgent",
+        kernel: "Kernel",
+        tools_override: "list[AssistantToolParam] | None" = None,
+        function_choice_behavior: FunctionChoiceBehavior | None = None,
+    ) -> list[dict[str, str]]:
+        """Get the list of tools for the assistant.
+
+        Args:
+            agent: The assistant agent.
+            kernel: The kernel to use for function metadata.
+            tools_override: When provided, overrides agent.definition.tools (SDK-level tools only).
+            function_choice_behavior: When provided, filters which kernel functions are included.
+
+        Returns:
+            The list of tools.
+        """
+        tools: list[Any] = []
+
+        source_tools = tools_override if tools_override is not None else agent.definition.tools
+        for tool in source_tools:
+            if isinstance(tool, CodeInterpreterTool):
+                tools.append({"type": "code_interpreter"})
+            elif isinstance(tool, FileSearchTool):
+                tools.append({"type": "file_search"})
+
+        # Determine kernel function metadata based on function_choice_behavior
+        if function_choice_behavior is not None and not function_choice_behavior.enable_kernel_functions:
+            funcs = []
+        elif function_choice_behavior is not None and function_choice_behavior.filters:
+            funcs = kernel.get_list_of_function_metadata(function_choice_behavior.filters)
+        else:
+            funcs = kernel.get_full_list_of_function_metadata()
+
+        tools.extend([kernel_function_metadata_to_function_call_format(f) for f in funcs])
+
+        return tools

python/semantic_kernel/agents/open_ai/azure_assistant_agent.py

@@ -0,0 +1,286 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import sys
+from collections.abc import Awaitable, Callable
+from copy import copy
+from typing import TYPE_CHECKING, Any
+
+from azure.core.credentials import TokenCredential
+from openai import AsyncAzureOpenAI
+from pydantic import ValidationError
+
+from semantic_kernel.agents import OpenAIAssistantAgent
+from semantic_kernel.agents.agent import register_agent_type
+from semantic_kernel.connectors.ai.open_ai.settings.azure_open_ai_settings import AzureOpenAISettings
+from semantic_kernel.exceptions.agent_exceptions import AgentInitializationException
+from semantic_kernel.utils.authentication.entra_id_authentication import get_entra_auth_token
+from semantic_kernel.utils.feature_stage_decorator import release_candidate
+from semantic_kernel.utils.telemetry.user_agent import APP_INFO, prepend_semantic_kernel_to_user_agent
+
+if TYPE_CHECKING:
+    from semantic_kernel.kernel_pydantic import KernelBaseSettings
+
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+if sys.version < "3.11":
+    from typing_extensions import Self  # pragma: no cover
+else:
+    from typing import Self  # type: ignore # pragma: no cover
+
+if sys.version_info >= (3, 13):
+    from warnings import deprecated
+else:
+    from typing_extensions import deprecated
+
+
+@release_candidate
+@register_agent_type("azure_assistant")
+class AzureAssistantAgent(OpenAIAssistantAgent):
+    """An Azure Assistant Agent class that extends the OpenAI Assistant Agent class."""
+
+    @staticmethod
+    @deprecated(
+        "setup_resources is deprecated. Use AzureAssistantAgent.create_client() instead. This method will be removed by 2025-06-15."  # noqa: E501
+    )
+    def setup_resources(
+        *,
+        ad_token: str | None = None,
+        ad_token_provider: Callable[[], str | Awaitable[str]] | None = None,
+        api_key: str | None = None,
+        api_version: str | None = None,
+        base_url: str | None = None,
+        default_headers: dict[str, str] | None = None,
+        deployment_name: str | None = None,
+        endpoint: str | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+        token_scope: str | None = None,
+        credential: TokenCredential | None = None,
+        **kwargs: Any,
+    ) -> tuple[AsyncAzureOpenAI, str]:
+        """A method to create the Azure OpenAI client and the deployment name/model from the provided arguments.
+
+        Any arguments provided will override the values in the environment variables/environment file.
+
+        Args:
+            ad_token: The Microsoft Entra (previously Azure AD) token represented as a string
+            ad_token_provider: The Microsoft Entra (previously Azure AD) token provider provided as a callback
+            api_key: The API key
+            api_version: The API version
+            base_url: The base URL in the form https://<resource>.azure.openai.com/openai/deployments/<deployment_name>
+            default_headers: The default headers to add to the client
+            deployment_name: The deployment name
+            endpoint: The endpoint in the form https://<resource>.azure.openai.com
+            env_file_path: The environment file path
+            env_file_encoding: The environment file encoding, defaults to utf-8
+            token_scope: The token scope
+            credential: The credential to use for authentication.
+            kwargs: Additional keyword arguments
+
+        Returns:
+            An Azure OpenAI client instance and the configured deployment name (model)
+        """
+        try:
+            azure_openai_settings = AzureOpenAISettings(
+                api_key=api_key,
+                base_url=base_url,
+                endpoint=endpoint,
+                chat_deployment_name=deployment_name,
+                api_version=api_version,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+                token_endpoint=token_scope,
+            )
+        except ValidationError as exc:
+            raise AgentInitializationException(f"Failed to create Azure OpenAI settings: {exc}") from exc
+
+        if (
+            azure_openai_settings.api_key is None
+            and ad_token_provider is None
+            and ad_token is None
+            and azure_openai_settings.token_endpoint
+            and credential
+        ):
+            ad_token = get_entra_auth_token(credential, azure_openai_settings.token_endpoint)
+
+        # If we still have no credentials, we can't proceed
+        if not azure_openai_settings.api_key and not ad_token and not ad_token_provider and not credential:
+            raise AgentInitializationException(
+                "Please provide either an api_key, ad_token, ad_token_provider or credential for authentication."
+            )
+
+        merged_headers = dict(copy(default_headers)) if default_headers else {}
+        if default_headers:
+            merged_headers.update(default_headers)
+        if APP_INFO:
+            merged_headers.update(APP_INFO)
+            merged_headers = prepend_semantic_kernel_to_user_agent(merged_headers)
+
+        if not azure_openai_settings.endpoint:
+            raise AgentInitializationException("Please provide an Azure OpenAI endpoint")
+
+        if not azure_openai_settings.chat_deployment_name:
+            raise AgentInitializationException("Please provide an Azure OpenAI deployment name")
+
+        client = AsyncAzureOpenAI(
+            azure_endpoint=str(azure_openai_settings.endpoint),
+            api_version=azure_openai_settings.api_version,
+            api_key=azure_openai_settings.api_key.get_secret_value() if azure_openai_settings.api_key else None,
+            azure_ad_token=ad_token,
+            azure_ad_token_provider=ad_token_provider,
+            default_headers=merged_headers,
+            **kwargs,
+        )
+
+        return client, azure_openai_settings.chat_deployment_name
+
+    @staticmethod
+    def create_client(
+        *,
+        ad_token: str | None = None,
+        ad_token_provider: Callable[[], str | Awaitable[str]] | None = None,
+        api_key: str | None = None,
+        api_version: str | None = None,
+        base_url: str | None = None,
+        default_headers: dict[str, str] | None = None,
+        deployment_name: str | None = None,
+        endpoint: str | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+        token_scope: str | None = None,
+        credential: TokenCredential | None = None,
+        **kwargs: Any,
+    ) -> AsyncAzureOpenAI:
+        """A method to create the Azure OpenAI client.
+
+        Any arguments provided will override the values in the environment variables/environment file.
+
+        Args:
+            ad_token: The Microsoft Entra (previously Azure AD) token represented as a string
+            ad_token_provider: The Microsoft Entra (previously Azure AD) token provider provided as a callback
+            api_key: The API key
+            api_version: The API version
+            base_url: The base URL in the form https://<resource>.azure.openai.com/openai/deployments/<deployment_name>
+            default_headers: The default headers to add to the client
+            deployment_name: The deployment name
+            endpoint: The endpoint in the form https://<resource>.azure.openai.com
+            env_file_path: The environment file path
+            env_file_encoding: The environment file encoding, defaults to utf-8
+            token_scope: The token scope
+            credential: The credential to use for authentication.
+            kwargs: Additional keyword arguments
+
+        Returns:
+            An Azure OpenAI client instance.
+        """
+        try:
+            azure_openai_settings = AzureOpenAISettings(
+                api_key=api_key,
+                base_url=base_url,
+                endpoint=endpoint,
+                chat_deployment_name=deployment_name,
+                api_version=api_version,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+                token_endpoint=token_scope,
+            )
+        except ValidationError as exc:
+            raise AgentInitializationException(f"Failed to create Azure OpenAI settings: {exc}") from exc
+
+        if (
+            azure_openai_settings.api_key is None
+            and ad_token_provider is None
+            and ad_token is None
+            and azure_openai_settings.token_endpoint
+            and credential
+        ):
+            ad_token = get_entra_auth_token(credential, azure_openai_settings.token_endpoint)
+
+        # If we still have no credentials, we can't proceed
+        if not azure_openai_settings.api_key and not ad_token and not ad_token_provider and not credential:
+            raise AgentInitializationException(
+                "Please provide either an api_key, ad_token, ad_token_provider or credential for authentication."
+            )
+
+        merged_headers = dict(copy(default_headers)) if default_headers else {}
+        if default_headers:
+            merged_headers.update(default_headers)
+        if APP_INFO:
+            merged_headers.update(APP_INFO)
+            merged_headers = prepend_semantic_kernel_to_user_agent(merged_headers)
+
+        if not azure_openai_settings.endpoint:
+            raise AgentInitializationException("Please provide an Azure OpenAI endpoint")
+
+        if not azure_openai_settings.chat_deployment_name:
+            raise AgentInitializationException("Please provide an Azure OpenAI deployment name")
+
+        return AsyncAzureOpenAI(
+            azure_endpoint=str(azure_openai_settings.endpoint),
+            api_version=azure_openai_settings.api_version,
+            api_key=azure_openai_settings.api_key.get_secret_value() if azure_openai_settings.api_key else None,
+            azure_ad_token=ad_token,
+            azure_ad_token_provider=ad_token_provider,
+            default_headers=merged_headers,
+            **kwargs,
+        )
+
+    @override
+    @classmethod
+    def resolve_placeholders(
+        cls: type[Self],
+        yaml_str: str,
+        settings: "KernelBaseSettings | None" = None,
+        extras: dict[str, Any] | None = None,
+    ) -> str:
+        """Substitute ${AzureOpenAI:Key} placeholders with fields from AzureOpenAIAgentSettings and extras."""
+        import re
+
+        pattern = re.compile(r"\$\{([^}]+)\}")
+
+        # Build the mapping only if settings is provided and valid
+        field_mapping: dict[str, Any] = {}
+
+        if settings is None:
+            settings = AzureOpenAISettings()
+
+        if not isinstance(settings, AzureOpenAISettings):
+            raise AgentInitializationException(f"Expected AzureOpenAISettings, got {type(settings).__name__}")
+
+        field_mapping.update({
+            "ChatModelId": cls._get_setting(getattr(settings, "chat_deployment_name", None)),
+            "AgentId": cls._get_setting(getattr(settings, "agent_id", None)),
+            "ApiKey": cls._get_setting(getattr(settings, "api_key", None)),
+            "ApiVersion": cls._get_setting(getattr(settings, "api_version", None)),
+            "BaseUrl": cls._get_setting(getattr(settings, "base_url", None)),
+            "Endpoint": cls._get_setting(getattr(settings, "endpoint", None)),
+            "TokenEndpoint": cls._get_setting(getattr(settings, "token_endpoint", None)),
+        })
+
+        if extras:
+            field_mapping.update(extras)
+
+        def replacer(match: re.Match[str]) -> str:
+            """Replace the matched placeholder with the corresponding value from field_mapping."""
+            full_key = match.group(1)  # for example, OpenAI:ApiKey
+            section, _, key = full_key.partition(":")
+            if section != "AzureOpenAI":
+                return match.group(0)
+
+            # Try short key first (ApiKey), then full (OpenAI:ApiKey)
+            return str(field_mapping.get(key) or field_mapping.get(full_key) or match.group(0))
+
+        result = pattern.sub(replacer, yaml_str)
+
+        # Safety check for unresolved placeholders
+        unresolved = pattern.findall(result)
+        if unresolved:
+            raise AgentInitializationException(
+                f"Unresolved placeholders in spec: {', '.join(f'${{{key}}}' for key in unresolved)}"
+            )
+
+        return result

python/semantic_kernel/agents/open_ai/azure_responses_agent.py

@@ -0,0 +1,293 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+from collections.abc import Awaitable, Callable
+from copy import copy
+from typing import TYPE_CHECKING, Any
+
+from azure.core.credentials import TokenCredential
+from openai import AsyncAzureOpenAI
+from pydantic import ValidationError
+
+from semantic_kernel.agents import OpenAIResponsesAgent
+from semantic_kernel.agents.agent import register_agent_type
+from semantic_kernel.connectors.ai.open_ai.settings.azure_open_ai_settings import AzureOpenAISettings
+from semantic_kernel.exceptions.agent_exceptions import (
+    AgentInitializationException,
+)
+from semantic_kernel.utils.authentication.entra_id_authentication import get_entra_auth_token
+from semantic_kernel.utils.feature_stage_decorator import experimental
+from semantic_kernel.utils.telemetry.user_agent import APP_INFO, prepend_semantic_kernel_to_user_agent
+
+if TYPE_CHECKING:
+    from semantic_kernel.kernel_pydantic import KernelBaseSettings
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+if sys.version < "3.11":
+    from typing_extensions import Self  # pragma: no cover
+else:
+    from typing import Self  # type: ignore # pragma: no cover
+
+if sys.version_info >= (3, 13):
+    from warnings import deprecated
+else:
+    from typing_extensions import deprecated
+
+
+@experimental
+@register_agent_type("azure_responses")
+class AzureResponsesAgent(OpenAIResponsesAgent):
+    """Azure Responses Agent class.
+
+    Provides the ability to interact with Azure's Responses API.
+    """
+
+    @staticmethod
+    @deprecated(
+        "setup_resources is deprecated. Use AzureResponsesAgent.create_client() instead. This method will be removed by 2025-06-15."  # noqa: E501
+    )
+    def setup_resources(
+        *,
+        ad_token: str | None = None,
+        ad_token_provider: Callable[[], str | Awaitable[str]] | None = None,
+        api_key: str | None = None,
+        api_version: str | None = None,
+        base_url: str | None = None,
+        default_headers: dict[str, str] | None = None,
+        deployment_name: str | None = None,
+        endpoint: str | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+        token_scope: str | None = None,
+        credential: TokenCredential | None = None,
+        **kwargs: Any,
+    ) -> tuple[AsyncAzureOpenAI, str]:
+        """A method to create the Azure OpenAI client and the deployment name/model from the provided arguments.
+
+        Any arguments provided will override the values in the environment variables/environment file.
+
+        Args:
+            ad_token: The Microsoft Entra (previously Azure AD) token represented as a string
+            ad_token_provider: The Microsoft Entra (previously Azure AD) token provider provided as a callback
+            api_key: The API key
+            api_version: The API version
+            base_url: The base URL in the form https://<resource>.azure.openai.com/openai/deployments/<deployment_name>
+            default_headers: The default headers to add to the client
+            deployment_name: The Responses deployment name
+            endpoint: The endpoint in the form https://<resource>.azure.openai.com
+            env_file_path: The environment file path
+            env_file_encoding: The environment file encoding, defaults to utf-8
+            token_scope: The token scope
+            credential: The credential to use for authentication.
+            kwargs: Additional keyword arguments
+
+        Returns:
+            An Azure OpenAI client instance and the configured deployment name (model)
+        """
+        try:
+            azure_openai_settings = AzureOpenAISettings(
+                api_key=api_key,
+                base_url=base_url,
+                endpoint=endpoint,
+                responses_deployment_name=deployment_name,
+                api_version=api_version,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+                token_endpoint=token_scope,
+            )
+        except ValidationError as exc:
+            raise AgentInitializationException(f"Failed to create Azure OpenAI settings: {exc}") from exc
+
+        if (
+            azure_openai_settings.api_key is None
+            and ad_token_provider is None
+            and ad_token is None
+            and azure_openai_settings.token_endpoint
+            and credential
+        ):
+            ad_token = get_entra_auth_token(credential, azure_openai_settings.token_endpoint)
+
+        # If we still have no credentials, we can't proceed
+        if not azure_openai_settings.api_key and not ad_token and not ad_token_provider and not credential:
+            raise AgentInitializationException(
+                "Please provide either an api_key, ad_token, ad_token_provider or credential for authentication."
+            )
+
+        merged_headers = dict(copy(default_headers)) if default_headers else {}
+        if default_headers:
+            merged_headers.update(default_headers)
+        if APP_INFO:
+            merged_headers.update(APP_INFO)
+            merged_headers = prepend_semantic_kernel_to_user_agent(merged_headers)
+
+        if not azure_openai_settings.endpoint:
+            raise AgentInitializationException("Please provide an Azure OpenAI endpoint")
+
+        if not azure_openai_settings.responses_deployment_name:
+            raise AgentInitializationException("Please provide an Azure OpenAI Responses deployment name")
+
+        client = AsyncAzureOpenAI(
+            azure_endpoint=str(azure_openai_settings.endpoint),
+            api_version=azure_openai_settings.api_version,
+            api_key=azure_openai_settings.api_key.get_secret_value() if azure_openai_settings.api_key else None,
+            azure_ad_token=ad_token,
+            azure_ad_token_provider=ad_token_provider,
+            default_headers=merged_headers,
+            **kwargs,
+        )
+
+        return client, azure_openai_settings.responses_deployment_name
+
+    @staticmethod
+    def create_client(
+        *,
+        ad_token: str | None = None,
+        ad_token_provider: Callable[[], str | Awaitable[str]] | None = None,
+        api_key: str | None = None,
+        api_version: str | None = None,
+        base_url: str | None = None,
+        default_headers: dict[str, str] | None = None,
+        deployment_name: str | None = None,
+        endpoint: str | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+        token_scope: str | None = None,
+        credential: TokenCredential | None = None,
+        **kwargs: Any,
+    ) -> AsyncAzureOpenAI:
+        """A method to create the Azure OpenAI client.
+
+        Any arguments provided will override the values in the environment variables/environment file.
+
+        Args:
+            ad_token: The Microsoft Entra (previously Azure AD) token represented as a string
+            ad_token_provider: The Microsoft Entra (previously Azure AD) token provider provided as a callback
+            api_key: The API key
+            api_version: The API version
+            base_url: The base URL in the form https://<resource>.azure.openai.com/openai/deployments/<deployment_name>
+            default_headers: The default headers to add to the client
+            deployment_name: The Responses deployment name
+            endpoint: The endpoint in the form https://<resource>.azure.openai.com
+            env_file_path: The environment file path
+            env_file_encoding: The environment file encoding, defaults to utf-8
+            token_scope: The token scope
+            credential: The credential to use for authentication.
+            kwargs: Additional keyword arguments
+
+        Returns:
+            An Azure OpenAI client instance.
+        """
+        try:
+            azure_openai_settings = AzureOpenAISettings(
+                api_key=api_key,
+                base_url=base_url,
+                endpoint=endpoint,
+                responses_deployment_name=deployment_name,
+                api_version=api_version,
+                env_file_path=env_file_path,
+                env_file_encoding=env_file_encoding,
+                token_endpoint=token_scope,
+            )
+        except ValidationError as exc:
+            raise AgentInitializationException(f"Failed to create Azure OpenAI settings: {exc}") from exc
+
+        if (
+            azure_openai_settings.api_key is None
+            and ad_token_provider is None
+            and ad_token is None
+            and azure_openai_settings.token_endpoint
+            and credential
+        ):
+            ad_token = get_entra_auth_token(credential, azure_openai_settings.token_endpoint)
+
+        # If we still have no credentials, we can't proceed
+        if not azure_openai_settings.api_key and not ad_token and not ad_token_provider and not credential:
+            raise AgentInitializationException(
+                "Please provide either an api_key, ad_token, ad_token_provider or credential for authentication."
+            )
+
+        merged_headers = dict(copy(default_headers)) if default_headers else {}
+        if default_headers:
+            merged_headers.update(default_headers)
+        if APP_INFO:
+            merged_headers.update(APP_INFO)
+            merged_headers = prepend_semantic_kernel_to_user_agent(merged_headers)
+
+        if not azure_openai_settings.endpoint:
+            raise AgentInitializationException("Please provide an Azure OpenAI endpoint")
+
+        if not azure_openai_settings.responses_deployment_name:
+            raise AgentInitializationException("Please provide an Azure OpenAI Responses deployment name")
+
+        return AsyncAzureOpenAI(
+            azure_endpoint=str(azure_openai_settings.endpoint),
+            api_version=azure_openai_settings.api_version,
+            api_key=azure_openai_settings.api_key.get_secret_value() if azure_openai_settings.api_key else None,
+            azure_ad_token=ad_token,
+            azure_ad_token_provider=ad_token_provider,
+            default_headers=merged_headers,
+            **kwargs,
+        )
+
+    @override
+    @classmethod
+    def resolve_placeholders(
+        cls: type[Self],
+        yaml_str: str,
+        settings: "KernelBaseSettings | None" = None,
+        extras: dict[str, Any] | None = None,
+    ) -> str:
+        """Substitute ${AzureOpenAI:Key} placeholders with fields from AzureOpenAIAgentSettings and extras."""
+        import re
+
+        pattern = re.compile(r"\$\{([^}]+)\}")
+
+        # Build the mapping only if settings is provided and valid
+        field_mapping: dict[str, Any] = {}
+
+        if settings is None:
+            settings = AzureOpenAISettings()
+
+        if not isinstance(settings, AzureOpenAISettings):
+            raise AgentInitializationException(f"Expected AzureOpenAISettings, got {type(settings).__name__}")
+
+        field_mapping.update({
+            "ChatModelId": getattr(settings, "responses_deployment_name", None),
+            "AgentId": getattr(settings, "agent_id", None),
+            "ApiKey": getattr(settings, "api_key", None),
+            "ApiVersion": getattr(settings, "api_version", None),
+            "BaseUrl": getattr(settings, "base_url", None),
+            "Endpoint": getattr(settings, "endpoint", None),
+            "TokenEndpoint": getattr(settings, "token_endpoint", None),
+        })
+
+        if extras:
+            field_mapping.update(extras)
+
+        def replacer(match: re.Match[str]) -> str:
+            """Replace the matched placeholder with the corresponding value from field_mapping."""
+            full_key = match.group(1)  # for example, AzureOpenAI:ApiKey
+            section, _, key = full_key.partition(":")
+            if section != "AzureOpenAI":
+                return match.group(0)
+
+            # Try short key first (ApiKey), then full (AzureOpenAI:ApiKey)
+            return str(field_mapping.get(key) or field_mapping.get(full_key) or match.group(0))
+
+        result = pattern.sub(replacer, yaml_str)
+
+        # Safety check for unresolved placeholders
+        unresolved = pattern.findall(result)
+        if unresolved:
+            raise AgentInitializationException(
+                f"Unresolved placeholders in spec: {', '.join(f'${{{key}}}' for key in unresolved)}"
+            )
+
+        return result

python/semantic_kernel/agents/open_ai/function_action_result.py

@@ -0,0 +1,19 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+from dataclasses import dataclass
+
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+@dataclass
+class FunctionActionResult:
+    """Function Action Result."""
+
+    function_call_streaming_content: StreamingChatMessageContent
+    function_result_streaming_content: StreamingChatMessageContent
+    tool_outputs: list[dict[str, str]]

python/semantic_kernel/agents/open_ai/run_polling_options.py

@@ -0,0 +1,31 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from datetime import timedelta
+
+from pydantic import Field
+
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class RunPollingOptions(KernelBaseModel):
+    """Configuration and defaults associated with polling behavior for Assistant API requests."""
+
+    default_polling_interval: timedelta = Field(default=timedelta(milliseconds=250))
+    default_polling_backoff: timedelta = Field(default=timedelta(seconds=1))
+    default_polling_backoff_threshold: int = Field(default=2)
+    default_message_synchronization_delay: timedelta = Field(default=timedelta(milliseconds=250))
+    run_polling_interval: timedelta = Field(default=timedelta(milliseconds=250))
+    run_polling_backoff: timedelta = Field(default=timedelta(seconds=1))
+    run_polling_backoff_threshold: int = Field(default=2)
+    message_synchronization_delay: timedelta = Field(default=timedelta(milliseconds=250))
+    run_polling_timeout: timedelta = Field(default=timedelta(minutes=1))  # New timeout attribute
+
+    def get_polling_interval(self, iteration_count: int) -> timedelta:
+        """Get the polling interval for the given iteration count."""
+        return (
+            self.run_polling_backoff
+            if iteration_count > self.run_polling_backoff_threshold
+            else self.run_polling_interval
+        )

python/semantic_kernel/agents/orchestration/agent_actor_base.py

@@ -0,0 +1,241 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import inspect
+import logging
+import sys
+from collections.abc import Awaitable, Callable
+from functools import wraps
+from typing import Any
+
+from semantic_kernel.agents.agent import Agent, AgentThread
+from semantic_kernel.agents.orchestration.orchestration_base import DefaultTypeAlias
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.routed_agent import RoutedAgent
+from semantic_kernel.contents import ChatHistory, ChatMessageContent, StreamingChatMessageContent
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class ActorBase(RoutedAgent):
+    """A base class for actors running in the AgentRuntime."""
+
+    def __init__(
+        self,
+        description: str,
+        exception_callback: Callable[[BaseException], None],
+    ):
+        """Initialize the actor with a description and an exception callback.
+
+        Args:
+            description (str): A description of the actor.
+            exception_callback (Callable[[BaseException], None]): A callback function to handle exceptions.
+        """
+        super().__init__(description=description)
+        self._exception_callback = exception_callback
+
+    @override
+    async def on_message_impl(self, message: Any, ctx: MessageContext) -> Any | None:
+        """Handle a message.
+
+        Stop the handling of the message if the cancellation token is cancelled.
+        """
+        if ctx.cancellation_token.is_cancelled():
+            return None
+
+        return await super().on_message_impl(message, ctx)
+
+    @staticmethod
+    def exception_handler(func: Callable[..., Any]) -> Callable[..., Any]:
+        """Decorator that wraps a function in a try-catch block and calls the exception callback on errors.
+
+        This decorator can be used on both synchronous and asynchronous functions. When an exception
+        occurs during function execution, it will call the exception_callback with the exception
+        and then re-raise the exception.
+
+        Args:
+            func: The function to be wrapped.
+
+        Returns:
+            The wrapped function.
+        """
+        log_message_template = "Exception occurred in agent {agent_id}:\n{exception}"
+
+        if inspect.iscoroutinefunction(func):
+
+            @wraps(func)
+            async def async_wrapper(self, *args, **kwargs):
+                try:
+                    return await func(self, *args, **kwargs)
+                except BaseException as e:
+                    self._exception_callback(e)
+                    logger.error(log_message_template.format(agent_id=self.id, exception=e))
+                    raise
+
+            return async_wrapper
+
+        @wraps(func)
+        def sync_wrapper(self, *args, **kwargs):
+            try:
+                return func(self, *args, **kwargs)
+            except BaseException as e:
+                self._exception_callback(e)
+                logger.error(log_message_template.format(agent_id=self.id, exception=e))
+                raise
+
+        return sync_wrapper
+
+
+@experimental
+class AgentActorBase(ActorBase):
+    """A agent actor for multi-agent orchestration running on Agent runtime."""
+
+    def __init__(
+        self,
+        agent: Agent,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+    ) -> None:
+        """Initialize the agent container.
+
+        Args:
+            agent (Agent): An agent to be run in the container.
+            internal_topic_type (str): The topic type of the internal topic.
+            exception_callback (Callable): A function that is called when an exception occurs.
+            agent_response_callback (Callable | None): A function that is called when a full response is produced
+                by the agents.
+            streaming_agent_response_callback (Callable | None): A function that is called when a streaming response
+                is produced by the agents.
+        """
+        self._agent = agent
+        self._internal_topic_type = internal_topic_type
+        self._agent_response_callback = agent_response_callback
+        self._streaming_agent_response_callback = streaming_agent_response_callback
+
+        self._agent_thread: AgentThread | None = None
+        # Chat history to temporarily store messages before each invoke.
+        self._message_cache: ChatHistory = ChatHistory()
+
+        super().__init__(agent.description or "Semantic Kernel Actor", exception_callback)
+
+    async def _call_agent_response_callback(self, message: DefaultTypeAlias) -> None:
+        """Call the agent_response_callback function if it is set.
+
+        Args:
+            message (DefaultTypeAlias): The message to be sent to the agent_response_callback.
+        """
+        if self._agent_response_callback:
+            if inspect.iscoroutinefunction(self._agent_response_callback):
+                await self._agent_response_callback(message)
+            else:
+                self._agent_response_callback(message)
+
+    async def _call_streaming_agent_response_callback(
+        self,
+        message_chunk: StreamingChatMessageContent,
+        is_final: bool,
+    ) -> None:
+        """Call the streaming_agent_response_callback function if it is set.
+
+        Args:
+            message_chunk (StreamingChatMessageContent): The message chunk.
+            is_final (bool): Whether this is the final chunk of the response.
+        """
+        if self._streaming_agent_response_callback:
+            if inspect.iscoroutinefunction(self._streaming_agent_response_callback):
+                await self._streaming_agent_response_callback(message_chunk, is_final)
+            else:
+                self._streaming_agent_response_callback(message_chunk, is_final)
+
+    @ActorBase.exception_handler
+    async def _invoke_agent(self, additional_messages: DefaultTypeAlias | None = None, **kwargs) -> ChatMessageContent:
+        """Invoke the agent with the current chat history or thread and optionally additional messages.
+
+        Args:
+            additional_messages (DefaultTypeAlias | None): Additional messages to be sent to the agent.
+            **kwargs: Additional keyword arguments to be passed to the agent's invoke method:
+                - kernel: The kernel to use for the agent invocation.
+
+        Returns:
+            DefaultTypeAlias: The response from the agent.
+        """
+        streaming_message_buffer: list[StreamingChatMessageContent] = []
+        messages = self._create_messages(additional_messages)
+
+        async for response_item in self._agent.invoke_stream(
+            messages,  # type: ignore[arg-type]
+            thread=self._agent_thread,
+            on_intermediate_message=self._handle_intermediate_message,
+            **kwargs,
+        ):
+            # Buffer message chunks and stream them with correct is_final flag.
+            streaming_message_buffer.append(response_item.message)
+            if len(streaming_message_buffer) > 1:
+                await self._call_streaming_agent_response_callback(streaming_message_buffer[-2], is_final=False)
+            if self._agent_thread is None:
+                self._agent_thread = response_item.thread
+
+        if streaming_message_buffer:
+            # Call the callback for the last message chunk with is_final=True.
+            await self._call_streaming_agent_response_callback(streaming_message_buffer[-1], is_final=True)
+
+        if not streaming_message_buffer:
+            raise RuntimeError(f'Agent "{self._agent.name}" did not return any response.')
+
+        # Build the full response from the streaming messages
+        full_response = sum(streaming_message_buffer[1:], streaming_message_buffer[0])
+        await self._call_agent_response_callback(full_response)
+
+        return full_response
+
+    def _create_messages(self, additional_messages: DefaultTypeAlias | None = None) -> list[ChatMessageContent]:
+        """Create a list of messages to be sent to the agent along with a potential thread.
+
+        Args:
+            additional_messages (DefaultTypeAlias | None): Additional messages to be sent to the agent.
+
+        Returns:
+            list[ChatMessageContent]: A list of messages to be sent to the agent.
+        """
+        base_messages = self._message_cache.messages[:]
+
+        # Clear the message cache for the next invoke.
+        self._message_cache.clear()
+
+        if additional_messages is None:
+            return base_messages
+
+        if isinstance(additional_messages, list):
+            return base_messages + additional_messages
+        return [*base_messages, additional_messages]
+
+    async def _handle_intermediate_message(self, message: ChatMessageContent) -> None:
+        """Handle intermediate messages from the agent."""
+        await self._call_agent_response_callback(message)
+        if isinstance(message, StreamingChatMessageContent):
+            await self._call_streaming_agent_response_callback(message, is_final=True)
+        else:
+            # Convert to StreamingChatMessageContent if needed
+            streaming_message = StreamingChatMessageContent(  # type: ignore[misc, call-overload]
+                role=message.role,
+                choice_index=0,
+                items=message.items,
+                content=message.content,
+                name=message.name,
+                inner_content=message.inner_content,
+                encoding=message.encoding,
+                finish_reason=message.finish_reason,
+                ai_model_id=message.ai_model_id,
+                metadata=message.metadata,
+            )
+            await self._call_streaming_agent_response_callback(streaming_message, is_final=True)

python/semantic_kernel/agents/orchestration/concurrent.py

@@ -0,0 +1,247 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+import sys
+from collections.abc import Awaitable, Callable
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.orchestration.agent_actor_base import ActorBase, AgentActorBase
+from semantic_kernel.agents.orchestration.orchestration_base import (
+    ChatMessageContent,
+    DefaultTypeAlias,
+    OrchestrationBase,
+    TIn,
+    TOut,
+)
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.routed_agent import message_handler
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.agents.runtime.in_process.type_subscription import TypeSubscription
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class ConcurrentRequestMessage(KernelBaseModel):
+    """A request message type for concurrent agents."""
+
+    body: DefaultTypeAlias
+
+
+@experimental
+class ConcurrentResponseMessage(KernelBaseModel):
+    """A response message type for concurrent agents."""
+
+    body: ChatMessageContent
+
+
+@experimental
+class ConcurrentAgentActor(AgentActorBase):
+    """A agent actor for concurrent agents that process tasks."""
+
+    def __init__(
+        self,
+        agent: Agent,
+        internal_topic_type: str,
+        collection_agent_type: str,
+        exception_callback: Callable[[BaseException], None],
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+    ) -> None:
+        """Initialize the agent actor.
+
+        Args:
+            agent: The agent to be executed.
+            internal_topic_type: The internal topic type for the actor.
+            collection_agent_type: The collection agent type for the actor.
+            exception_callback: A callback function to handle exceptions.
+            agent_response_callback: A callback function to handle the full response from the agent.
+            streaming_agent_response_callback: A callback function to handle streaming responses from the agent.
+        """
+        self._collection_agent_type = collection_agent_type
+        super().__init__(
+            agent=agent,
+            internal_topic_type=internal_topic_type,
+            exception_callback=exception_callback,
+            agent_response_callback=agent_response_callback,
+            streaming_agent_response_callback=streaming_agent_response_callback,
+        )
+
+    @message_handler
+    async def _handle_message(self, message: ConcurrentRequestMessage, ctx: MessageContext) -> None:
+        """Handle a message."""
+        logger.debug(f"Concurrent actor (Actor ID: {self.id}; Agent name: {self._agent.name}) started processing...")
+
+        response = await self._invoke_agent(additional_messages=message.body)
+
+        logger.debug(f"Concurrent actor (Actor ID: {self.id}; Agent name: {self._agent.name}) finished processing.")
+
+        target_actor_id = await self.runtime.get(self._collection_agent_type)
+        await self.send_message(
+            ConcurrentResponseMessage(body=response),
+            target_actor_id,
+            cancellation_token=ctx.cancellation_token,
+        )
+
+
+@experimental
+class CollectionActor(ActorBase):
+    """A agent container for collecting results from concurrent agents."""
+
+    def __init__(
+        self,
+        description: str,
+        expected_answer_count: int,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+    ) -> None:
+        """Initialize the collection agent container."""
+        self._expected_answer_count = expected_answer_count
+        self._result_callback = result_callback
+        self._results: list[ChatMessageContent] = []
+        self._lock = asyncio.Lock()
+
+        super().__init__(description, exception_callback)
+
+    @message_handler
+    async def _handle_message(self, message: ConcurrentResponseMessage, _: MessageContext) -> None:
+        async with self._lock:
+            self._results.append(message.body)
+
+        if len(self._results) == self._expected_answer_count:
+            logger.debug(f"Collection actor (Actor ID: {self.id}) finished processing all responses.")
+            if self._result_callback:
+                await self._result_callback(self._results)
+
+
+@experimental
+class ConcurrentOrchestration(OrchestrationBase[TIn, TOut]):
+    """A concurrent multi-agent pattern orchestration."""
+
+    @override
+    async def _start(
+        self,
+        task: DefaultTypeAlias,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        cancellation_token: CancellationToken,
+    ) -> None:
+        """Start the concurrent pattern."""
+        await runtime.publish_message(
+            ConcurrentRequestMessage(body=task),
+            TopicId(internal_topic_type, self.__class__.__name__),
+            cancellation_token=cancellation_token,
+        )
+
+    @override
+    async def _prepare(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Register the actors and orchestrations with the runtime and add the required subscriptions."""
+        await asyncio.gather(*[
+            self._register_members(
+                runtime,
+                internal_topic_type,
+                exception_callback,
+            ),
+            self._register_collection_actor(
+                runtime,
+                internal_topic_type,
+                exception_callback,
+                result_callback=result_callback,
+            ),
+            self._add_subscriptions(
+                runtime,
+                internal_topic_type,
+            ),
+        ])
+
+    async def _register_members(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+    ) -> None:
+        """Register the members."""
+
+        async def _internal_helper(agent: Agent) -> None:
+            await ConcurrentAgentActor.register(
+                runtime,
+                self._get_agent_actor_type(agent, internal_topic_type),
+                lambda agent=agent: ConcurrentAgentActor(  # type: ignore[misc]
+                    agent,
+                    internal_topic_type,
+                    self._get_collection_actor_type(internal_topic_type),
+                    exception_callback,
+                    agent_response_callback=self._agent_response_callback,
+                    streaming_agent_response_callback=self._streaming_agent_response_callback,
+                ),
+            )
+
+        await asyncio.gather(*[_internal_helper(agent) for agent in self._members])
+
+    async def _register_collection_actor(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+    ) -> None:
+        await CollectionActor.register(
+            runtime,
+            self._get_collection_actor_type(internal_topic_type),
+            lambda: CollectionActor(
+                description="An internal agent that is responsible for collection results",
+                expected_answer_count=len(self._members),
+                exception_callback=exception_callback,
+                result_callback=result_callback,
+            ),
+        )
+
+    async def _add_subscriptions(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+    ) -> None:
+        await asyncio.gather(*[
+            runtime.add_subscription(
+                TypeSubscription(
+                    internal_topic_type,
+                    self._get_agent_actor_type(agent, internal_topic_type),
+                )
+            )
+            for agent in self._members
+        ])
+
+    def _get_agent_actor_type(self, worker: Agent, internal_topic_type: str) -> str:
+        """Get the container type for an agent.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{worker.name}_{internal_topic_type}"
+
+    def _get_collection_actor_type(self, internal_topic_type: str) -> str:
+        """Get the collection agent type.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{CollectionActor.__name__}_{internal_topic_type}"

python/semantic_kernel/agents/orchestration/group_chat.py

@@ -0,0 +1,530 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import inspect
+import logging
+import sys
+from abc import ABC, abstractmethod
+from collections.abc import Awaitable, Callable
+from typing import Generic, TypeVar
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.orchestration.agent_actor_base import ActorBase, AgentActorBase
+from semantic_kernel.agents.orchestration.orchestration_base import DefaultTypeAlias, OrchestrationBase, TIn, TOut
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.routed_agent import message_handler
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.agents.runtime.in_process.type_subscription import TypeSubscription
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+# region Messages and Types
+
+
+@experimental
+class GroupChatStartMessage(KernelBaseModel):
+    """A message type to start a group chat."""
+
+    body: DefaultTypeAlias
+
+
+@experimental
+class GroupChatRequestMessage(KernelBaseModel):
+    """A request message type for agents in a group chat."""
+
+    agent_name: str
+
+
+@experimental
+class GroupChatResponseMessage(KernelBaseModel):
+    """A response message type from agents in a group chat."""
+
+    body: ChatMessageContent
+
+
+_TGroupChatManagerResult = TypeVar("_TGroupChatManagerResult", ChatMessageContent, str, bool)
+
+
+@experimental
+class GroupChatManagerResult(KernelBaseModel, Generic[_TGroupChatManagerResult]):
+    """A result message type from the group chat manager."""
+
+    result: _TGroupChatManagerResult
+    reason: str
+
+
+# Subclassing GroupChatManagerResult to create specific result types because
+# we need to change the names of the classes to remove the generic type parameters.
+# Many model services (e.g. OpenAI) do not support generic type parameters in the
+# class name (e.g. "GroupChatManagerResult[bool]").
+@experimental
+class BooleanResult(GroupChatManagerResult[bool]):
+    """A result message type from the group chat manager with a boolean result."""
+
+    pass
+
+
+@experimental
+class StringResult(GroupChatManagerResult[str]):
+    """A result message type from the group chat manager with a string result."""
+
+    pass
+
+
+@experimental
+class MessageResult(GroupChatManagerResult[ChatMessageContent]):
+    """A result message type from the group chat manager with a message result."""
+
+    pass
+
+
+# endregion Messages and Types
+
+# region GroupChatAgentActor
+
+
+@experimental
+class GroupChatAgentActor(AgentActorBase):
+    """An agent actor that process messages in a group chat."""
+
+    @message_handler
+    async def _handle_start_message(self, message: GroupChatStartMessage, ctx: MessageContext) -> None:
+        """Handle the start message for the group chat."""
+        logger.debug(f"{self.id}: Received group chat start message.")
+        if isinstance(message.body, ChatMessageContent):
+            self._message_cache.add_message(message.body)
+        elif isinstance(message.body, list) and all(isinstance(m, ChatMessageContent) for m in message.body):
+            for m in message.body:
+                self._message_cache.add_message(m)
+        else:
+            raise ValueError(f"Invalid message body type: {type(message.body)}. Expected {DefaultTypeAlias}.")
+
+    @message_handler
+    async def _handle_response_message(self, message: GroupChatResponseMessage, ctx: MessageContext) -> None:
+        logger.debug(f"{self.id}: Received group chat response message.")
+        self._message_cache.add_message(message.body)
+
+    @message_handler
+    async def _handle_request_message(self, message: GroupChatRequestMessage, ctx: MessageContext) -> None:
+        if message.agent_name != self._agent.name:
+            return
+
+        logger.debug(f"{self.id}: Received group chat request message.")
+
+        response = await self._invoke_agent()
+
+        logger.debug(f"{self.id} responded with {response}.")
+
+        await self.publish_message(
+            GroupChatResponseMessage(body=response),
+            TopicId(self._internal_topic_type, self.id.key),
+            cancellation_token=ctx.cancellation_token,
+        )
+
+
+# endregion GroupChatAgentActor
+
+
+# region GroupChatManager
+
+
+@experimental
+class GroupChatManager(KernelBaseModel, ABC):
+    """A group chat manager that manages the flow of a group chat."""
+
+    current_round: int = 0
+    max_rounds: int | None = None
+
+    human_response_function: Callable[[ChatHistory], Awaitable[ChatMessageContent] | ChatMessageContent] | None = None
+
+    @abstractmethod
+    async def should_request_user_input(self, chat_history: ChatHistory) -> BooleanResult:
+        """Check if the group chat should request user input.
+
+        Args:
+            chat_history (ChatHistory): The chat history of the group chat.
+        """
+        ...
+
+    async def should_terminate(self, chat_history: ChatHistory) -> BooleanResult:
+        """Check if the group chat should terminate.
+
+        Args:
+            chat_history (ChatHistory): The chat history of the group chat.
+        """
+        self.current_round += 1
+
+        if self.max_rounds is not None:
+            return BooleanResult(
+                result=self.current_round > self.max_rounds,
+                reason="Maximum rounds reached."
+                if self.current_round > self.max_rounds
+                else "Not reached maximum rounds.",
+            )
+        return BooleanResult(result=False, reason="No maximum rounds set.")
+
+    @abstractmethod
+    async def select_next_agent(
+        self,
+        chat_history: ChatHistory,
+        participant_descriptions: dict[str, str],
+    ) -> StringResult:
+        """Select the next agent to speak.
+
+        Args:
+            chat_history (ChatHistory): The chat history of the group chat.
+            participant_descriptions (dict[str, str]): The descriptions of the participants in the group chat.
+        """
+        ...
+
+    @abstractmethod
+    async def filter_results(
+        self,
+        chat_history: ChatHistory,
+    ) -> MessageResult:
+        """Filter the results of the group chat.
+
+        Args:
+            chat_history (ChatHistory): The chat history of the group chat.
+            participant_descriptions (dict[str, str]): The descriptions of the participants in the group chat.
+        """
+        ...
+
+
+@experimental
+class RoundRobinGroupChatManager(GroupChatManager):
+    """A round-robin group chat manager."""
+
+    current_index: int = 0
+
+    @override
+    async def should_request_user_input(self, chat_history: ChatHistory) -> BooleanResult:
+        """Check if the group chat should request user input."""
+        return BooleanResult(
+            result=False,
+            reason="The default round-robin group chat manager does not request user input.",
+        )
+
+    @override
+    async def select_next_agent(
+        self,
+        chat_history: ChatHistory,
+        participant_descriptions: dict[str, str],
+    ) -> StringResult:
+        """Select the next agent to speak."""
+        next_agent = list(participant_descriptions.keys())[self.current_index]
+        self.current_index = (self.current_index + 1) % len(participant_descriptions)
+        return StringResult(result=next_agent, reason="Round-robin selection.")
+
+    @override
+    async def filter_results(
+        self,
+        chat_history: ChatHistory,
+    ) -> MessageResult:
+        """Filter the results of the group chat."""
+        return MessageResult(
+            result=chat_history.messages[-1],
+            reason="The last message in the chat history is the result in the default round-robin group chat manager.",
+        )
+
+
+# endregion GroupChatManager
+
+# region GroupChatManagerActor
+
+
+@experimental
+class GroupChatManagerActor(ActorBase):
+    """A group chat manager actor."""
+
+    def __init__(
+        self,
+        manager: GroupChatManager,
+        internal_topic_type: str,
+        participant_descriptions: dict[str, str],
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+    ):
+        """Initialize the group chat manager actor.
+
+        Args:
+            manager (GroupChatManager): The group chat manager that manages the flow of the group chat.
+            internal_topic_type (str): The topic type of the internal topic.
+            participant_descriptions (dict[str, str]): The descriptions of the participants in the group chat.
+            exception_callback (Callable[[BaseException], None]): A function that is called when an exception occurs.
+            result_callback (Callable | None): A function that is called when the group chat manager produces a result.
+        """
+        self._manager = manager
+        self._internal_topic_type = internal_topic_type
+        self._chat_history = ChatHistory()
+        self._participant_descriptions = participant_descriptions
+        self._result_callback = result_callback
+
+        super().__init__("An actor for the group chat manager.", exception_callback)
+
+    @message_handler
+    async def _handle_start_message(self, message: GroupChatStartMessage, ctx: MessageContext) -> None:
+        """Handle the start message for the group chat."""
+        logger.debug(f"{self.id}: Received group chat start message.")
+        if isinstance(message.body, ChatMessageContent):
+            self._chat_history.add_message(message.body)
+        elif isinstance(message.body, list) and all(isinstance(m, ChatMessageContent) for m in message.body):
+            for m in message.body:
+                self._chat_history.add_message(m)
+        else:
+            raise ValueError(f"Invalid message body type: {type(message.body)}. Expected {DefaultTypeAlias}.")
+
+        await self._determine_state_and_take_action(ctx.cancellation_token)
+
+    @message_handler
+    async def _handle_response_message(self, message: GroupChatResponseMessage, ctx: MessageContext) -> None:
+        if message.body.role != AuthorRole.USER:
+            self._chat_history.add_message(
+                ChatMessageContent(
+                    role=AuthorRole.USER,
+                    content=f"Transferred to {message.body.name}",
+                )
+            )
+        self._chat_history.add_message(message.body)
+
+        await self._determine_state_and_take_action(ctx.cancellation_token)
+
+    @ActorBase.exception_handler
+    async def _determine_state_and_take_action(self, cancellation_token: CancellationToken) -> None:
+        """Determine the state of the group chat and take action accordingly."""
+        # User input state
+        should_request_user_input = await self._manager.should_request_user_input(
+            self._chat_history.model_copy(deep=True)
+        )
+        if should_request_user_input.result and self._manager.human_response_function:
+            logger.debug(f"Group chat manager requested user input. Reason: {should_request_user_input.reason}")
+            user_input_message = await self._call_human_response_function()
+            self._chat_history.add_message(user_input_message)
+            await self.publish_message(
+                GroupChatResponseMessage(body=user_input_message),
+                TopicId(self._internal_topic_type, self.id.key),
+                cancellation_token=cancellation_token,
+            )
+            logger.debug("User input received and added to chat history.")
+
+        # Determine if the group chat should terminate
+        should_terminate = await self._manager.should_terminate(self._chat_history.model_copy(deep=True))
+        if should_terminate.result:
+            logger.debug(f"Group chat manager decided to terminate the group chat. Reason: {should_terminate.reason}")
+            if self._result_callback:
+                result = await self._manager.filter_results(self._chat_history.model_copy(deep=True))
+                result.result.metadata["termination_reason"] = should_terminate.reason
+                result.result.metadata["filter_result_reason"] = result.reason
+                await self._result_callback(result.result)
+            return
+
+        # Select the next agent to speak if the group chat is not terminating
+        next_agent = await self._manager.select_next_agent(
+            self._chat_history.model_copy(deep=True),
+            self._participant_descriptions,
+        )
+        logger.debug(
+            f"Group chat manager selected agent: {next_agent.result} on round {self._manager.current_round}. "
+            f"Reason: {next_agent.reason}"
+        )
+
+        await self.publish_message(
+            GroupChatRequestMessage(agent_name=next_agent.result),
+            TopicId(self._internal_topic_type, self.id.key),
+            cancellation_token=cancellation_token,
+        )
+
+    async def _call_human_response_function(self) -> ChatMessageContent:
+        """Call the human response function if it is set."""
+        assert self._manager.human_response_function  # nosec B101
+        if inspect.iscoroutinefunction(self._manager.human_response_function):
+            return await self._manager.human_response_function(self._chat_history.model_copy(deep=True))
+        return self._manager.human_response_function(self._chat_history.model_copy(deep=True))  # type: ignore[return-value]
+
+
+# endregion GroupChatManagerActor
+
+# region GroupChatOrchestration
+
+
+@experimental
+class GroupChatOrchestration(OrchestrationBase[TIn, TOut]):
+    """A group chat multi-agent pattern orchestration."""
+
+    def __init__(
+        self,
+        members: list[Agent],
+        manager: GroupChatManager,
+        name: str | None = None,
+        description: str | None = None,
+        input_transform: Callable[[TIn], Awaitable[DefaultTypeAlias] | DefaultTypeAlias] | None = None,
+        output_transform: Callable[[DefaultTypeAlias], Awaitable[TOut] | TOut] | None = None,
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+    ) -> None:
+        """Initialize the group chat orchestration.
+
+        Args:
+            members (list[Agent | OrchestrationBase]): A list of agents or orchestrations that are part of the
+                handoff group. This first agent in the list will be the one that receives the first message.
+            manager (GroupChatManager): The group chat manager that manages the flow of the group chat.
+            name (str | None): The name of the orchestration.
+            description (str | None): The description of the orchestration.
+            input_transform (Callable | None): A function that transforms the external input message.
+            output_transform (Callable | None): A function that transforms the internal output message.
+            agent_response_callback (Callable | None): A function that is called when a full response is produced
+                by the agents.
+            streaming_agent_response_callback (Callable | None): A function that is called when a streaming response
+                is produced by the agents.
+        """
+        self._manager = manager
+
+        for member in members:
+            if member.description is None:
+                raise ValueError("All members must have a description.")
+
+        super().__init__(
+            members=members,
+            name=name,
+            description=description,
+            input_transform=input_transform,
+            output_transform=output_transform,
+            agent_response_callback=agent_response_callback,
+            streaming_agent_response_callback=streaming_agent_response_callback,
+        )
+
+    @override
+    async def _start(
+        self,
+        task: DefaultTypeAlias,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        cancellation_token: CancellationToken,
+    ) -> None:
+        """Start the group chat process.
+
+        This ensures that all initial messages are sent to the individual actors
+        and processed before the group chat begins. It's important because if the
+        manager actor processes its start message too quickly (or other actors are
+        too slow), it might send a request to the next agent before the other actors
+        have the necessary context.
+        """
+
+        async def send_start_message(agent: Agent) -> None:
+            target_actor_id = await runtime.get(self._get_agent_actor_type(agent, internal_topic_type))
+            await runtime.send_message(
+                GroupChatStartMessage(body=task),
+                target_actor_id,
+                cancellation_token=cancellation_token,
+            )
+
+        await asyncio.gather(*[send_start_message(agent) for agent in self._members])
+
+        # Send the start message to the manager actor
+        target_actor_id = await runtime.get(self._get_manager_actor_type(internal_topic_type))
+        await runtime.send_message(
+            GroupChatStartMessage(body=task),
+            target_actor_id,
+            cancellation_token=cancellation_token,
+        )
+
+    @override
+    async def _prepare(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Register the actors and orchestrations with the runtime and add the required subscriptions."""
+        await self._register_members(runtime, internal_topic_type, exception_callback)
+        await self._register_manager(runtime, internal_topic_type, exception_callback, result_callback)
+        await self._add_subscriptions(runtime, internal_topic_type)
+
+    async def _register_members(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+    ) -> None:
+        """Register the agents."""
+        await asyncio.gather(*[
+            GroupChatAgentActor.register(
+                runtime,
+                self._get_agent_actor_type(agent, internal_topic_type),
+                lambda agent=agent: GroupChatAgentActor(  # type: ignore[misc]
+                    agent,
+                    internal_topic_type,
+                    exception_callback=exception_callback,
+                    agent_response_callback=self._agent_response_callback,
+                    streaming_agent_response_callback=self._streaming_agent_response_callback,
+                ),
+            )
+            for agent in self._members
+        ])
+
+    async def _register_manager(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+    ) -> None:
+        """Register the group chat manager."""
+        await GroupChatManagerActor.register(
+            runtime,
+            self._get_manager_actor_type(internal_topic_type),
+            lambda: GroupChatManagerActor(
+                self._manager,
+                internal_topic_type=internal_topic_type,
+                participant_descriptions={agent.name: agent.description for agent in self._members},  # type: ignore[misc]
+                exception_callback=exception_callback,
+                result_callback=result_callback,
+            ),
+        )
+
+    async def _add_subscriptions(self, runtime: CoreRuntime, internal_topic_type: str) -> None:
+        """Add subscriptions."""
+        subscriptions: list[TypeSubscription] = []
+        for agent in self._members:
+            subscriptions.append(
+                TypeSubscription(internal_topic_type, self._get_agent_actor_type(agent, internal_topic_type))
+            )
+        subscriptions.append(TypeSubscription(internal_topic_type, self._get_manager_actor_type(internal_topic_type)))
+
+        await asyncio.gather(*[runtime.add_subscription(sub) for sub in subscriptions])
+
+    def _get_agent_actor_type(self, agent: Agent, internal_topic_type: str) -> str:
+        """Get the actor type for an agent.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{agent.name}_{internal_topic_type}"
+
+    def _get_manager_actor_type(self, internal_topic_type: str) -> str:
+        """Get the actor type for the group chat manager.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{GroupChatManagerActor.__name__}_{internal_topic_type}"
+
+
+# endregion GroupChatOrchestration

python/semantic_kernel/agents/orchestration/handoffs.py

@@ -0,0 +1,530 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import asyncio
+import inspect
+import logging
+import sys
+from collections.abc import Awaitable, Callable
+from functools import partial
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.orchestration.agent_actor_base import ActorBase, AgentActorBase
+from semantic_kernel.agents.orchestration.orchestration_base import DefaultTypeAlias, OrchestrationBase, TIn, TOut
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.routed_agent import message_handler
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.agents.runtime.in_process.type_subscription import TypeSubscription
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.filters.auto_function_invocation.auto_function_invocation_context import (
+    AutoFunctionInvocationContext,
+)
+from semantic_kernel.filters.filter_types import FilterTypes
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
+from semantic_kernel.functions.kernel_function_from_method import KernelFunctionFromMethod
+from semantic_kernel.functions.kernel_function_metadata import KernelFunctionMetadata
+from semantic_kernel.functions.kernel_parameter_metadata import KernelParameterMetadata
+from semantic_kernel.functions.kernel_plugin import KernelPlugin
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+if sys.version_info >= (3, 11):
+    from typing import Self  # pragma: no cover
+else:
+    from typing_extensions import Self  # pragma: no cover
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+# region Messages and Types
+
+
+# A type alias for a mapping of agent names to their descriptions
+# of the possible handoff connections for an agent.
+AgentHandoffs = dict[str, str]
+
+
+@experimental
+class OrchestrationHandoffs(dict[str, AgentHandoffs]):
+    """A dictionary mapping agent names to their handoff connections.
+
+    Handoff connections are represented as a dictionary where the key is the target agent name
+    and the value is a description of the handoff connection. For example:
+    {
+        "AgentA": {
+            "AgentB": "Transfer to Agent B for further assistance.",
+            "AgentC": "Transfer to Agent C for technical support."
+        },
+        "AgentB": {
+            "AgentA": "Transfer to Agent A for general inquiries.",
+            "AgentC": "Transfer to Agent C for billing issues."
+        }
+    }
+
+    This class allows for easy addition of handoff connections between agents.
+    """
+
+    def add(self, source_agent: str | Agent, target_agent: str | Agent, description: str | None = None) -> "Self":
+        """Add a handoff connection to the source agent.
+
+        Args:
+            source_agent (str | Agent): The source agent name or instance.
+            target_agent (str | Agent): The target agent name or instance.
+            description (str | None): The description of the handoff connection.
+
+        Returns:
+            Self: The updated orchestration handoffs, allowing for method chaining.
+        """
+        return self._add(
+            source_agent=source_agent if isinstance(source_agent, str) else source_agent.name,
+            target_agent=target_agent if isinstance(target_agent, str) else target_agent.name,
+            description=description or target_agent.description or "" if isinstance(target_agent, Agent) else "",
+        )
+
+    def add_many(self, source_agent: str | Agent, target_agents: list[str | Agent] | AgentHandoffs) -> "Self":
+        """Add multiple handoff connections to the source agent.
+
+        Args:
+            source_agent (str | Agent): The source agent name or instance.
+            target_agents (list[str | Agent] | AgentHandoffs): A list of target agent names or instances.
+
+        Returns:
+            Self: The updated orchestration handoffs, allowing for method chaining.
+        """
+        if isinstance(target_agents, list):
+            for target_agent in target_agents:
+                self._add(
+                    source_agent=source_agent if isinstance(source_agent, str) else source_agent.name,
+                    target_agent=target_agent if isinstance(target_agent, str) else target_agent.name,
+                    description=target_agent.description or "" if isinstance(target_agent, Agent) else "",
+                )
+        elif isinstance(target_agents, dict):
+            for target_agent_name, description in target_agents.items():
+                self._add(
+                    source_agent=source_agent if isinstance(source_agent, str) else source_agent.name,
+                    target_agent=target_agent_name,
+                    description=description,
+                )
+        return self
+
+    def _add(self, source_agent: str, target_agent: str, description: str) -> "Self":
+        """Helper method to add a handoff connection."""
+        self.setdefault(source_agent, AgentHandoffs())[target_agent] = description or ""
+
+        return self
+
+
+@experimental
+class HandoffStartMessage(KernelBaseModel):
+    """A start message type to kick off a handoff group chat."""
+
+    body: DefaultTypeAlias
+
+
+@experimental
+class HandoffRequestMessage(KernelBaseModel):
+    """A request message type for agents in a handoff group chat."""
+
+    agent_name: str
+
+
+@experimental
+class HandoffResponseMessage(KernelBaseModel):
+    """A response message type from agents in a handoff group chat."""
+
+    body: ChatMessageContent
+
+
+HANDOFF_PLUGIN_NAME = "Handoff"
+
+
+# endregion Messages and Types
+
+# region HandoffAgentActor
+
+
+@experimental
+class HandoffAgentActor(AgentActorBase):
+    """An agent actor that handles handoff messages in a group chat."""
+
+    def __init__(
+        self,
+        agent: Agent,
+        internal_topic_type: str,
+        handoff_connections: AgentHandoffs,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+        human_response_function: Callable[[], Awaitable[ChatMessageContent] | ChatMessageContent] | None = None,
+    ) -> None:
+        """Initialize the handoff agent actor."""
+        self._handoff_connections = handoff_connections
+        self._result_callback = result_callback
+
+        self._kernel = agent.kernel.clone()
+        self._add_handoff_functions()
+
+        self._handoff_agent_name: str | None = None
+        self._task_completed = False
+        self._human_response_function = human_response_function
+
+        super().__init__(
+            agent=agent,
+            internal_topic_type=internal_topic_type,
+            exception_callback=exception_callback,
+            agent_response_callback=agent_response_callback,
+            streaming_agent_response_callback=streaming_agent_response_callback,
+        )
+
+    def _add_handoff_functions(self) -> None:
+        """Add handoff functions to the agent's kernel."""
+        functions: list[KernelFunctionFromMethod] = []
+        for handoff_agent_name, handoff_description in self._handoff_connections.items():
+            function_name = f"transfer_to_{handoff_agent_name}"
+            function_description = handoff_description
+            return_parameter = KernelParameterMetadata(
+                name="return",
+                description="",
+                default_value=None,
+                type_="None",
+                type_object=None,
+                is_required=False,
+            )
+            function_metadata = KernelFunctionMetadata(
+                name=function_name,
+                description=function_description,
+                parameters=[],
+                return_parameter=return_parameter,
+                is_prompt=False,
+                is_asynchronous=True,
+                plugin_name=HANDOFF_PLUGIN_NAME,
+                additional_properties={},
+            )
+            functions.append(
+                KernelFunctionFromMethod.model_construct(
+                    metadata=function_metadata,
+                    method=partial(self._handoff_to_agent, handoff_agent_name),
+                )
+            )
+        functions.append(KernelFunctionFromMethod(self._complete_task, plugin_name=HANDOFF_PLUGIN_NAME))
+        self._kernel.add_plugin(plugin=KernelPlugin(name=HANDOFF_PLUGIN_NAME, functions=functions))
+        self._kernel.add_filter(FilterTypes.AUTO_FUNCTION_INVOCATION, self._handoff_function_filter)
+
+    async def _handoff_to_agent(self, agent_name: str) -> None:
+        """Handoff the conversation to another agent."""
+        logger.debug(f"{self.id}: Setting handoff agent name to {agent_name}.")
+        self._handoff_agent_name = agent_name
+
+    async def _handoff_function_filter(self, context: AutoFunctionInvocationContext, next):
+        """A filter to terminate an agent when it decides to handoff the conversation to another agent."""
+        await next(context)
+        if context.function.plugin_name == HANDOFF_PLUGIN_NAME:
+            context.terminate = True
+
+    @kernel_function(
+        name="complete_task", description="Complete the task with a summary when no further requests are given."
+    )
+    async def _complete_task(self, task_summary: str) -> None:
+        """End the task with a summary."""
+        logger.debug(f"{self.id}: Completing task with summary: {task_summary}")
+        if self._result_callback:
+            await self._result_callback(
+                ChatMessageContent(
+                    role=AuthorRole.ASSISTANT,
+                    name=self._agent.name,
+                    content=f"Task is completed with summary: {task_summary}",
+                )
+            )
+        self._task_completed = True
+
+    @message_handler
+    async def _handle_start_message(self, message: HandoffStartMessage, cts: MessageContext) -> None:
+        logger.debug(f"{self.id}: Received handoff start message.")
+        if isinstance(message.body, ChatMessageContent):
+            self._message_cache.add_message(message.body)
+        elif isinstance(message.body, list) and all(isinstance(m, ChatMessageContent) for m in message.body):
+            for m in message.body:
+                self._message_cache.add_message(m)
+        else:
+            raise ValueError(f"Invalid message body type: {type(message.body)}. Expected {DefaultTypeAlias}.")
+
+    @message_handler
+    async def _handle_response_message(self, message: HandoffResponseMessage, cts: MessageContext) -> None:
+        """Handle a response message from an agent in the handoff group."""
+        logger.debug(f"{self.id}: Received handoff response message.")
+        self._message_cache.add_message(message.body)
+
+    @message_handler
+    async def _handle_request_message(self, message: HandoffRequestMessage, cts: MessageContext) -> None:
+        """Handle a request message from an agent in the handoff group."""
+        if message.agent_name != self._agent.name:
+            return
+        logger.debug(f"{self.id}: Received handoff request message.")
+
+        response = await self._invoke_agent_with_potentially_no_response(kernel=self._kernel)
+
+        while not self._task_completed:
+            if self._handoff_agent_name:
+                await self.publish_message(
+                    HandoffRequestMessage(agent_name=self._handoff_agent_name),
+                    TopicId(self._internal_topic_type, self.id.key),
+                )
+                self._handoff_agent_name = None
+                break
+
+            if response is None:
+                raise RuntimeError(
+                    f'Agent "{self._agent.name}" did not return any response nor did not set a handoff agent name.'
+                )
+
+            await self.publish_message(
+                HandoffResponseMessage(body=response),
+                TopicId(self._internal_topic_type, self.id.key),
+                cancellation_token=cts.cancellation_token,
+            )
+
+            if self._human_response_function:
+                human_response = await self._call_human_response_function()
+                await self.publish_message(
+                    HandoffResponseMessage(body=human_response),
+                    TopicId(self._internal_topic_type, self.id.key),
+                    cancellation_token=cts.cancellation_token,
+                )
+                response = await self._invoke_agent_with_potentially_no_response(
+                    additional_messages=human_response,
+                    kernel=self._kernel,
+                )
+            else:
+                await self._complete_task(
+                    task_summary="No handoff agent name provided and no human response function set. Ending task."
+                )
+                break
+
+    async def _call_human_response_function(self) -> ChatMessageContent:
+        """Call the human response function if it is set."""
+        assert self._human_response_function  # nosec B101
+        if inspect.iscoroutinefunction(self._human_response_function):
+            return await self._human_response_function()
+        return self._human_response_function()  # type: ignore[return-value]
+
+    @ActorBase.exception_handler
+    async def _invoke_agent_with_potentially_no_response(
+        self, additional_messages: DefaultTypeAlias | None = None, **kwargs
+    ) -> ChatMessageContent | None:
+        """Invoke the agent with the current chat history or thread and optionally additional messages.
+
+        This method differs from `_invoke_agent` in that it handles the case where no response is returned
+        from the agent gracefully, returning `None` instead of raising an error.
+
+        The reason for this is that agents in a handoff group chat may not always produce a response when
+        a handoff function is invoked, where the `_handoff_function_filter` will terminate the auto function
+        invocation loop before a response is produced. In such cases, this method will return `None`
+        instead of raising an error.
+        """
+        streaming_message_buffer: list[StreamingChatMessageContent] = []
+        messages = self._create_messages(additional_messages)
+
+        async for response_item in self._agent.invoke_stream(
+            messages,  # type: ignore[arg-type]
+            thread=self._agent_thread,
+            on_intermediate_message=self._handle_intermediate_message,
+            **kwargs,
+        ):
+            # Buffer message chunks and stream them with correct is_final flag.
+            streaming_message_buffer.append(response_item.message)
+            if len(streaming_message_buffer) > 1:
+                await self._call_streaming_agent_response_callback(streaming_message_buffer[-2], is_final=False)
+            if self._agent_thread is None:
+                self._agent_thread = response_item.thread
+
+        if streaming_message_buffer:
+            # Call the callback for the last message chunk with is_final=True.
+            await self._call_streaming_agent_response_callback(streaming_message_buffer[-1], is_final=True)
+
+        if not streaming_message_buffer:
+            return None
+
+        # Build the full response from the streaming messages
+        full_response = sum(streaming_message_buffer[1:], streaming_message_buffer[0])
+        await self._call_agent_response_callback(full_response)
+
+        return full_response
+
+
+# endregion HandoffAgentActor
+
+# region HandoffOrchestration
+
+
+@experimental
+class HandoffOrchestration(OrchestrationBase[TIn, TOut]):
+    """An orchestration class for managing handoff agents in a group chat."""
+
+    def __init__(
+        self,
+        members: list[Agent],
+        handoffs: OrchestrationHandoffs,
+        name: str | None = None,
+        description: str | None = None,
+        input_transform: Callable[[TIn], Awaitable[DefaultTypeAlias] | DefaultTypeAlias] | None = None,
+        output_transform: Callable[[DefaultTypeAlias], Awaitable[TOut] | TOut] | None = None,
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+        human_response_function: Callable[[], Awaitable[ChatMessageContent] | ChatMessageContent] | None = None,
+    ) -> None:
+        """Initialize the handoff orchestration.
+
+        Args:
+            members (list[Agent]): A list of agents or orchestrations that are part of the
+                handoff group. This first agent in the list will be the one that receives the first message.
+            handoffs (OrchestrationHandoffs): Defines the handoff connections between agents.
+            name (str | None): The name of the orchestration.
+            description (str | None): The description of the orchestration.
+            input_transform (Callable | None): A function that transforms the external input message.
+            output_transform (Callable | None): A function that transforms the internal output message.
+            agent_response_callback (Callable | None): A function that is called when a full response is produced
+                by the agents.
+            streaming_agent_response_callback (Callable | None): A function that is called when a streaming response
+                is produced by the agents.
+            human_response_function (Callable | None): A function that is called when a human response is
+                needed.
+        """
+        self._handoffs = handoffs
+        self._human_response_function = human_response_function
+
+        super().__init__(
+            members=members,
+            name=name,
+            description=description,
+            input_transform=input_transform,
+            output_transform=output_transform,
+            agent_response_callback=agent_response_callback,
+            streaming_agent_response_callback=streaming_agent_response_callback,
+        )
+
+        self._validate_handoffs()
+
+    @override
+    async def _start(
+        self,
+        task: DefaultTypeAlias,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        cancellation_token: CancellationToken,
+    ) -> None:
+        """Start the handoff pattern.
+
+        This ensures that all initial messages are sent to the individual actors
+        and processed before the group chat begins. It's important because if the
+        manager actor processes its start message too quickly (or other actors are
+        too slow), it might send a request to the next agent before the other actors
+        have the necessary context.
+        """
+
+        async def send_start_message(agent: Agent) -> None:
+            target_actor_id = await runtime.get(self._get_agent_actor_type(agent, internal_topic_type))
+            await runtime.send_message(
+                HandoffStartMessage(body=task),
+                target_actor_id,
+                cancellation_token=cancellation_token,
+            )
+
+        await asyncio.gather(*[send_start_message(agent) for agent in self._members])
+
+        # Send the handoff request message to the first agent in the list
+        target_actor_id = await runtime.get(self._get_agent_actor_type(self._members[0], internal_topic_type))
+        await runtime.send_message(
+            HandoffRequestMessage(agent_name=self._members[0].name),
+            target_actor_id,
+            cancellation_token=cancellation_token,
+        )
+
+    @override
+    async def _prepare(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Register the actors and orchestrations with the runtime and add the required subscriptions."""
+        await self._register_members(runtime, internal_topic_type, exception_callback, result_callback)
+        await self._add_subscriptions(runtime, internal_topic_type)
+
+    async def _register_members(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+    ) -> None:
+        """Register the members with the runtime."""
+
+        async def _register_helper(agent: Agent) -> None:
+            handoff_connections = self._handoffs.get(agent.name, AgentHandoffs())
+            await HandoffAgentActor.register(
+                runtime,
+                self._get_agent_actor_type(agent, internal_topic_type),
+                lambda agent=agent, handoff_connections=handoff_connections: HandoffAgentActor(  # type: ignore[misc]
+                    agent,
+                    internal_topic_type,
+                    handoff_connections,
+                    exception_callback,
+                    result_callback=result_callback,
+                    agent_response_callback=self._agent_response_callback,
+                    streaming_agent_response_callback=self._streaming_agent_response_callback,
+                    human_response_function=self._human_response_function,
+                ),
+            )
+
+        await asyncio.gather(*[_register_helper(member) for member in self._members])
+
+    async def _add_subscriptions(self, runtime: CoreRuntime, internal_topic_type: str) -> None:
+        """Add subscriptions to the runtime."""
+        subscriptions: list[TypeSubscription] = [
+            TypeSubscription(
+                internal_topic_type,
+                self._get_agent_actor_type(member, internal_topic_type),
+            )
+            for member in self._members
+        ]
+
+        await asyncio.gather(*[runtime.add_subscription(subscription) for subscription in subscriptions])
+
+    def _get_agent_actor_type(self, agent: Agent, internal_topic_type: str) -> str:
+        """Get the actor type for an agent.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{agent.name}_{internal_topic_type}"
+
+    def _validate_handoffs(self) -> None:
+        """Validate the handoffs to ensure all connections are valid."""
+        if not self._handoffs:
+            raise ValueError("Handoffs cannot be empty. Please provide at least one handoff connection.")
+
+        member_names = {m.name for m in self._members}
+        for agent_name, connections in self._handoffs.items():
+            if agent_name not in member_names:
+                raise ValueError(f"Agent {agent_name} is not a member of the handoff group.")
+            for handoff_agent_name in connections:
+                if handoff_agent_name not in member_names:
+                    raise ValueError(f"Agent {handoff_agent_name} is not a member of the handoff group.")
+                if handoff_agent_name == agent_name:
+                    raise ValueError(f"Agent {agent_name} cannot handoff to itself.")
+
+
+# endregion HandoffOrchestration

python/semantic_kernel/agents/orchestration/magentic.py

@@ -0,0 +1,911 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+import sys
+from abc import ABC, abstractmethod
+from collections.abc import Awaitable, Callable
+from html import escape
+from typing import Annotated
+
+from pydantic import Field
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.orchestration.agent_actor_base import ActorBase, AgentActorBase
+from semantic_kernel.agents.orchestration.orchestration_base import DefaultTypeAlias, OrchestrationBase, TIn, TOut
+from semantic_kernel.agents.orchestration.prompts._magentic_prompts import (
+    ORCHESTRATOR_FINAL_ANSWER_PROMPT,
+    ORCHESTRATOR_PROGRESS_LEDGER_PROMPT,
+    ORCHESTRATOR_TASK_LEDGER_FACTS_PROMPT,
+    ORCHESTRATOR_TASK_LEDGER_FACTS_UPDATE_PROMPT,
+    ORCHESTRATOR_TASK_LEDGER_FULL_PROMPT,
+    ORCHESTRATOR_TASK_LEDGER_PLAN_PROMPT,
+    ORCHESTRATOR_TASK_LEDGER_PLAN_UPDATE_PROMPT,
+)
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.routed_agent import message_handler
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.agents.runtime.in_process.type_subscription import TypeSubscription
+from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.prompt_template.kernel_prompt_template import KernelPromptTemplate
+from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+# region Messages and Types
+
+
+@experimental
+class MagenticStartMessage(KernelBaseModel):
+    """A message to start a magentic group chat."""
+
+    body: ChatMessageContent
+
+
+@experimental
+class MagenticRequestMessage(KernelBaseModel):
+    """A request message type for agents in a magentic group chat."""
+
+    agent_name: str
+
+
+@experimental
+class MagenticResponseMessage(KernelBaseModel):
+    """A response message type from agents in a magentic group chat."""
+
+    body: ChatMessageContent
+
+
+@experimental
+class MagenticResetMessage(KernelBaseModel):
+    """A message to reset a participant's chat history in a magentic group chat."""
+
+    pass
+
+
+@experimental
+class ProgressLedgerItem(KernelBaseModel):
+    """A progress ledger item."""
+
+    reason: str
+    answer: str | bool
+
+
+@experimental
+class ProgressLedger(KernelBaseModel):
+    """A progress ledger."""
+
+    is_request_satisfied: ProgressLedgerItem
+    is_in_loop: ProgressLedgerItem
+    is_progress_being_made: ProgressLedgerItem
+    next_speaker: ProgressLedgerItem
+    instruction_or_question: ProgressLedgerItem
+
+
+@experimental
+class MagenticContext(KernelBaseModel):
+    """Context for the Magentic manager."""
+
+    task: Annotated[ChatMessageContent, Field(description="The task to be completed.")]
+    chat_history: Annotated[
+        ChatHistory, Field(description="The chat history to be used to generate the facts and plan.")
+    ] = ChatHistory()
+    participant_descriptions: Annotated[
+        dict[str, str], Field(description="The descriptions of the participants in the group.")
+    ]
+    round_count: Annotated[int, Field(description="The number of rounds completed.")] = 0
+    stall_count: Annotated[int, Field(description="The number of stalls detected.")] = 0
+    reset_count: Annotated[int, Field(description="The number of resets detected.")] = 0
+
+    def reset(self) -> None:
+        """Reset the context.
+
+        This will clear the chat history and reset the stall count.
+        This won't reset the task, round count, or participant descriptions.
+        """
+        self.chat_history.clear()
+        self.stall_count = 0
+        self.reset_count += 1
+
+
+# endregion Messages and Types
+
+# region MagenticManager
+
+
+@experimental
+class MagenticManagerBase(KernelBaseModel, ABC):
+    """Base class for the Magentic One manager."""
+
+    max_stall_count: Annotated[int, Field(description="The maximum number of stalls allowed before a reset.", ge=0)] = 3
+    max_reset_count: Annotated[int | None, Field(description="The maximum number of resets allowed.", ge=0)] = None
+    max_round_count: Annotated[
+        int | None, Field(description="The maximum number of rounds (agent responses) allowed.", gt=0)
+    ] = None
+
+    @abstractmethod
+    async def plan(self, magentic_context: MagenticContext) -> ChatMessageContent:
+        """Create a plan for the task.
+
+        This is called when the task is first started.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ChatMessageContent: The task ledger.
+        """
+        ...
+
+    @abstractmethod
+    async def replan(self, magentic_context: MagenticContext) -> ChatMessageContent:
+        """Replan for the task.
+
+        This is called when the task is stalled or looping.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ChatMessageContent: The updated task ledger.
+        """
+        ...
+
+    @abstractmethod
+    async def create_progress_ledger(self, magentic_context: MagenticContext) -> ProgressLedger:
+        """Create a progress ledger.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ProgressLedger: The progress ledger.
+        """
+        ...
+
+    @abstractmethod
+    async def prepare_final_answer(self, magentic_context: MagenticContext) -> ChatMessageContent:
+        """Prepare the final answer.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ChatMessageContent: The final answer.
+        """
+        ...
+
+
+@experimental
+class _TaskLedger(KernelBaseModel):
+    """Task ledger for the Standard Magentic manager."""
+
+    facts: Annotated[ChatMessageContent, Field(description="The facts about the task.")]
+    plan: Annotated[ChatMessageContent, Field(description="The plan for the task.")]
+
+
+@experimental
+class StandardMagenticManager(MagenticManagerBase):
+    """Standard Magentic manager implementation.
+
+    This is the default implementation of the Magentic manager.
+    It uses the task ledger to keep track of the facts and plan for the task.
+
+    This implementation requires a chat completion model with structured outputs.
+    """
+
+    chat_completion_service: ChatCompletionClientBase
+    prompt_execution_settings: PromptExecutionSettings
+
+    task_ledger_facts_prompt: str = ORCHESTRATOR_TASK_LEDGER_FACTS_PROMPT
+    task_ledger_plan_prompt: str = ORCHESTRATOR_TASK_LEDGER_PLAN_PROMPT
+    task_ledger_full_prompt: str = ORCHESTRATOR_TASK_LEDGER_FULL_PROMPT
+    task_ledger_facts_update_prompt: str = ORCHESTRATOR_TASK_LEDGER_FACTS_UPDATE_PROMPT
+    task_ledger_plan_update_prompt: str = ORCHESTRATOR_TASK_LEDGER_PLAN_UPDATE_PROMPT
+    progress_ledger_prompt: str = ORCHESTRATOR_PROGRESS_LEDGER_PROMPT
+    final_answer_prompt: str = ORCHESTRATOR_FINAL_ANSWER_PROMPT
+
+    task_ledger: _TaskLedger | None = None
+
+    def __init__(
+        self,
+        chat_completion_service: ChatCompletionClientBase,
+        prompt_execution_settings: PromptExecutionSettings | None = None,
+        **kwargs,
+    ) -> None:
+        """Initialize the Standard Magentic manager.
+
+        Args:
+            chat_completion_service (ChatCompletionClientBase): The chat completion service to use.
+            prompt_execution_settings (PromptExecutionSettings | None): The prompt execution settings to use.
+            **kwargs: Additional keyword arguments for prompts:
+                - task_ledger_facts_prompt: The prompt to use for the task ledger facts.
+                - task_ledger_plan_prompt: The prompt to use for the task ledger plan.
+                - task_ledger_full_prompt: The prompt to use for the full task ledger.
+                - task_ledger_facts_update_prompt: The prompt to use for the task ledger facts update.
+                - task_ledger_plan_update_prompt: The prompt to use for the task ledger plan update.
+                - progress_ledger_prompt: The prompt to use for the progress ledger.
+                - final_answer_prompt: The prompt to use for the final answer.
+        """
+        # Bast effort to make sure the service supports structured output. Even if the service supports
+        # structured output, the model may not support it, in which case there is no good way to check.
+        if prompt_execution_settings is None:
+            prompt_execution_settings = chat_completion_service.instantiate_prompt_execution_settings()
+            if not hasattr(prompt_execution_settings, "response_format"):
+                raise ValueError("The service must support structured output.")
+        else:
+            if not hasattr(prompt_execution_settings, "response_format"):
+                raise ValueError("The service must support structured output.")
+            if getattr(prompt_execution_settings, "response_format", None) is not None:
+                raise ValueError("The prompt execution settings must not have a response format set.")
+
+        super().__init__(
+            chat_completion_service=chat_completion_service,
+            prompt_execution_settings=prompt_execution_settings,
+            **kwargs,
+        )
+
+    @override
+    async def plan(self, magentic_context: MagenticContext) -> ChatMessageContent:
+        """Plan the task.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ChatMessageContent: The task ledger.
+        """
+        # 1. Gather the facts
+        prompt_template = KernelPromptTemplate(
+            prompt_template_config=PromptTemplateConfig(template=self.task_ledger_facts_prompt)
+        )
+        magentic_context.chat_history.add_message(
+            ChatMessageContent(
+                role=AuthorRole.USER,
+                content=await prompt_template.render(Kernel(), KernelArguments(task=magentic_context.task.content)),
+            )
+        )
+        facts = await self.chat_completion_service.get_chat_message_content(
+            magentic_context.chat_history,
+            self.prompt_execution_settings,
+        )
+        assert facts is not None  # nosec B101
+        magentic_context.chat_history.add_message(facts)
+
+        # 2. Create the plan
+        prompt_template = KernelPromptTemplate(
+            prompt_template_config=PromptTemplateConfig(template=self.task_ledger_plan_prompt),
+            allow_dangerously_set_content=True,
+        )
+
+        escaped_participant_descriptions: dict[str, str] = {}
+
+        for key, value in magentic_context.participant_descriptions.items():
+            escaped_participant_descriptions[key] = escape(value)
+
+        magentic_context.chat_history.add_message(
+            ChatMessageContent(
+                role=AuthorRole.USER,
+                content=await prompt_template.render(
+                    Kernel(),
+                    KernelArguments(team=escaped_participant_descriptions),
+                ),
+            )
+        )
+        plan = await self.chat_completion_service.get_chat_message_content(
+            magentic_context.chat_history,
+            self.prompt_execution_settings,
+        )
+        assert plan is not None  # nosec B101
+
+        self.task_ledger = _TaskLedger(facts=facts, plan=plan)
+        return await self._render_task_ledger(magentic_context)
+
+    @override
+    async def replan(self, magentic_context: MagenticContext) -> ChatMessageContent:
+        """Replan the task.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ChatMessageContent: The updated task ledger.
+        """
+        if self.task_ledger is None:
+            raise RuntimeError("The task ledger is not initialized. Planning needs to happen first.")
+
+        # 1. Update the facts
+        prompt_template = KernelPromptTemplate(
+            prompt_template_config=PromptTemplateConfig(template=self.task_ledger_facts_update_prompt)
+        )
+        magentic_context.chat_history.add_message(
+            ChatMessageContent(
+                role=AuthorRole.USER,
+                content=await prompt_template.render(
+                    Kernel(),
+                    KernelArguments(task=magentic_context.task.content, old_facts=self.task_ledger.facts.content),
+                ),
+            )
+        )
+        facts = await self.chat_completion_service.get_chat_message_content(
+            magentic_context.chat_history,
+            self.prompt_execution_settings,
+        )
+        assert facts is not None  # nosec B101
+        magentic_context.chat_history.add_message(facts)
+
+        # 2. Update the plan
+        prompt_template = KernelPromptTemplate(
+            prompt_template_config=PromptTemplateConfig(template=self.task_ledger_plan_update_prompt),
+            allow_dangerously_set_content=True,
+        )
+
+        escaped_participant_descriptions: dict[str, str] = {}
+
+        for key, value in magentic_context.participant_descriptions.items():
+            escaped_participant_descriptions[key] = escape(value)
+
+        magentic_context.chat_history.add_message(
+            ChatMessageContent(
+                role=AuthorRole.USER,
+                content=await prompt_template.render(
+                    Kernel(),
+                    KernelArguments(team=escaped_participant_descriptions),
+                ),
+            )
+        )
+        plan = await self.chat_completion_service.get_chat_message_content(
+            magentic_context.chat_history,
+            self.prompt_execution_settings,
+        )
+        assert plan is not None  # nosec B101
+
+        self.task_ledger.facts = facts
+        self.task_ledger.plan = plan
+        return await self._render_task_ledger(magentic_context)
+
+    async def _render_task_ledger(self, magentic_context: MagenticContext) -> ChatMessageContent:
+        """Render the task ledger to a string.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ChatMessageContent: The rendered task ledger.
+        """
+        if self.task_ledger is None:
+            raise RuntimeError("The task ledger is not initialized. Planning needs to happen first.")
+
+        prompt_template = KernelPromptTemplate(
+            prompt_template_config=PromptTemplateConfig(template=self.task_ledger_full_prompt),
+            allow_dangerously_set_content=True,
+        )
+
+        escaped_participant_descriptions: dict[str, str] = {}
+
+        for key, value in magentic_context.participant_descriptions.items():
+            escaped_participant_descriptions[key] = escape(value)
+
+        rendered_task_ledger = await prompt_template.render(
+            Kernel(),
+            KernelArguments(
+                task=magentic_context.task.content,
+                team=escaped_participant_descriptions,
+                facts=self.task_ledger.facts.content,
+                plan=self.task_ledger.plan.content,
+            ),
+        )
+
+        return ChatMessageContent(role=AuthorRole.ASSISTANT, content=rendered_task_ledger)
+
+    @override
+    async def create_progress_ledger(self, magentic_context: MagenticContext) -> ProgressLedger:
+        """Create a progress ledger.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ProgressLedger: The progress ledger.
+        """
+        prompt_template = KernelPromptTemplate(
+            prompt_template_config=PromptTemplateConfig(template=self.progress_ledger_prompt),
+            allow_dangerously_set_content=True,
+        )
+
+        escaped_participant_descriptions: dict[str, str] = {}
+
+        for key, value in magentic_context.participant_descriptions.items():
+            escaped_participant_descriptions[key] = escape(value)
+
+        progress_ledger_prompt = await prompt_template.render(
+            Kernel(),
+            KernelArguments(
+                task=magentic_context.task.content,
+                team=escaped_participant_descriptions,
+                names=", ".join(magentic_context.participant_descriptions.keys()),
+            ),
+        )
+        magentic_context.chat_history.add_message(
+            ChatMessageContent(role=AuthorRole.USER, content=progress_ledger_prompt)
+        )
+
+        prompt_execution_settings_clone = PromptExecutionSettings.from_prompt_execution_settings(
+            self.prompt_execution_settings
+        )
+        prompt_execution_settings_clone.update_from_prompt_execution_settings(
+            PromptExecutionSettings(extension_data={"response_format": ProgressLedger})
+        )
+
+        response = await self.chat_completion_service.get_chat_message_content(
+            magentic_context.chat_history,
+            prompt_execution_settings_clone,
+        )
+        assert response is not None  # nosec B101
+
+        return ProgressLedger.model_validate_json(response.content)
+
+    @override
+    async def prepare_final_answer(self, magentic_context: MagenticContext) -> ChatMessageContent:
+        """Prepare the final answer.
+
+        Args:
+            magentic_context (MagenticContext): The context for the Magentic manager.
+
+        Returns:
+            ChatMessageContent: The final answer.
+        """
+        prompt_template = KernelPromptTemplate(
+            prompt_template_config=PromptTemplateConfig(template=self.final_answer_prompt),
+            allow_dangerously_set_content=True,
+        )
+
+        magentic_context.task.content = escape(magentic_context.task.content)
+
+        magentic_context.chat_history.add_message(
+            ChatMessageContent(
+                role=AuthorRole.USER,
+                content=await prompt_template.render(Kernel(), KernelArguments(task=magentic_context.task)),
+            )
+        )
+
+        response = await self.chat_completion_service.get_chat_message_content(
+            magentic_context.chat_history,
+            self.prompt_execution_settings,
+        )
+        assert response is not None  # nosec B101
+
+        return response
+
+
+# endregion MagenticManager
+
+# region MagenticManagerActor
+
+
+@experimental
+class MagenticManagerActor(ActorBase):
+    """Actor for the Magentic One manager."""
+
+    def __init__(
+        self,
+        manager: MagenticManagerBase,
+        internal_topic_type: str,
+        participant_descriptions: dict[str, str],
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+    ) -> None:
+        """Initialize the Magentic One manager actor.
+
+        Args:
+            manager (MagenticManagerBase): The Magentic One manager.
+            internal_topic_type (str): The internal topic type.
+            participant_descriptions (dict[str, str]): The participant descriptions.
+            exception_callback (Callable[[BaseException], None]): A callback function to handle exceptions.
+            result_callback (Callable | None): A callback function to handle the final answer.
+        """
+        self._manager = manager
+        self._internal_topic_type = internal_topic_type
+        self._result_callback = result_callback
+        self._participant_descriptions = participant_descriptions
+        self._context: MagenticContext | None = None
+        self._task_ledger: ChatMessageContent | None = None
+
+        super().__init__("Magentic One Manager", exception_callback)
+
+    @message_handler
+    @ActorBase.exception_handler
+    async def _handle_start_message(self, message: MagenticStartMessage, ctx: MessageContext) -> None:
+        """Handle the start message for the Magentic One manager."""
+        logger.debug(f"{self.id}: Received Magentic One start message.")
+
+        self._context = MagenticContext(
+            task=message.body,
+            participant_descriptions=self._participant_descriptions,
+        )
+
+        # Initial planning
+        self._task_ledger = await self._manager.plan(self._context.model_copy(deep=True))
+
+        await self._run_outer_loop(ctx.cancellation_token)
+
+    @message_handler
+    @ActorBase.exception_handler
+    async def _handle_response_message(self, message: MagenticResponseMessage, ctx: MessageContext) -> None:
+        """Handle the response message for the Magentic One manager."""
+        if self._context is None or self._task_ledger is None:
+            raise RuntimeError("The Magentic manager is not started yet. Make sure to send a start message first.")
+
+        if message.body.role != AuthorRole.USER:
+            self._context.chat_history.add_message(
+                ChatMessageContent(
+                    role=AuthorRole.USER,
+                    content=f"Transferred to {message.body.name}",
+                )
+            )
+        self._context.chat_history.add_message(message.body)
+
+        logger.debug(f"{self.id}: Running inner loop.")
+        await self._run_inner_loop(ctx.cancellation_token)
+
+    async def _run_outer_loop(self, cancellation_token: CancellationToken) -> None:
+        if self._context is None or self._task_ledger is None:
+            raise RuntimeError("The Magentic manager is not started yet. Make sure to send a start message first.")
+
+        # 1. Publish the rendered task ledger to the group chat.
+        # Need to add the task ledger to the orchestrator's chat history
+        # since the publisher won't receive the message it sends even though
+        # the publisher also subscribes to the topic.
+        self._context.chat_history.add_message(
+            ChatMessageContent(
+                role=AuthorRole.ASSISTANT,
+                content=self._task_ledger.content,
+                name=self.__class__.__name__,
+            )
+        )
+
+        logger.debug(f"Initial task ledger:\n{self._task_ledger.content}")
+        await self.publish_message(
+            MagenticResponseMessage(
+                body=self._context.chat_history.messages[-1],
+            ),
+            TopicId(self._internal_topic_type, self.id.key),
+            cancellation_token=cancellation_token,
+        )
+
+        # 2. Start the inner loop.
+        await self._run_inner_loop(cancellation_token)
+
+    async def _run_inner_loop(self, cancellation_token: CancellationToken) -> None:
+        if self._context is None or self._task_ledger is None:
+            raise RuntimeError("The Magentic manager is not started yet. Make sure to send a start message first.")
+
+        within_limits = await self._check_within_limits()
+        if not within_limits:
+            return
+        self._context.round_count += 1
+
+        # 1. Create a progress ledger
+        current_progress_ledger = await self._manager.create_progress_ledger(self._context.model_copy(deep=True))
+        logger.debug(f"Current progress ledger:\n{current_progress_ledger.model_dump_json(indent=2)}")
+
+        # 2. Process the progress ledger
+        # 2.1 Check for task completion
+        if current_progress_ledger.is_request_satisfied.answer:
+            logger.debug("Task completed.")
+            await self._prepare_final_answer()
+            return
+        # 2.2 Check for stalling or looping
+        if not current_progress_ledger.is_progress_being_made.answer or current_progress_ledger.is_in_loop.answer:
+            self._context.stall_count += 1
+        else:
+            self._context.stall_count = max(0, self._context.stall_count - 1)
+
+        if self._context.stall_count > self._manager.max_stall_count:
+            logger.debug("Stalling detected. Resetting the task.")
+            self._task_ledger = await self._manager.replan(self._context.model_copy(deep=True))
+            await self._reset_for_outer_loop(cancellation_token)
+            logger.debug("Restarting outer loop.")
+            await self._run_outer_loop(cancellation_token)
+            return
+
+        # 2.3 Publish for next step
+        next_step = current_progress_ledger.instruction_or_question.answer
+        self._context.chat_history.add_message(
+            ChatMessageContent(
+                role=AuthorRole.ASSISTANT,
+                content=next_step if isinstance(next_step, str) else str(next_step),
+                name=self.__class__.__name__,
+            )
+        )
+        await self.publish_message(
+            MagenticResponseMessage(
+                body=self._context.chat_history.messages[-1],
+            ),
+            TopicId(self._internal_topic_type, self.id.key),
+            cancellation_token=cancellation_token,
+        )
+
+        # 2.4 Request the next speaker to speak
+        next_speaker = current_progress_ledger.next_speaker.answer
+        if next_speaker not in self._participant_descriptions:
+            raise ValueError(f"Unknown speaker: {next_speaker}")
+
+        logger.debug(f"Magentic One manager selected agent: {next_speaker}")
+
+        await self.publish_message(
+            MagenticRequestMessage(agent_name=next_speaker),
+            TopicId(self._internal_topic_type, self.id.key),
+            cancellation_token=cancellation_token,
+        )
+
+    async def _reset_for_outer_loop(self, cancellation_token: CancellationToken) -> None:
+        """Reset the context for the outer loop."""
+        if self._context is None:
+            raise RuntimeError("The Magentic manager is not started yet. Make sure to send a start message first.")
+
+        await self.publish_message(
+            MagenticResetMessage(),
+            TopicId(self._internal_topic_type, self.id.key),
+            cancellation_token=cancellation_token,
+        )
+        self._context.reset()
+
+    async def _prepare_final_answer(self) -> None:
+        """Prepare the final answer and send it to the result callback."""
+        if self._context is None:
+            raise RuntimeError("The Magentic manager is not started yet. Make sure to send a start message first.")
+
+        final_answer = await self._manager.prepare_final_answer(self._context.model_copy(deep=True))
+
+        if self._result_callback:
+            await self._result_callback(final_answer)
+
+    async def _check_within_limits(self) -> bool:
+        """Check if the manager is within the limits."""
+        if self._context is None:
+            raise RuntimeError("The Magentic manager is not started yet. Make sure to send a start message first.")
+
+        hit_round_limit = (
+            self._manager.max_round_count is not None and self._context.round_count >= self._manager.max_round_count
+        )
+        hit_reset_limit = (
+            self._manager.max_reset_count is not None and self._context.reset_count > self._manager.max_reset_count
+        )
+
+        if hit_round_limit or hit_reset_limit:
+            limit_type = "round" if hit_round_limit else "reset"
+            logger.error(f"Max {limit_type} count reached.")
+
+            # Retrieve the latest assistant content produced so far
+            partial_result = next(
+                (m for m in reversed(self._context.chat_history.messages) if m.role == AuthorRole.ASSISTANT),
+                None,
+            )
+            if partial_result is None:
+                partial_result = ChatMessageContent(
+                    role=AuthorRole.ASSISTANT,
+                    content=f"Stopped because the maximum {limit_type} limit was reached. No partial result available.",
+                    name=self.__class__.__name__,
+                )
+
+            if self._result_callback:
+                await self._result_callback(partial_result)
+
+            return False
+
+        return True
+
+
+# endregion MagenticManagerActor
+
+# region MagenticAgentActor
+
+
+@experimental
+class MagenticAgentActor(AgentActorBase):
+    """An agent actor that process messages in a Magentic One group chat."""
+
+    @message_handler
+    async def _handle_response_message(self, message: MagenticResponseMessage, ctx: MessageContext) -> None:
+        logger.debug(f"{self.id}: Received response message.")
+        self._message_cache.add_message(message.body)
+
+    @message_handler
+    async def _handle_request_message(self, message: MagenticRequestMessage, ctx: MessageContext) -> None:
+        if message.agent_name != self._agent.name:
+            return
+
+        logger.debug(f"{self.id}: Received request message.")
+
+        response = await self._invoke_agent()
+
+        logger.debug(f"{self.id} responded with {response}.")
+
+        await self.publish_message(
+            MagenticResponseMessage(body=response),
+            TopicId(self._internal_topic_type, self.id.key),
+            cancellation_token=ctx.cancellation_token,
+        )
+
+    @message_handler
+    async def _handle_reset_message(self, message: MagenticResetMessage, ctx: MessageContext) -> None:
+        """Handle the reset message for the Magentic One group chat."""
+        logger.debug(f"{self.id}: Received reset message.")
+        self._message_cache.clear()
+        if self._agent_thread:
+            await self._agent_thread.delete()
+            self._agent_thread = None
+
+
+# endregion MagenticAgentActor
+
+# region MagenticOrchestration
+
+
+@experimental
+class MagenticOrchestration(OrchestrationBase[TIn, TOut]):
+    """The Magentic One pattern orchestration."""
+
+    def __init__(
+        self,
+        members: list[Agent],
+        manager: MagenticManagerBase,
+        name: str | None = None,
+        description: str | None = None,
+        input_transform: Callable[[TIn], Awaitable[DefaultTypeAlias] | DefaultTypeAlias] | None = None,
+        output_transform: Callable[[DefaultTypeAlias], Awaitable[TOut] | TOut] | None = None,
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+    ) -> None:
+        """Initialize the Magentic One orchestration.
+
+        Args:
+            members (list[Agent]): A list of agents.
+            manager (MagenticManagerBase): The manager for the Magentic One pattern.
+            name (str | None): The name of the orchestration.
+            description (str | None): The description of the orchestration.
+            input_transform (Callable | None): A function that transforms the external input message.
+            output_transform (Callable | None): A function that transforms the internal output message.
+            agent_response_callback (Callable | None): A function that is called when a response is produced
+                by the agents.
+            streaming_agent_response_callback (Callable | None): A function that is called when a streaming response
+                is produced by the agents.
+        """
+        self._manager = manager
+
+        for member in members:
+            if member.description is None:
+                raise ValueError("All members must have a description.")
+
+        super().__init__(
+            members=members,
+            name=name,
+            description=description,
+            input_transform=input_transform,
+            output_transform=output_transform,
+            agent_response_callback=agent_response_callback,
+            streaming_agent_response_callback=streaming_agent_response_callback,
+        )
+
+    @override
+    async def _start(
+        self,
+        task: DefaultTypeAlias,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        cancellation_token: CancellationToken,
+    ) -> None:
+        """Start the Magentic pattern."""
+        if not isinstance(task, ChatMessageContent):
+            # Magentic One only supports ChatMessageContent as input.
+            raise ValueError("The task must be a ChatMessageContent object.")
+
+        target_actor_id = await runtime.get(self._get_manager_actor_type(internal_topic_type))
+        await runtime.send_message(
+            MagenticStartMessage(body=task),
+            target_actor_id,
+            cancellation_token=cancellation_token,
+        )
+
+    @override
+    async def _prepare(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Register the actors and orchestrations with the runtime and add the required subscriptions."""
+        await self._register_members(runtime, internal_topic_type, exception_callback)
+        await self._register_manager(runtime, internal_topic_type, exception_callback, result_callback=result_callback)
+        await self._add_subscriptions(runtime, internal_topic_type)
+
+    async def _register_members(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+    ) -> None:
+        """Register the agents."""
+        await asyncio.gather(*[
+            MagenticAgentActor.register(
+                runtime,
+                self._get_agent_actor_type(agent, internal_topic_type),
+                lambda agent=agent: MagenticAgentActor(  # type: ignore[misc]
+                    agent,
+                    internal_topic_type,
+                    exception_callback,
+                    self._agent_response_callback,
+                    self._streaming_agent_response_callback,
+                ),
+            )
+            for agent in self._members
+        ])
+
+    async def _register_manager(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]] | None = None,
+    ) -> None:
+        """Register the group chat manager."""
+        await MagenticManagerActor.register(
+            runtime,
+            self._get_manager_actor_type(internal_topic_type),
+            lambda: MagenticManagerActor(
+                self._manager,
+                internal_topic_type=internal_topic_type,
+                participant_descriptions={agent.name: agent.description for agent in self._members},  # type: ignore[misc]
+                exception_callback=exception_callback,
+                result_callback=result_callback,
+            ),
+        )
+
+    async def _add_subscriptions(self, runtime: CoreRuntime, internal_topic_type: str) -> None:
+        subscriptions: list[TypeSubscription] = []
+        for agent in self._members:
+            subscriptions.append(
+                TypeSubscription(internal_topic_type, self._get_agent_actor_type(agent, internal_topic_type))
+            )
+        subscriptions.append(TypeSubscription(internal_topic_type, self._get_manager_actor_type(internal_topic_type)))
+
+        await asyncio.gather(*[runtime.add_subscription(sub) for sub in subscriptions])
+
+    def _get_agent_actor_type(self, agent: Agent, internal_topic_type: str) -> str:
+        """Get the actor type for an agent.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{agent.name}_{internal_topic_type}"
+
+    def _get_manager_actor_type(self, internal_topic_type: str) -> str:
+        """Get the actor type for the group chat manager.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{MagenticManagerActor.__name__}_{internal_topic_type}"
+
+
+# endregion MagenticOrchestration

python/semantic_kernel/agents/orchestration/orchestration_base.py

@@ -0,0 +1,344 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import inspect
+import json
+import logging
+import uuid
+from abc import ABC, abstractmethod
+from collections.abc import Awaitable, Callable
+from typing import Generic, Union, get_args
+
+from pydantic import Field
+from typing_extensions import TypeVar
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+DefaultTypeAlias = Union[ChatMessageContent, list[ChatMessageContent]]
+
+TIn = TypeVar("TIn", default=DefaultTypeAlias)
+TOut = TypeVar("TOut", default=DefaultTypeAlias)
+
+
+@experimental
+class OrchestrationResult(KernelBaseModel, Generic[TOut]):
+    """The result of an invocation of an orchestration."""
+
+    background_task: asyncio.Task | None = None
+    value: TOut | None = None
+    exception: BaseException | None = None
+    event: asyncio.Event = Field(default_factory=asyncio.Event)
+    cancellation_token: CancellationToken = Field(default_factory=CancellationToken)
+
+    async def get(self, timeout: float | None = None) -> TOut:
+        """Get the result of the invocation.
+
+        If a timeout is specified, the method will wait for the result for the specified time.
+        If the result is not available within the timeout, a TimeoutError will be raised but the
+        invocation will not be aborted.
+
+        Args:
+            timeout (int | None): The timeout (seconds) for getting the result. If None, wait indefinitely.
+
+        Returns:
+            TOut: The result of the invocation.
+        """
+        if timeout is not None:
+            await asyncio.wait_for(self.event.wait(), timeout=timeout)
+        else:
+            await self.event.wait()
+
+        if self.value is None:
+            if self.cancellation_token.is_cancelled():
+                raise RuntimeError("The invocation was canceled before it could complete.")
+            if self.exception is not None:
+                raise self.exception
+            raise RuntimeError("The invocation did not produce a result.")
+        return self.value
+
+    def cancel(self) -> None:
+        """Cancel the invocation.
+
+        This method will cancel the invocation.
+        Actors that have received messages will continue to process them, but no new messages will be processed.
+        """
+        if self.cancellation_token.is_cancelled():
+            raise RuntimeError("The invocation has already been canceled.")
+        if self.event.is_set():
+            raise RuntimeError("The invocation has already been completed.")
+
+        self.cancellation_token.cancel()
+        self.event.set()
+
+
+@experimental
+class OrchestrationBase(ABC, Generic[TIn, TOut]):
+    """Base class for multi-agent orchestration."""
+
+    t_in: type[TIn] | None = None
+    t_out: type[TOut] | None = None
+
+    def __init__(
+        self,
+        members: list[Agent],
+        name: str | None = None,
+        description: str | None = None,
+        input_transform: Callable[[TIn], Awaitable[DefaultTypeAlias] | DefaultTypeAlias] | None = None,
+        output_transform: Callable[[DefaultTypeAlias], Awaitable[TOut] | TOut] | None = None,
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+    ) -> None:
+        """Initialize the orchestration base.
+
+        Args:
+            members (list[Agent]): The list of agents to be used.
+            name (str | None): A unique name of the orchestration. If None, a unique name will be generated.
+            description (str | None): The description of the orchestration. If None, use a default description.
+            input_transform (Callable | None): A function that transforms the external input message.
+            output_transform (Callable | None): A function that transforms the internal output message.
+            agent_response_callback (Callable | None): A function that is called when a full response is produced
+                by the agents.
+            streaming_agent_response_callback (Callable | None): A function that is called when a streaming response
+                is produced by the agents.
+        """
+        if not members:
+            raise ValueError("The members list cannot be empty.")
+        self._members = members
+
+        self.name = name or f"{self.__class__.__name__}_{uuid.uuid4().hex}"
+        self.description = description or "A multi-agent orchestration."
+
+        self._input_transform = input_transform or self._default_input_transform
+        self._output_transform = output_transform or self._default_output_transform
+
+        self._agent_response_callback = agent_response_callback
+        self._streaming_agent_response_callback = streaming_agent_response_callback
+
+    def _set_types(self) -> None:
+        """Set the external input and output types from the class arguments.
+
+        This method can only be run after the class has been initialized because it relies on the
+        `__orig_class__` attributes to determine the type parameters.
+
+        This method will first try to get the type parameters from the class itself. The `__orig_class__`
+        attribute will contain the external input and output types if they are explicitly given, for example:
+        ```
+        class MyOrchestration(OrchestrationBase[TIn, TOut]):
+            pass
+
+
+        my_orchestration = MyOrchestration[str, str](...)
+        ```
+        If the type parameters are not explicitly given, for example when the TypeVars has defaults, for example:
+        ```
+        TIn = TypeVar("TIn", default=str)
+        TOut = TypeVar("TOut", default=str)
+
+
+        class MyOrchestration(OrchestrationBase[TIn, TOut]):
+            pass
+
+
+        my_orchestration = MyOrchestration(...)
+        ```
+        The type parameters can be inferred from the `__orig_bases__` attribute.
+        """
+        if all([self.t_in is not None, self.t_out is not None]):
+            return
+
+        try:
+            args = self.__orig_class__.__args__  # type: ignore[attr-defined]
+            if len(args) == 1:
+                self.t_in = args[0]
+                self.t_out = DefaultTypeAlias  # type: ignore[assignment]
+            elif len(args) == 2:
+                self.t_in = args[0]
+                self.t_out = args[1]
+            else:
+                raise TypeError("Orchestration must have two type parameters.")
+        except AttributeError:
+            args = get_args(self.__orig_bases__[0])  # type: ignore[attr-defined]
+
+            if len(args) != 2:
+                raise TypeError("Orchestration must be subclassed with two type parameters.")
+            self.t_in = args[0] if isinstance(args[0], type) else getattr(args[0], "__default__", None)  # type: ignore[assignment]
+            self.t_out = args[1] if isinstance(args[1], type) else getattr(args[1], "__default__", None)  # type: ignore[assignment]
+
+        if any([self.t_in is None, self.t_out is None]):
+            raise TypeError("Orchestration must have concrete types for all type parameters.")
+
+    async def invoke(
+        self,
+        task: str | DefaultTypeAlias | TIn,
+        runtime: CoreRuntime,
+    ) -> OrchestrationResult[TOut]:
+        """Invoke the multi-agent orchestration.
+
+        This method is non-blocking and will return immediately.
+        To wait for the result, use the `get` method of the `OrchestrationResult` object.
+
+        Args:
+            task (str, DefaultTypeAlias, TIn): The task to be executed by the agents.
+            runtime (CoreRuntime): The runtime environment for the agents.
+        """
+        self._set_types()
+
+        orchestration_result = OrchestrationResult[self.t_out]()  # type: ignore[name-defined]
+
+        async def result_callback(result: DefaultTypeAlias) -> None:
+            nonlocal orchestration_result
+            if inspect.iscoroutinefunction(self._output_transform):
+                transformed_result = await self._output_transform(result)
+            else:
+                transformed_result = self._output_transform(result)
+
+            orchestration_result.value = transformed_result
+            orchestration_result.event.set()
+
+        def inner_exception_callback(exception: BaseException) -> None:
+            nonlocal orchestration_result
+            orchestration_result.exception = exception
+            orchestration_result.event.set()
+
+        # This unique topic type is used to isolate the orchestration run from others.
+        internal_topic_type = uuid.uuid4().hex
+
+        await self._prepare(
+            runtime,
+            internal_topic_type=internal_topic_type,
+            result_callback=result_callback,
+            exception_callback=inner_exception_callback,
+        )
+
+        if isinstance(task, str):
+            prepared_task = ChatMessageContent(role=AuthorRole.USER, content=task)
+        elif isinstance(task, ChatMessageContent) or (
+            isinstance(task, list) and all(isinstance(item, ChatMessageContent) for item in task)
+        ):
+            prepared_task = task  # type: ignore[assignment]
+        else:
+            if inspect.iscoroutinefunction(self._input_transform):
+                prepared_task = await self._input_transform(task)  # type: ignore[arg-type]
+            else:
+                prepared_task = self._input_transform(task)  # type: ignore[arg-type,assignment]
+
+        background_task = asyncio.create_task(
+            self._start(
+                prepared_task,
+                runtime,
+                internal_topic_type,
+                orchestration_result.cancellation_token,
+            )
+        )
+
+        # Add a callback to surface any exceptions that occur during outside of the runtime.
+        def outer_exception_callback(task: asyncio.Task) -> None:
+            nonlocal orchestration_result
+            try:
+                task.result()
+            except BaseException as e:
+                orchestration_result.exception = e
+                orchestration_result.event.set()
+
+        background_task.add_done_callback(outer_exception_callback)
+        orchestration_result.background_task = background_task
+
+        return orchestration_result
+
+    @abstractmethod
+    async def _start(
+        self,
+        task: DefaultTypeAlias,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        cancellation_token: CancellationToken,
+    ) -> None:
+        """Start the multi-agent orchestration.
+
+        Args:
+            task (ChatMessageContent | list[ChatMessageContent]): The task to be executed by the agents.
+            runtime (CoreRuntime): The runtime environment for the agents.
+            internal_topic_type (str): The internal topic type for the orchestration that this actor is part of.
+            cancellation_token (CancellationToken): The cancellation token for the orchestration.
+        """
+        pass
+
+    @abstractmethod
+    async def _prepare(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Register the actors and orchestrations with the runtime and add the required subscriptions.
+
+        Args:
+            runtime (CoreRuntime): The runtime environment for the agents.
+            internal_topic_type (str): The internal topic type for the orchestration that this actor is part of.
+            exception_callback (Callable): A function that is called when an exception occurs.
+            result_callback (Callable): A function that is called when the result is available.
+        """
+        pass
+
+    def _default_input_transform(self, input_message: TIn) -> DefaultTypeAlias:
+        """Default input transform function.
+
+        This function transforms the external input message to chat message content(s).
+        If the input message is already in the correct format, it is returned as is.
+
+        Args:
+            input_message (TIn): The input message to be transformed.
+
+        Returns:
+            DefaultTypeAlias: The transformed input message.
+        """
+        if isinstance(input_message, ChatMessageContent):
+            return input_message
+
+        if isinstance(input_message, list) and all(isinstance(item, ChatMessageContent) for item in input_message):
+            return input_message
+
+        if isinstance(input_message, self.t_in):  # type: ignore[arg-type]
+            return ChatMessageContent(
+                role=AuthorRole.USER,
+                content=json.dumps(input_message.__dict__),
+            )
+
+        raise TypeError(f"Invalid input message type: {type(input_message)}. Expected {self.t_in}.")
+
+    def _default_output_transform(self, output_message: DefaultTypeAlias) -> TOut:
+        """Default output transform function.
+
+        This function transforms the internal output message to the external output message.
+        If the output message is already in the correct format, it is returned as is.
+
+        Args:
+            output_message (DefaultTypeAlias): The output message to be transformed.
+
+        Returns:
+            TOut: The transformed output message.
+        """
+        if self.t_out == DefaultTypeAlias or self.t_out in get_args(DefaultTypeAlias):
+            if isinstance(output_message, ChatMessageContent) or (
+                isinstance(output_message, list)
+                and all(isinstance(item, ChatMessageContent) for item in output_message)
+            ):
+                return output_message  # type: ignore[return-value]
+            raise TypeError(f"Invalid output message type: {type(output_message)}. Expected {self.t_out}.")
+
+        if isinstance(output_message, ChatMessageContent):
+            return self.t_out(**json.loads(output_message.content))  # type: ignore[misc]
+
+        raise TypeError(f"Unable to transform output message of type {type(output_message)} to {self.t_out}.")

python/semantic_kernel/agents/orchestration/prompts/_magentic_prompts.py

@@ -0,0 +1,150 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+ORCHESTRATOR_TASK_LEDGER_FACTS_PROMPT = """Below I will present you a request.
+
+Before we begin addressing the request, please answer the following pre-survey to the best of your ability.
+Keep in mind that you are Ken Jennings-level with trivia, and Mensa-level with puzzles, so there should be
+a deep well to draw from.
+
+Here is the request:
+
+{{$task}}
+
+Here is the pre-survey:
+
+    1. Please list any specific facts or figures that are GIVEN in the request itself. It is possible that
+       there are none.
+    2. Please list any facts that may need to be looked up, and WHERE SPECIFICALLY they might be found.
+       In some cases, authoritative sources are mentioned in the request itself.
+    3. Please list any facts that may need to be derived (e.g., via logical deduction, simulation, or computation)
+    4. Please list any facts that are recalled from memory, hunches, well-reasoned guesses, etc.
+
+When answering this survey, keep in mind that "facts" will typically be specific names, dates, statistics, etc.
+Your answer should use headings:
+
+    1. GIVEN OR VERIFIED FACTS
+    2. FACTS TO LOOK UP
+    3. FACTS TO DERIVE
+    4. EDUCATED GUESSES
+
+DO NOT include any other headings or sections in your response. DO NOT list next steps or plans until asked to do so.
+"""
+
+ORCHESTRATOR_TASK_LEDGER_PLAN_PROMPT = """Fantastic. To address this request we have assembled the following team:
+
+{{$team}}
+
+Based on the team composition, and known and unknown facts, please devise a short bullet-point plan for addressing the
+original request. Remember, there is no requirement to involve all team members -- a team member's particular expertise
+may not be needed for this task.
+"""
+
+ORCHESTRATOR_TASK_LEDGER_FULL_PROMPT = """
+We are working to address the following user request:
+
+{{$task}}
+
+
+To answer this request we have assembled the following team:
+
+{{$team}}
+
+
+Here is an initial fact sheet to consider:
+
+{{$facts}}
+
+
+Here is the plan to follow as best as possible:
+
+{{$plan}}
+"""
+
+ORCHESTRATOR_PROGRESS_LEDGER_PROMPT = """
+Recall we are working on the following request:
+
+{{$task}}
+
+And we have assembled the following team:
+
+{{$team}}
+
+To make progress on the request, please answer the following questions, including necessary reasoning:
+
+    - Is the request fully satisfied? (True if complete, or False if the original request has yet to be
+      SUCCESSFULLY and FULLY addressed)
+    - Are we in a loop where we are repeating the same requests and / or getting the same responses as before?
+      Loops can span multiple turns, and can include repeated actions like scrolling up or down more than a
+      handful of times.
+    - Are we making forward progress? (True if just starting, or recent messages are adding value. False if recent
+      messages show evidence of being stuck in a loop or if there is evidence of significant barriers to success
+      such as the inability to read from a required file)
+    - Who should speak next? (select from: {{$names}})
+    - What instruction or question would you give this team member? (Phrase as if speaking directly to them, and
+      include any specific information they may need)
+
+Please output an answer in pure JSON format according to the following schema. The JSON object must be parsable as-is.
+DO NOT OUTPUT ANYTHING OTHER THAN JSON, AND DO NOT DEVIATE FROM THIS SCHEMA:
+
+{
+    "is_request_satisfied": {
+        "reason": string,
+        "answer": boolean
+    },
+    "is_in_loop": {
+        "reason": string,
+        "answer": boolean
+    },
+    "is_progress_being_made": {
+        "reason": string,
+        "answer": boolean
+    },
+    "next_speaker": {
+        "reason": string,
+        "answer": string (select from: {{$names}})
+    },
+    "instruction_or_question": {
+        "reason": string,
+        "answer": string
+    }
+}
+"""
+
+ORCHESTRATOR_TASK_LEDGER_FACTS_UPDATE_PROMPT = """As a reminder, we are working to solve the following task:
+
+{{$task}}
+
+It's clear we aren't making as much progress as we would like, but we may have learned something new.
+Please rewrite the following fact sheet, updating it to include anything new we have learned that may be helpful.
+
+Example edits can include (but are not limited to) adding new guesses, moving educated guesses to verified facts
+if appropriate, etc. Updates may be made to any section of the fact sheet, and more than one section of the fact
+sheet can be edited. This is an especially good time to update educated guesses, so please at least add or update
+one educated guess or hunch, and explain your reasoning.
+
+Here is the old fact sheet:
+
+{{$old_facts}}
+"""
+
+
+ORCHESTRATOR_TASK_LEDGER_PLAN_UPDATE_PROMPT = """Please briefly explain what went wrong on this last run (the root
+cause of the failure), and then come up with a new plan that takes steps and/or includes hints to overcome prior
+challenges and especially avoids repeating the same mistakes. As before, the new plan should be concise, be expressed
+in bullet-point form, and consider the following team composition (do not involve any other outside people since we
+cannot contact anyone else):
+
+{{$team}}
+"""
+
+ORCHESTRATOR_FINAL_ANSWER_PROMPT = """
+We are working on the following task:
+{{$task}}
+
+We have completed the task.
+
+The above messages contain the conversation that took place to complete the task.
+
+Based on the information gathered, provide the final answer to the original request.
+The answer should be phrased as if you were speaking to the user.
+"""

python/semantic_kernel/agents/orchestration/sequential.py

@@ -0,0 +1,205 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+from collections.abc import Awaitable, Callable
+
+from semantic_kernel.agents.agent import Agent
+from semantic_kernel.agents.orchestration.agent_actor_base import ActorBase, AgentActorBase
+from semantic_kernel.agents.orchestration.orchestration_base import DefaultTypeAlias, OrchestrationBase, TIn, TOut
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.routed_agent import message_handler
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class SequentialRequestMessage(KernelBaseModel):
+    """A request message type for sequential agents."""
+
+    body: DefaultTypeAlias
+
+
+@experimental
+class SequentialResultMessage(KernelBaseModel):
+    """A result message type for sequential agents."""
+
+    body: ChatMessageContent
+
+
+@experimental
+class SequentialAgentActor(AgentActorBase):
+    """A agent actor for sequential agents that process tasks."""
+
+    def __init__(
+        self,
+        agent: Agent,
+        internal_topic_type: str,
+        next_agent_type: str,
+        exception_callback: Callable[[BaseException], None],
+        agent_response_callback: Callable[[DefaultTypeAlias], Awaitable[None] | None] | None = None,
+        streaming_agent_response_callback: Callable[[StreamingChatMessageContent, bool], Awaitable[None] | None]
+        | None = None,
+    ) -> None:
+        """Initialize the agent actor."""
+        self._next_agent_type = next_agent_type
+        super().__init__(
+            agent=agent,
+            internal_topic_type=internal_topic_type,
+            exception_callback=exception_callback,
+            agent_response_callback=agent_response_callback,
+            streaming_agent_response_callback=streaming_agent_response_callback,
+        )
+
+    @message_handler
+    async def _handle_message(self, message: SequentialRequestMessage, ctx: MessageContext) -> None:
+        """Handle a message."""
+        logger.debug(f"Sequential actor (Actor ID: {self.id}; Agent name: {self._agent.name}) started processing...")
+
+        response = await self._invoke_agent(additional_messages=message.body)
+
+        logger.debug(f"Sequential actor (Actor ID: {self.id}; Agent name: {self._agent.name}) finished processing.")
+
+        target_actor_id = await self.runtime.get(self._next_agent_type)
+        await self.send_message(
+            SequentialRequestMessage(body=response),
+            target_actor_id,
+            cancellation_token=ctx.cancellation_token,
+        )
+
+
+@experimental
+class CollectionActor(ActorBase):
+    """A agent container for collection results from the last agent in the sequence."""
+
+    def __init__(
+        self,
+        description: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Initialize the collection actor."""
+        self._result_callback = result_callback
+
+        super().__init__(description, exception_callback)
+
+    @message_handler
+    async def _handle_message(self, message: SequentialRequestMessage, _: MessageContext) -> None:
+        """Handle the last message."""
+        await self._result_callback(message.body)
+
+
+@experimental
+class SequentialOrchestration(OrchestrationBase[TIn, TOut]):
+    """A sequential multi-agent pattern orchestration."""
+
+    @override
+    async def _start(
+        self,
+        task: DefaultTypeAlias,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        cancellation_token: CancellationToken,
+    ) -> None:
+        """Start the sequential pattern."""
+        target_actor_id = await runtime.get(self._get_agent_actor_type(self._members[0], internal_topic_type))
+        await runtime.send_message(
+            SequentialRequestMessage(body=task),
+            target_actor_id,
+            cancellation_token=cancellation_token,
+        )
+
+    @override
+    async def _prepare(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Register the actors and orchestrations with the runtime and add the required subscriptions."""
+        await self._register_members(runtime, internal_topic_type, exception_callback)
+        await self._register_collection_actor(runtime, internal_topic_type, exception_callback, result_callback)
+
+    async def _register_members(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+    ) -> None:
+        """Register the members.
+
+        The members will be registered in the reverse order so that the actor type of the next worker
+        is available when the current worker is registered. This is important for the sequential
+        orchestration, where actors need to know its next actor type to send the message to.
+
+        Args:
+            runtime (CoreRuntime): The agent runtime.
+            internal_topic_type (str): The internal topic type for the orchestration that this actor is part of.
+            exception_callback (Callable[[BaseException], None]): A callback function to handle exceptions.
+
+        Returns:
+            str: The first actor type in the sequence.
+        """
+        next_actor_type = self._get_collection_actor_type(internal_topic_type)
+        for agent in reversed(self._members):
+            await SequentialAgentActor.register(
+                runtime,
+                self._get_agent_actor_type(agent, internal_topic_type),
+                lambda agent=agent, next_actor_type=next_actor_type: SequentialAgentActor(  # type: ignore[misc]
+                    agent,
+                    internal_topic_type,
+                    next_agent_type=next_actor_type,
+                    exception_callback=exception_callback,
+                    agent_response_callback=self._agent_response_callback,
+                    streaming_agent_response_callback=self._streaming_agent_response_callback,
+                ),
+            )
+            logger.debug(f"Registered agent actor of type {self._get_agent_actor_type(agent, internal_topic_type)}")
+            next_actor_type = self._get_agent_actor_type(agent, internal_topic_type)
+
+    async def _register_collection_actor(
+        self,
+        runtime: CoreRuntime,
+        internal_topic_type: str,
+        exception_callback: Callable[[BaseException], None],
+        result_callback: Callable[[DefaultTypeAlias], Awaitable[None]],
+    ) -> None:
+        """Register the collection actor."""
+        await CollectionActor.register(
+            runtime,
+            self._get_collection_actor_type(internal_topic_type),
+            lambda: CollectionActor(
+                description="An internal agent that is responsible for collection results",
+                exception_callback=exception_callback,
+                result_callback=result_callback,
+            ),
+        )
+
+    def _get_agent_actor_type(self, agent: Agent, internal_topic_type: str) -> str:
+        """Get the actor type for an agent.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{agent.name}_{internal_topic_type}"
+
+    def _get_collection_actor_type(self, internal_topic_type: str) -> str:
+        """Get the collection actor type.
+
+        The type is appended with the internal topic type to ensure uniqueness in the runtime
+        that may be shared by multiple orchestrations.
+        """
+        return f"{CollectionActor.__name__}_{internal_topic_type}"

python/semantic_kernel/agents/orchestration/tools.py

@@ -0,0 +1,67 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+from collections.abc import Awaitable, Callable
+
+from pydantic import BaseModel
+
+from semantic_kernel.agents.orchestration.orchestration_base import DefaultTypeAlias
+from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+def structured_outputs_transform(
+    target_structure: type[BaseModel],
+    service: ChatCompletionClientBase,
+    prompt_execution_settings: PromptExecutionSettings | None = None,
+) -> Callable[[DefaultTypeAlias], Awaitable[BaseModel]]:
+    """Return a function that transforms the output of a chat completion service into a target structure.
+
+    Args:
+        target_structure (type): The target structure to transform the output into.
+        service (ChatCompletionClientBase): The chat completion service to use for the transformation. This service
+            must support structured output.
+        prompt_execution_settings (PromptExecutionSettings, optional): The settings to use for the prompt execution.
+
+    Returns:
+        Callable[[DefaultTypeAlias], Awaitable[BaseModel]]: A function that takes the output of
+            the chat completion service and transforms it into the target structure.
+    """
+    kernel = Kernel()
+    kernel.add_service(service)
+
+    settings = kernel.get_prompt_execution_settings_from_service_id(service.service_id)
+    if prompt_execution_settings:
+        settings.update_from_prompt_execution_settings(prompt_execution_settings)
+    if not hasattr(settings, "response_format"):
+        raise ValueError("The service must support structured output.")
+    settings.response_format = target_structure
+
+    chat_history = ChatHistory(
+        system_message=(
+            "Try your best to summarize the conversation into structured format:\n"
+            f"{target_structure.model_json_schema()}."
+        ),
+    )
+
+    async def output_transform(output: DefaultTypeAlias) -> BaseModel:
+        """Transform the output of the chat completion service into the target structure."""
+        if isinstance(output, ChatMessageContent):
+            chat_history.add_message(output)
+        elif isinstance(output, list) and all(isinstance(item, ChatMessageContent) for item in output):
+            for item in output:
+                chat_history.add_message(item)
+        else:
+            raise ValueError(f"Output must be {DefaultTypeAlias}.")
+
+        response = await service.get_chat_message_content(chat_history, settings)
+        assert response is not None  # nosec B101
+
+        return target_structure.model_validate_json(response.content)
+
+    return output_transform

python/semantic_kernel/agents/runtime/__init__.py

@@ -0,0 +1,33 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from semantic_kernel.agents.runtime.core.agent import Agent
+from semantic_kernel.agents.runtime.core.agent_id import AgentId, CoreAgentId
+from semantic_kernel.agents.runtime.core.agent_metadata import AgentMetadata, CoreAgentMetadata
+from semantic_kernel.agents.runtime.core.base_agent import BaseAgent
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.routed_agent import MessageHandler, RoutedAgent, message_handler
+from semantic_kernel.agents.runtime.core.subscription import Subscription
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.agents.runtime.in_process.default_subscription import DefaultSubscription
+from semantic_kernel.agents.runtime.in_process.in_process_runtime import InProcessRuntime
+from semantic_kernel.agents.runtime.in_process.type_subscription import TypeSubscription
+
+__all__ = [
+    "Agent",
+    "AgentId",
+    "AgentMetadata",
+    "BaseAgent",
+    "CoreAgentId",
+    "CoreAgentMetadata",
+    "CoreRuntime",
+    "DefaultSubscription",
+    "InProcessRuntime",
+    "MessageContext",
+    "MessageHandler",
+    "RoutedAgent",
+    "Subscription",
+    "TopicId",
+    "TypeSubscription",
+    "message_handler",
+]

python/semantic_kernel/agents/runtime/core/__init__.py

@@ -0,0 +1,18 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId, CoreAgentId
+from semantic_kernel.agents.runtime.core.agent_metadata import AgentMetadata, CoreAgentMetadata
+from semantic_kernel.agents.runtime.core.agent_type import AgentType, CoreAgentType
+from semantic_kernel.agents.runtime.core.base_agent import BaseAgent
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+
+__all__ = [
+    "AgentId",
+    "AgentMetadata",
+    "AgentType",
+    "BaseAgent",
+    "CoreAgentId",
+    "CoreAgentMetadata",
+    "CoreAgentType",
+    "CoreRuntime",
+]

python/semantic_kernel/agents/runtime/core/agent.py

@@ -0,0 +1,57 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Mapping
+from typing import Any, Protocol, runtime_checkable
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.agent_metadata import AgentMetadata
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+@runtime_checkable
+class Agent(Protocol):
+    """Protocol for an agent."""
+
+    @property
+    def metadata(self) -> AgentMetadata:
+        """Metadata of the agent."""
+        ...
+
+    @property
+    def id(self) -> AgentId:
+        """ID of the agent."""
+        ...
+
+    async def on_message(self, message: Any, ctx: MessageContext) -> Any:
+        """Message handler for the agent. This should only be called by the runtime, not by other agents.
+
+        Args:
+            message (Any): Received message. Type is one of the types in `subscriptions`.
+            ctx (MessageContext): Context of the message.
+
+        Returns:
+            Any: Response to the message. Can be None.
+
+        Raises:
+            asyncio.CancelledError: If the message was cancelled.
+            CantHandleException: If the agent cannot handle the message.
+        """
+        ...
+
+    async def save_state(self) -> Mapping[str, Any]:
+        """Save the state of the agent. The result must be JSON serializable."""
+        ...
+
+    async def load_state(self, state: Mapping[str, Any]) -> None:
+        """Load in the state of the agent obtained from `save_state`.
+
+        Args:
+            state (Mapping[str, Any]): State of the agent. Must be JSON serializable.
+        """
+        ...
+
+    async def close(self) -> None:
+        """Called when the runtime is closed."""
+        ...

python/semantic_kernel/agents/runtime/core/agent_id.py

@@ -0,0 +1,102 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import sys
+from typing import Protocol, runtime_checkable
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version < "3.11":
+    from typing_extensions import Self  # pragma: no cover
+else:
+    from typing import Self  # type: ignore # pragma: no cover
+
+from semantic_kernel.agents.runtime.core.agent_type import AgentType
+from semantic_kernel.agents.runtime.core.validation_utils import is_valid_agent_type
+
+
+@experimental
+@runtime_checkable
+class AgentId(Protocol):
+    """Defines the minimal interface an AgentId.
+
+    It must fulfill a 'type' and a 'key' that identify the agent instance.
+    """
+
+    @property
+    def type(self) -> str:
+        """Defines the 'type' or category of the agent."""
+        ...
+
+    @property
+    def key(self) -> str:
+        """Defines the unique instance key within the agent type."""
+        ...
+
+    def __eq__(self, other: object) -> bool:
+        """Equality check must differentiate between different IDs."""
+        ...
+
+    def __hash__(self) -> int:
+        """Hash value needed to store AgentIds in sets/dicts."""
+        ...
+
+    def __str__(self) -> str:
+        """String representation of the AgentId, e.g. 'type/key'."""
+        ...
+
+
+@experimental
+class CoreAgentId(AgentId):
+    """Core implementation of the AgentId protocol."""
+
+    def __init__(self, type: str | AgentType, key: str) -> None:
+        """Initialize the AgentId with the given type and key."""
+        # If `type` is itself an AgentType, extract the string property.
+        if isinstance(type, AgentType):
+            type = type.type
+
+        if not is_valid_agent_type(type):
+            raise ValueError(
+                rf"Invalid agent type: {type}. "
+                r"Allowed values MUST match the regex: `^[\w\-\.]+\Z`"
+            )
+
+        self._type = type
+        self._key = key
+
+    @classmethod
+    def from_str(cls, agent_id: str) -> Self:
+        """Convert a string of the format ``type/key`` into a CoreAgentId."""
+        items = agent_id.split("/", maxsplit=1)
+        if len(items) != 2:
+            raise ValueError(f"Invalid agent id: {agent_id}")
+        t, k = items[0], items[1]
+        return cls(t, k)
+
+    @property
+    def type(self) -> str:
+        r"""The agent's 'type' (or category). Must match `^[\\w\\-\\.]+$`."""
+        return self._type
+
+    @property
+    def key(self) -> str:
+        """The agent's instance key, e.g. 'default' or a unique identifier."""
+        return self._key
+
+    def __eq__(self, value: object) -> bool:
+        """Check if two AgentIds are equal by comparing 'type' and 'key'."""
+        if not isinstance(value, AgentId):
+            return False
+        return (self.type == value.type) and (self.key == value.key)
+
+    def __hash__(self) -> int:
+        """Generate a hash so we can store AgentIds in sets/dicts."""
+        return hash((self._type, self._key))
+
+    def __str__(self) -> str:
+        """Convert the AgentId to a user-friendly string."""
+        return f"{self._type}/{self._key}"
+
+    def __repr__(self) -> str:
+        """Generate a detailed string representation."""
+        return f'CoreAgentId(type="{self._type}", key="{self._key}")'

python/semantic_kernel/agents/runtime/core/agent_metadata.py

@@ -0,0 +1,56 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import Protocol, runtime_checkable
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+@runtime_checkable
+class AgentMetadata(Protocol):
+    """Provides a description for an agent: type, key, and an optional 'description' field."""
+
+    @property
+    def type(self) -> str:
+        """Defines the 'type' or category of the agent."""
+        ...
+
+    @property
+    def key(self) -> str:
+        """Defines the 'key' or identifier of the agent."""
+        ...
+
+    @property
+    def description(self) -> str:
+        """Defines the 'description' of the agent."""
+        ...
+
+
+@experimental
+class CoreAgentMetadata(AgentMetadata):
+    """Concrete immutable implementation of AgentMetadata."""
+
+    _type: str
+    _key: str
+    _description: str
+
+    def __init__(self, type: str, key: str, description: str = "") -> None:
+        """Initialize the agent metadata."""
+        self._type = type
+        self._key = key
+        self._description = description
+
+    @property
+    def type(self) -> str:
+        """Defines the 'type' or category of the agent."""
+        return self._type
+
+    @property
+    def key(self) -> str:
+        """Defines the 'key' or identifier of the agent."""
+        return self._key
+
+    @property
+    def description(self) -> str:
+        """Defines the 'description' of the agent."""
+        return self._description

python/semantic_kernel/agents/runtime/core/agent_type.py

@@ -0,0 +1,35 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+from dataclasses import dataclass
+from typing import Protocol, runtime_checkable
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+@runtime_checkable
+class AgentType(Protocol):
+    """Defines the minimal interface an AgentType."""
+
+    @property
+    def type(self) -> str:
+        """Defines the 'type' or category of the agent."""
+        ...
+
+
+@experimental
+@dataclass(eq=True, frozen=True)
+class CoreAgentType:
+    """Concrete immutable implementation of AgentType."""
+
+    _type: str
+
+    @property
+    def type(self) -> str:
+        """Defines the 'type' or category of the agent."""
+        return self._type
+
+    def __str__(self) -> str:
+        """Return the string representation of the agent type."""
+        return self._type

python/semantic_kernel/agents/runtime/core/base_agent.py

@@ -0,0 +1,221 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import inspect
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from typing import Any, ClassVar, TypeVar, final
+
+from typing_extensions import Self
+
+from semantic_kernel.agents.runtime.core.agent import Agent
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.agent_metadata import AgentMetadata, CoreAgentMetadata
+from semantic_kernel.agents.runtime.core.agent_type import AgentType, CoreAgentType
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.serialization import MessageSerializer, try_get_known_serializers_for_type
+from semantic_kernel.agents.runtime.core.subscription import Subscription, UnboundSubscription
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.agents.runtime.in_process.agent_instantiation_context import AgentInstantiationContext
+from semantic_kernel.agents.runtime.in_process.subscription_context import SubscriptionInstantiationContext
+from semantic_kernel.agents.runtime.in_process.type_prefix_subscription import TypePrefixSubscription
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+T = TypeVar("T", bound=Agent)
+
+BaseAgentType = TypeVar("BaseAgentType", bound="BaseAgent")
+
+
+# Decorator for adding an unbound subscription to an agent
+@experimental
+def subscription_factory(subscription: UnboundSubscription) -> Callable[[type[BaseAgentType]], type[BaseAgentType]]:
+    """Decorator for adding an unbound subscription to an agent."""
+
+    def decorator(cls: type[BaseAgentType]) -> type[BaseAgentType]:
+        cls.internal_unbound_subscriptions_list.append(subscription)
+        return cls
+
+    return decorator
+
+
+@experimental
+def handles(
+    msg_type: type[Any], serializer: MessageSerializer[Any] | list[MessageSerializer[Any]] | None = None
+) -> Callable[[type[BaseAgentType]], type[BaseAgentType]]:
+    """Decorator for associating a message type and corresponding serializer(s) with a BaseAgent or its subclass."""
+
+    def decorator(cls: type[BaseAgentType]) -> type[BaseAgentType]:
+        if serializer is None:
+            serializer_list = try_get_known_serializers_for_type(msg_type)
+        else:
+            serializer_list = [serializer] if not isinstance(serializer, Sequence) else list(serializer)
+
+        if not serializer_list:
+            raise ValueError(f"No serializers found for type {msg_type!r}. Please provide an explicit serializer.")
+
+        cls.internal_extra_handles_types.append((msg_type, serializer_list))
+        return cls
+
+    return decorator
+
+
+@experimental
+class BaseAgent(ABC, Agent):
+    """Base class for all agents."""
+
+    internal_unbound_subscriptions_list: ClassVar[list[UnboundSubscription]] = []
+    """:meta private:"""
+    internal_extra_handles_types: ClassVar[list[tuple[type[Any], list[MessageSerializer[Any]]]]] = []
+    """:meta private:"""
+
+    def __init_subclass__(cls, **kwargs: Any) -> None:
+        """Initialize the class."""
+        super().__init_subclass__(**kwargs)
+        # Automatically set class_variable in each subclass so that they are not shared between subclasses
+        cls.internal_extra_handles_types = []
+        cls.internal_unbound_subscriptions_list = []
+
+    @classmethod
+    def _handles_types(cls) -> list[tuple[type[Any], list[MessageSerializer[Any]]]]:
+        return cls.internal_extra_handles_types
+
+    @classmethod
+    def _unbound_subscriptions(cls) -> list[UnboundSubscription]:
+        return cls.internal_unbound_subscriptions_list
+
+    @property
+    def metadata(self) -> AgentMetadata:
+        """Get the metadata for this agent."""
+        assert self._id is not None  # nosec
+        return CoreAgentMetadata(key=self._id.key, type=self._id.type, description=self._description)
+
+    def __init__(self, description: str) -> None:
+        """Initialize the agent."""
+        try:
+            runtime = AgentInstantiationContext.current_runtime()
+            id = AgentInstantiationContext.current_agent_id()
+        except LookupError as e:
+            raise RuntimeError(
+                "BaseAgent must be instantiated within the context of an AgentRuntime. It cannot be directly "
+                "instantiated."
+            ) from e
+
+        self._runtime: CoreRuntime = runtime
+        self._id: AgentId = id
+        if not isinstance(description, str):
+            raise ValueError("Agent description must be a string")
+        self._description = description
+
+    @property
+    def type(self) -> str:
+        """Get the type of the agent."""
+        return self.id.type
+
+    @property
+    def id(self) -> AgentId:
+        """Get the id of the agent."""
+        return self._id
+
+    @property
+    def runtime(self) -> CoreRuntime:
+        """Get the runtime of the agent."""
+        return self._runtime
+
+    @final
+    async def on_message(self, message: Any, ctx: MessageContext) -> Any:
+        """Handle a message sent to this agent."""
+        return await self.on_message_impl(message, ctx)
+
+    @abstractmethod
+    async def on_message_impl(self, message: Any, ctx: MessageContext) -> Any:
+        """Handle a message sent to this agent."""
+        ...
+
+    async def send_message(
+        self,
+        message: Any,
+        recipient: AgentId,
+        *,
+        cancellation_token: CancellationToken | None = None,
+        message_id: str | None = None,
+    ) -> Any:
+        """Send a message to another agent."""
+        if cancellation_token is None:
+            cancellation_token = CancellationToken()
+
+        return await self._runtime.send_message(
+            message,
+            sender=self.id,
+            recipient=recipient,
+            cancellation_token=cancellation_token,
+            message_id=message_id,
+        )
+
+    async def publish_message(
+        self,
+        message: Any,
+        topic_id: TopicId,
+        *,
+        cancellation_token: CancellationToken | None = None,
+    ) -> None:
+        """Publish a message."""
+        await self._runtime.publish_message(message, topic_id, sender=self.id, cancellation_token=cancellation_token)
+
+    async def save_state(self) -> Mapping[str, Any]:
+        """Save the state of the agent."""
+        warnings.warn("save_state not implemented", stacklevel=2)
+        return {}
+
+    async def load_state(self, state: Mapping[str, Any]) -> None:
+        """Load the state of the agent."""
+        warnings.warn("load_state not implemented", stacklevel=2)
+        pass
+
+    async def close(self) -> None:
+        """Close the agent."""
+        pass
+
+    @classmethod
+    async def register(
+        cls,
+        runtime: CoreRuntime,
+        type: str,
+        factory: Callable[[], Self | Awaitable[Self]],
+        *,
+        skip_class_subscriptions: bool = False,
+        skip_direct_message_subscription: bool = False,
+    ) -> AgentType:
+        """Register the agent with the runtime."""
+        agent_type = CoreAgentType(type)
+        agent_type = await runtime.register_factory(type=agent_type, agent_factory=factory, expected_class=cls)  # type: ignore
+        if not skip_class_subscriptions:
+            with SubscriptionInstantiationContext.populate_context(agent_type):
+                subscriptions: list[Subscription] = []
+                for unbound_subscription in cls._unbound_subscriptions():
+                    subscriptions_list_result = unbound_subscription()
+                    if inspect.isawaitable(subscriptions_list_result):
+                        subscriptions_list = await subscriptions_list_result
+                    else:
+                        subscriptions_list = subscriptions_list_result
+
+                    subscriptions.extend(subscriptions_list)
+            for subscription in subscriptions:
+                await runtime.add_subscription(subscription)
+
+        if not skip_direct_message_subscription:
+            # Additionally adds a special prefix subscription for this agent to receive direct messages
+            await runtime.add_subscription(
+                TypePrefixSubscription(
+                    # The prefix MUST include ":" to avoid collisions with other agents
+                    topic_type_prefix=agent_type.type + ":",
+                    agent_type=agent_type.type,
+                )
+            )
+
+        # TODO(evmattso): deduplication
+        for _message_type, serializer in cls._handles_types():
+            runtime.add_message_serializer(serializer)
+
+        return agent_type

python/semantic_kernel/agents/runtime/core/cancellation_token.py

@@ -0,0 +1,53 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import threading
+from asyncio import Future
+from collections.abc import Callable
+from typing import Any
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class CancellationToken:
+    """A token used to cancel pending async calls."""
+
+    def __init__(self) -> None:
+        """Initialize the CancellationToken."""
+        self._cancelled: bool = False
+        self._lock: threading.Lock = threading.Lock()
+        self._callbacks: list[Callable[[], None]] = []
+
+    def cancel(self) -> None:
+        """Cancel pending async calls linked to this cancellation token."""
+        with self._lock:
+            if not self._cancelled:
+                self._cancelled = True
+                for callback in self._callbacks:
+                    callback()
+
+    def is_cancelled(self) -> bool:
+        """Check if the CancellationToken has been used."""
+        with self._lock:
+            return self._cancelled
+
+    def add_callback(self, callback: Callable[[], None]) -> None:
+        """Attach a callback that will be called when cancel is invoked."""
+        with self._lock:
+            if self._cancelled:
+                callback()
+            else:
+                self._callbacks.append(callback)
+
+    def link_future(self, future: Future[Any]) -> Future[Any]:
+        """Link a pending async call to a token to allow its cancellation."""
+        with self._lock:
+            if self._cancelled:
+                future.cancel()
+            else:
+
+                def _cancel() -> None:
+                    future.cancel()
+
+                self._callbacks.append(_cancel)
+        return future

python/semantic_kernel/agents/runtime/core/core_runtime.py

@@ -0,0 +1,204 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from typing import Any, Protocol, TypeVar, overload, runtime_checkable
+
+from semantic_kernel.agents.runtime.core.agent import Agent
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.agent_metadata import AgentMetadata
+from semantic_kernel.agents.runtime.core.agent_type import AgentType
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.serialization import MessageSerializer
+from semantic_kernel.agents.runtime.core.subscription import Subscription
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+# Undeliverable - error
+
+T = TypeVar("T", bound=Agent)
+
+
+@experimental
+@runtime_checkable
+class CoreRuntime(Protocol):
+    """CoreRuntime is the main entry point for the agent runtime.
+
+    It is responsible for managing agents and their interactions.
+    """
+
+    async def send_message(
+        self,
+        message: Any,
+        recipient: AgentId,
+        *,
+        sender: AgentId | None = None,
+        cancellation_token: CancellationToken | None = None,
+        message_id: str | None = None,
+    ) -> Any:
+        """Send a message to an agent and get a response.
+
+        Args:
+            message (Any): The message to send.
+            recipient (AgentId): The agent to send the message to.
+            sender (AgentId | None, optional): Agent which sent the message. Should **only** be None if this was sent
+                from no agent, such as directly to the runtime externally. Defaults to None.
+            cancellation_token (CancellationToken | None, optional): Token used to cancel an in progress.
+                Defaults to None.
+            message_id (str | None, optional): The message id. If None, a new message id will be generated.
+
+        Raises:
+            CantHandleException: If the recipient cannot handle the message.
+            UndeliverableException: If the message cannot be delivered.
+            Other: Any other exception raised by the recipient.
+
+        Returns:
+            Any: The response from the agent.
+        """
+        ...
+
+    async def publish_message(
+        self,
+        message: Any,
+        topic_id: TopicId,
+        *,
+        sender: AgentId | None = None,
+        cancellation_token: CancellationToken | None = None,
+        message_id: str | None = None,
+    ) -> None:
+        """Publish a message to all agents in the given namespace.
+
+        If no namespace is provided, the namespace of the sender. No responses are expected from publishing.
+
+        Args:
+            message (Any): The message to publish.
+            topic_id (TopicId): The topic to publish the message to.
+            sender (AgentId | None, optional): The agent which sent the message. Defaults to None.
+            cancellation_token (CancellationToken | None, optional): Token used to cancel an in progress.
+                Defaults to None.
+            message_id (str | None, optional): The message id. If None, a new message id will be generated.
+                Defaults to None. This message id must be unique. and is recommended to be a UUID.
+
+        Raises:
+            UndeliverableException: If the message cannot be delivered.
+        """
+        ...
+
+    async def register_factory(
+        self,
+        type: str | AgentType,
+        agent_factory: Callable[[], T | Awaitable[T]],
+        *,
+        expected_class: type[T] | None = None,
+    ) -> AgentType:
+        """Register an agent factory with the runtime associated with a specific type. The type must be unique.
+
+        This API does not add any subscriptions.
+
+
+        Args:
+            type (str): The type of agent this factory creates. It is not the same as agent class name.
+                The `type` parameter is used to differentiate between different factory functions rather than
+                agent classes.
+            agent_factory (Callable[[], T]): The factory that creates the agent, where T is a concrete Agent type.
+                Inside the factory, use `agent_runtime.AgentInstantiationContext` to access variables like the current
+                runtime and agent ID.
+            expected_class (type[T] | None, optional): The expected class of the agent, used for runtime validation
+                of the factory. Defaults to None. If None, no validation is performed.
+        """
+        ...
+
+    # TODO(evmattso): uncomment out the following type ignore when this is fixed in mypy: https://github.com/python/mypy/issues/3737
+    async def try_get_underlying_agent_instance(self, id: AgentId, type: type[T] = Agent) -> T:  # type: ignore[assignment]
+        """Try to get the underlying agent instance by name and namespace.
+
+        This is generally discouraged (hence the long name), but can be useful in some cases.
+
+        If the underlying agent is not accessible, this will raise an exception.
+
+        Args:
+            id (AgentId): The agent id.
+            type (Type[T], optional): The expected type of the agent. Defaults to Agent.
+
+        Returns:
+            T: The concrete agent instance.
+
+        Raises:
+            LookupError: If the agent is not found.
+            NotAccessibleError: If the agent is not accessible, for example if it is located remotely.
+            TypeError: If the agent is not of the expected type.
+        """
+        ...
+
+    @overload
+    async def get(self, id: AgentId, /, *, lazy: bool = ...) -> AgentId: ...
+
+    @overload
+    async def get(self, type: AgentType | str, /, key: str = ..., *, lazy: bool = ...) -> AgentId: ...
+
+    async def get(
+        self, id_or_type: AgentId | AgentType | str, /, key: str = "default", *, lazy: bool = True
+    ) -> AgentId:
+        """Get an agent by id or type."""
+        ...
+
+    async def agent_metadata(self, agent: AgentId) -> AgentMetadata:
+        """Get the metadata for an agent.
+
+        Args:
+            agent (AgentId): The agent id.
+
+        Returns:
+            AgentMetadata: The agent metadata.
+        """
+        ...
+
+    async def agent_save_state(self, agent: AgentId) -> Mapping[str, Any]:
+        """Save the state of a single agent.
+
+        The structure of the state is implementation defined and can be any JSON serializable object.
+
+        Args:
+            agent (AgentId): The agent id.
+
+        Returns:
+            Mapping[str, Any]: The saved state.
+        """
+        ...
+
+    async def agent_load_state(self, agent: AgentId, state: Mapping[str, Any]) -> None:
+        """Load the state of a single agent.
+
+        Args:
+            agent (AgentId): The agent id.
+            state (Mapping[str, Any]): The saved state.
+        """
+        ...
+
+    async def add_subscription(self, subscription: Subscription) -> None:
+        """Add a new subscription that the runtime should fulfill when processing published messages.
+
+        Args:
+            subscription (Subscription): The subscription to add
+        """
+        ...
+
+    async def remove_subscription(self, id: str) -> None:
+        """Remove a subscription from the runtime.
+
+        Args:
+            id (str): id of the subscription to remove
+
+        Raises:
+            LookupError: If the subscription does not exist
+        """
+        ...
+
+    def add_message_serializer(self, serializer: MessageSerializer[Any] | Sequence[MessageSerializer[Any]]) -> None:
+        """Add a new message serialization serializer to the runtime.
+
+        Note: This will deduplicate serializers based on the type_name and data_content_type properties
+
+        Args:
+            serializer (MessageSerializer[Any] | Sequence[MessageSerializer[Any]]): The serializer/s to add
+        """
+        ...

python/semantic_kernel/agents/runtime/core/exceptions.py

@@ -0,0 +1,25 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+__all__ = ["CantHandleException", "MessageDroppedException", "NotAccessibleError", "UndeliverableException"]
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class CantHandleException(Exception):
+    """Raised when a handler can't handle the exception."""
+
+
+@experimental
+class UndeliverableException(Exception):
+    """Raised when a message can't be delivered."""
+
+
+@experimental
+class MessageDroppedException(Exception):
+    """Raised when a message is dropped."""
+
+
+@experimental
+class NotAccessibleError(Exception):
+    """Tried to access a value that is not accessible. For example if it is remote cannot be accessed locally."""

python/semantic_kernel/agents/runtime/core/intervention.py

@@ -0,0 +1,76 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import Any, Protocol, final
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+__all__ = [
+    "DefaultInterventionHandler",
+    "DropMessage",
+    "InterventionHandler",
+]
+
+
+@experimental
+@final
+class DropMessage:
+    """Marker type for signalling that a message should be dropped by an intervention handler.
+
+    The type itself should be returned from the handler.
+    """
+
+    ...
+
+
+@experimental
+class InterventionHandler(Protocol):
+    """An intervention handler is a class that can be used to modify, log or drop messages.
+
+    These messages are being processed by the :class:`autogen_core.base.AgentRuntime`.
+
+    The handler is called when the message is submitted to the runtime.
+
+    Currently the only runtime which supports this is the :class:`autogen_core.base.SingleThreadedAgentRuntime`.
+
+    Note: Returning None from any of the intervention handler methods will result in a warning being issued and treated
+    as "no change". If you intend to drop a message, you should return :class:`DropMessage` explicitly.
+    """
+
+    async def on_send(
+        self, message: Any, *, message_context: MessageContext, recipient: AgentId
+    ) -> Any | type[DropMessage]:
+        """Called when a message is submitted to the AgentRuntime."""
+        ...
+
+    async def on_publish(self, message: Any, *, message_context: MessageContext) -> Any | type[DropMessage]:
+        """Called when a message is published to the AgentRuntime."""
+        ...
+
+    async def on_response(self, message: Any, *, sender: AgentId, recipient: AgentId | None) -> Any | type[DropMessage]:
+        """Called when a response is received by the AgentRuntime from an Agent's message handler returning a value."""
+        ...
+
+
+@experimental
+class DefaultInterventionHandler(InterventionHandler):
+    """Simple class that provides a default implementation for all intervention handler methods.
+
+    Simply returns the message unchanged. Allows for easy
+    subclassing to override only the desired methods.
+    """
+
+    async def on_send(
+        self, message: Any, *, message_context: MessageContext, recipient: AgentId
+    ) -> Any | type[DropMessage]:
+        """Called when a message is submitted to the AgentRuntime."""
+        return message
+
+    async def on_publish(self, message: Any, *, message_context: MessageContext) -> Any | type[DropMessage]:
+        """Called when a message is published to the AgentRuntime."""
+        return message
+
+    async def on_response(self, message: Any, *, sender: AgentId, recipient: AgentId | None) -> Any | type[DropMessage]:
+        """Called when a response is received by the AgentRuntime from an Agent's message handler returning a value."""
+        return message

python/semantic_kernel/agents/runtime/core/logging.py

@@ -0,0 +1,130 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import json
+from enum import Enum
+from typing import Any
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class MessageKind(Enum):
+    """Message kind enum."""
+
+    DIRECT = 1
+    PUBLISH = 2
+    RESPOND = 3
+
+
+@experimental
+class DeliveryStage(Enum):
+    """Delivery stage enum."""
+
+    SEND = 1
+    DELIVER = 2
+
+
+@experimental
+class MessageEvent:
+    """Base class for message events."""
+
+    def __init__(
+        self,
+        *,
+        payload: str,
+        sender: AgentId | None,
+        receiver: AgentId | TopicId | None,
+        kind: MessageKind,
+        delivery_stage: DeliveryStage,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a message event."""
+        self.kwargs = kwargs
+        self.kwargs["payload"] = payload
+        self.kwargs["sender"] = None if sender is None else str(sender)
+        self.kwargs["receiver"] = None if receiver is None else str(receiver)
+        self.kwargs["kind"] = str(kind)
+        self.kwargs["delivery_stage"] = str(delivery_stage)
+        self.kwargs["type"] = "Message"
+
+    # This must output the event in a json serializable format
+    def __str__(self) -> str:
+        """Convert the event to a string."""
+        return json.dumps(self.kwargs)
+
+
+@experimental
+class MessageDroppedEvent:
+    """Event for dropped messages."""
+
+    def __init__(
+        self,
+        *,
+        payload: str,
+        sender: AgentId | None,
+        receiver: AgentId | TopicId | None,
+        kind: MessageKind,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a message dropped event."""
+        self.kwargs = kwargs
+        self.kwargs["payload"] = payload
+        self.kwargs["sender"] = None if sender is None else str(sender)
+        self.kwargs["receiver"] = None if receiver is None else str(receiver)
+        self.kwargs["kind"] = str(kind)
+        self.kwargs["type"] = "MessageDropped"
+
+    # This must output the event in a json serializable format
+    def __str__(self) -> str:
+        """Convert the event to a string."""
+        return json.dumps(self.kwargs)
+
+
+@experimental
+class MessageHandlerExceptionEvent:
+    """Event for exceptions in message handlers."""
+
+    def __init__(
+        self,
+        *,
+        payload: str,
+        handling_agent: AgentId,
+        exception: BaseException,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a message handler exception event."""
+        self.kwargs = kwargs
+        self.kwargs["payload"] = payload
+        self.kwargs["handling_agent"] = str(handling_agent)
+        self.kwargs["exception"] = str(exception)
+        self.kwargs["type"] = "MessageHandlerException"
+
+    # This must output the event in a json serializable format
+    def __str__(self) -> str:
+        """Convert the event to a string."""
+        return json.dumps(self.kwargs)
+
+
+@experimental
+class AgentConstructionExceptionEvent:
+    """Event for exceptions during agent construction."""
+
+    def __init__(
+        self,
+        *,
+        agent_id: AgentId,
+        exception: BaseException,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize an agent construction exception event."""
+        self.kwargs = kwargs
+        self.kwargs["agent_id"] = str(agent_id)
+        self.kwargs["exception"] = str(exception)
+        self.kwargs["type"] = "AgentConstructionException"
+
+    # This must output the event in a json serializable format
+    def __str__(self) -> str:
+        """Convert the event to a string."""
+        return json.dumps(self.kwargs)

python/semantic_kernel/agents/runtime/core/message_context.py

@@ -0,0 +1,20 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from dataclasses import dataclass
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+@dataclass
+class MessageContext:
+    """Context for a message sent to an agent."""
+
+    sender: AgentId | None
+    topic_id: TopicId | None
+    is_rpc: bool
+    cancellation_token: CancellationToken
+    message_id: str

python/semantic_kernel/agents/runtime/core/routed_agent.py

@@ -0,0 +1,530 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+from collections.abc import Callable, Coroutine, Sequence
+from functools import wraps
+from typing import Any, DefaultDict, Literal, Protocol, TypeVar, cast, get_type_hints, overload, runtime_checkable
+
+from semantic_kernel.agents.runtime.core.base_agent import BaseAgent
+from semantic_kernel.agents.runtime.core.exceptions import CantHandleException
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.serialization import MessageSerializer, try_get_known_serializers_for_type
+from semantic_kernel.agents.runtime.core.type_helpers import AnyType, get_types
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+logger = logging.getLogger("agent_runtime.core")
+
+AgentT = TypeVar("AgentT")
+ReceivesT = TypeVar("ReceivesT")
+ProducesT = TypeVar("ProducesT", covariant=True)
+
+# TODO(evmattso): Generic typevar bound binding U to agent type
+# Can't do because python doesnt support it
+
+# region MessageHandler Protocol and Methods
+
+
+# Pyright and mypy disagree on the variance of ReceivesT. Mypy thinks it should be contravariant here.
+# Revisit this later to see if we can remove the ignore.
+@experimental
+@runtime_checkable
+class MessageHandler(Protocol[AgentT, ReceivesT, ProducesT]):  # type: ignore
+    """A protocol for message handlers."""
+
+    target_types: Sequence[type]
+    produces_types: Sequence[type]
+    is_message_handler: Literal[True]
+    router: Callable[[ReceivesT, MessageContext], bool]
+
+    # agent_instance binds to self in the method
+    @staticmethod
+    async def __call__(agent_instance: AgentT, message: ReceivesT, ctx: MessageContext) -> ProducesT:
+        """Override the __call__ method to make this a callable class."""
+        ...
+
+
+# NOTE: this works on concrete types and not inheritance
+# TODO(evmattso): Use a protocol for the outer function to check checked arg names
+
+
+@experimental
+@overload
+def message_handler(
+    func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]],
+) -> MessageHandler[AgentT, ReceivesT, ProducesT]: ...
+
+
+@experimental
+@overload
+def message_handler(
+    func: None = None,
+    *,
+    match: None = ...,
+    strict: bool = ...,
+) -> Callable[
+    [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]]],
+    MessageHandler[AgentT, ReceivesT, ProducesT],
+]: ...
+
+
+@experimental
+@overload
+def message_handler(
+    func: None = None,
+    *,
+    match: Callable[[ReceivesT, MessageContext], bool],
+    strict: bool = ...,
+) -> Callable[
+    [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]]],
+    MessageHandler[AgentT, ReceivesT, ProducesT],
+]: ...
+
+
+@experimental
+def message_handler(
+    func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]] | None = None,
+    *,
+    strict: bool = True,
+    match: Callable[[ReceivesT, MessageContext], bool] | None = None,
+) -> (
+    Callable[
+        [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]]],
+        MessageHandler[AgentT, ReceivesT, ProducesT],
+    ]
+    | MessageHandler[AgentT, ReceivesT, ProducesT]
+):
+    """Decorator for generic message handlers.
+
+    Add this decorator to methods in a :class:`RoutedAgent` class that are intended to handle both
+    event and RPC messages.
+
+    These methods must have a specific signature that needs to be followed for it to be valid:
+
+    - The method must be an `async` method.
+    - The method must be decorated with the `@message_handler` decorator.
+    - The method must have exactly 3 arguments:
+        1. `self`
+        2. `message`: The message to be handled, this must be type-hinted with the message type that it is
+            intended to handle.
+        3. `ctx`: A :class:`agent_runtime.core.MessageContext` object.
+    - The method must be type hinted with what message types it can return as a response, or it can return `None` if
+    it does not return anything.
+
+    Handlers can handle more than one message type by accepting a Union of the message types. It can also return more
+    than one message type by returning a Union of the message types.
+
+    Args:
+        func: The function to be decorated.
+        strict: If `True`, the handler will raise an exception if the message type or return type is not in the target
+            types. If `False`, it will log a warning instead.
+        match: A function that takes the message and the context as arguments and returns a boolean. This is used for
+            secondary routing after the message type. For handlers addressing the same message type, the match function
+            is applied in alphabetical order of the handlers and the first matching handler will be called while the
+            rest are skipped. If `None`, the first handler in alphabetical order matching the same message type will
+            be called.
+    """
+
+    def decorator(
+        func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]],
+    ) -> MessageHandler[AgentT, ReceivesT, ProducesT]:
+        type_hints = get_type_hints(func)
+        if "message" not in type_hints:
+            raise AssertionError("message parameter not found in function signature")
+
+        if "return" not in type_hints:
+            raise AssertionError("return not found in function signature")
+
+        # Get the type of the message parameter
+        target_types = get_types(type_hints["message"])
+        if target_types is None:
+            raise AssertionError("Message type not found")
+
+        return_types = get_types(type_hints["return"])
+
+        if return_types is None:
+            raise AssertionError("Return type not found")
+
+        # Convert target_types to list and stash
+
+        @wraps(func)
+        async def wrapper(self: AgentT, message: ReceivesT, ctx: MessageContext) -> ProducesT:
+            if type(message) not in target_types:
+                if strict:
+                    raise CantHandleException(f"Message type {type(message)} not in target types {target_types}")
+                logger.warning(f"Message type {type(message)} not in target types {target_types}")
+
+            return_value = await func(self, message, ctx)
+
+            if AnyType not in return_types and type(return_value) not in return_types:
+                if strict:
+                    raise ValueError(f"Return type {type(return_value)} not in return types {return_types}")
+                logger.warning(f"Return type {type(return_value)} not in return types {return_types}")
+
+            return return_value
+
+        wrapper_handler = cast(MessageHandler[AgentT, ReceivesT, ProducesT], wrapper)
+        wrapper_handler.target_types = list(target_types)
+        wrapper_handler.produces_types = list(return_types)
+        wrapper_handler.is_message_handler = True
+        wrapper_handler.router = match or (lambda _message, _ctx: True)
+
+        return wrapper_handler
+
+    if func is None and not callable(func):
+        return decorator
+    if callable(func):
+        return decorator(func)
+    raise ValueError("Invalid arguments")
+
+
+# endregion
+
+# region Message Handler Decorators
+
+
+@experimental
+@overload
+def event(
+    func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, None]],
+) -> MessageHandler[AgentT, ReceivesT, None]: ...
+
+
+@experimental
+@overload
+def event(
+    func: None = None,
+    *,
+    match: None = ...,
+    strict: bool = ...,
+) -> Callable[
+    [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, None]]],
+    MessageHandler[AgentT, ReceivesT, None],
+]: ...
+
+
+@experimental
+@overload
+def event(
+    func: None = None,
+    *,
+    match: Callable[[ReceivesT, MessageContext], bool],
+    strict: bool = ...,
+) -> Callable[
+    [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, None]]],
+    MessageHandler[AgentT, ReceivesT, None],
+]: ...
+
+
+@experimental
+def event(
+    func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, None]] | None = None,
+    *,
+    strict: bool = True,
+    match: Callable[[ReceivesT, MessageContext], bool] | None = None,
+) -> (
+    Callable[
+        [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, None]]],
+        MessageHandler[AgentT, ReceivesT, None],
+    ]
+    | MessageHandler[AgentT, ReceivesT, None]
+):
+    """Decorator for event message handlers.
+
+    Add this decorator to methods in a :class:`RoutedAgent` class that are intended to handle event messages.
+    These methods must have a specific signature that needs to be followed for it to be valid:
+
+    - The method must be an `async` method.
+    - The method must be decorated with the `@message_handler` decorator.
+    - The method must have exactly 3 arguments:
+        1. `self`
+        2. `message`: The event message to be handled, this must be type-hinted with the message type that it is
+            intended to handle.
+        3. `ctx`: A :class:`agent_runtime.core.MessageContext` object.
+    - The method must return `None`.
+
+    Handlers can handle more than one message type by accepting a Union of the message types.
+
+    Args:
+        func: The function to be decorated.
+        strict: If `True`, the handler will raise an exception if the message type is not in the target types.
+            If `False`, it will log a warning instead.
+        match: A function that takes the message and the context as arguments and returns a boolean. This is used for
+            secondary routing after the message type. For handlers addressing the same message type, the match function
+            is applied in alphabetical order of the handlers and the first matching handler will be called while the
+            rest are skipped. If `None`, the first handler in alphabetical order matching the same message type will be
+            called.
+    """
+
+    def decorator(
+        func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, None]],
+    ) -> MessageHandler[AgentT, ReceivesT, None]:
+        type_hints = get_type_hints(func)
+        if "message" not in type_hints:
+            raise AssertionError("message parameter not found in function signature")
+
+        if "return" not in type_hints:
+            raise AssertionError("return not found in function signature")
+
+        # Get the type of the message parameter
+        target_types = get_types(type_hints["message"])
+        if target_types is None:
+            raise AssertionError("Message type not found. Please provide a type hint for the message parameter.")
+
+        return_types = get_types(type_hints["return"])
+
+        if return_types is None:
+            raise AssertionError("Return type not found. Please use `None` as the type hint of the return type.")
+
+        # Convert target_types to list and stash
+
+        @wraps(func)
+        async def wrapper(self: AgentT, message: ReceivesT, ctx: MessageContext) -> None:
+            if type(message) not in target_types:
+                if strict:
+                    raise CantHandleException(f"Message type {type(message)} not in target types {target_types}")
+                logger.warning(f"Message type {type(message)} not in target types {target_types}")
+
+            return_value = await func(self, message, ctx)  # type: ignore
+
+            if return_value is not None:
+                if strict:
+                    raise ValueError(f"Return type {type(return_value)} is not None.")
+                logger.warning(f"Return type {type(return_value)} is not None. It will be ignored.")
+
+            return
+
+        wrapper_handler = cast(MessageHandler[AgentT, ReceivesT, None], wrapper)
+        wrapper_handler.target_types = list(target_types)
+        wrapper_handler.produces_types = list(return_types)
+        wrapper_handler.is_message_handler = True
+        # Wrap the match function with a check on the is_rpc flag.
+        wrapper_handler.router = lambda _message, _ctx: (not _ctx.is_rpc) and (match(_message, _ctx) if match else True)
+
+        return wrapper_handler
+
+    if func is None and not callable(func):
+        return decorator
+    if callable(func):
+        return decorator(func)
+    raise ValueError("Invalid arguments")
+
+
+@experimental
+@overload
+def rpc(
+    func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]],
+) -> MessageHandler[AgentT, ReceivesT, ProducesT]: ...
+
+
+@experimental
+@overload
+def rpc(
+    func: None = None,
+    *,
+    match: None = ...,
+    strict: bool = ...,
+) -> Callable[
+    [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]]],
+    MessageHandler[AgentT, ReceivesT, ProducesT],
+]: ...
+
+
+@experimental
+@overload
+def rpc(
+    func: None = None,
+    *,
+    match: Callable[[ReceivesT, MessageContext], bool],
+    strict: bool = ...,
+) -> Callable[
+    [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]]],
+    MessageHandler[AgentT, ReceivesT, ProducesT],
+]: ...
+
+
+@experimental
+def rpc(
+    func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]] | None = None,
+    *,
+    strict: bool = True,
+    match: Callable[[ReceivesT, MessageContext], bool] | None = None,
+) -> (
+    Callable[
+        [Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]]],
+        MessageHandler[AgentT, ReceivesT, ProducesT],
+    ]
+    | MessageHandler[AgentT, ReceivesT, ProducesT]
+):
+    """Decorator for RPC message handlers.
+
+    Add this decorator to methods in a :class:`RoutedAgent` class that are intended to handle RPC messages.
+    These methods must have a specific signature that needs to be followed for it to be valid:
+
+    - The method must be an `async` method.
+    - The method must be decorated with the `@message_handler` decorator.
+    - The method must have exactly 3 arguments:
+        1. `self`
+        2. `message`: The message to be handled, this must be type-hinted with the message type that it is intended to
+            handle.
+        3. `ctx`: A :class:`agent_runtime.core.MessageContext` object.
+    - The method must be type hinted with what message types it can return as a response, or it can return `None` if
+        it does not return anything.
+
+    Handlers can handle more than one message type by accepting a Union of the message types. It can also return more
+    than one message type by returning a Union of the message types.
+
+    Args:
+        func: The function to be decorated.
+        strict: If `True`, the handler will raise an exception if the message type or return type is not in the target
+            types. If `False`, it will log a warning instead.
+        match: A function that takes the message and the context as arguments and returns a boolean. This is used for
+            secondary routing after the message type. For handlers addressing the same message type, the match function
+            is applied in alphabetical order of the handlers and the first matching handler will be called while the
+            rest are skipped. If `None`, the first handler in alphabetical order matching the same message type will be
+            called.
+    """
+
+    def decorator(
+        func: Callable[[AgentT, ReceivesT, MessageContext], Coroutine[Any, Any, ProducesT]],
+    ) -> MessageHandler[AgentT, ReceivesT, ProducesT]:
+        type_hints = get_type_hints(func)
+        if "message" not in type_hints:
+            raise AssertionError("message parameter not found in function signature")
+
+        if "return" not in type_hints:
+            raise AssertionError("return not found in function signature")
+
+        # Get the type of the message parameter
+        target_types = get_types(type_hints["message"])
+        if target_types is None:
+            raise AssertionError("Message type not found")
+
+        return_types = get_types(type_hints["return"])
+
+        if return_types is None:
+            raise AssertionError("Return type not found")
+
+        # Convert target_types to list and stash
+
+        @wraps(func)
+        async def wrapper(self: AgentT, message: ReceivesT, ctx: MessageContext) -> ProducesT:
+            if type(message) not in target_types:
+                if strict:
+                    raise CantHandleException(f"Message type {type(message)} not in target types {target_types}")
+                logger.warning(f"Message type {type(message)} not in target types {target_types}")
+
+            return_value = await func(self, message, ctx)
+
+            if AnyType not in return_types and type(return_value) not in return_types:
+                if strict:
+                    raise ValueError(f"Return type {type(return_value)} not in return types {return_types}")
+                logger.warning(f"Return type {type(return_value)} not in return types {return_types}")
+
+            return return_value
+
+        wrapper_handler = cast(MessageHandler[AgentT, ReceivesT, ProducesT], wrapper)
+        wrapper_handler.target_types = list(target_types)
+        wrapper_handler.produces_types = list(return_types)
+        wrapper_handler.is_message_handler = True
+        wrapper_handler.router = lambda _message, _ctx: (_ctx.is_rpc) and (match(_message, _ctx) if match else True)
+
+        return wrapper_handler
+
+    if func is None and not callable(func):
+        return decorator
+    if callable(func):
+        return decorator(func)
+    raise ValueError("Invalid arguments")
+
+
+# endregion
+
+
+# region RoutedAgent
+
+
+@experimental
+class RoutedAgent(BaseAgent):
+    """A base class for agents that route messages to handlers.
+
+    Messages are routed based on the type of the message and optional matching functions.
+
+    To create a routed agent, subclass this class and add message handlers as methods decorated with
+    either :func:`event` or :func:`rpc` decorator.
+    """
+
+    def __init__(self, description: str) -> None:
+        """Initialize the routed agent.
+
+        Args:
+            description: The description of the agent.
+        """
+        # Self is already bound to the handlers
+        self._handlers: DefaultDict[
+            type[Any],
+            list[MessageHandler[RoutedAgent, Any, Any]],
+        ] = DefaultDict(list)
+
+        handlers = self._discover_handlers()
+        for message_handler in handlers:
+            for target_type in message_handler.target_types:
+                self._handlers[target_type].append(message_handler)
+
+        super().__init__(description)
+
+    async def on_message_impl(self, message: Any, ctx: MessageContext) -> Any | None:
+        """Handle a message by routing it to the appropriate message handler.
+
+        Do not override this method in subclasses. Instead, add message handlers as methods decorated with
+        either the :func:`event` or :func:`rpc` decorator.
+        """
+        key_type: type[Any] = type(message)  # type: ignore
+        handlers = self._handlers.get(key_type)  # type: ignore
+        if handlers is not None:
+            # Iterate over all handlers for this matching message type.
+            # Call the first handler whose router returns True and then return the result.
+            for h in handlers:
+                if h.router(message, ctx):
+                    return await h(self, message, ctx)
+        return await self.on_unhandled_message(message, ctx)  # type: ignore
+
+    async def on_unhandled_message(self, message: Any, ctx: MessageContext) -> None:
+        """Called when a message is received that does not have a matching message handler.
+
+        The default implementation logs an info message.
+
+        Args:
+            message: The message that was not handled.
+            ctx: The context of the message.
+        """
+        logger.info(f"Unhandled message: {message}")
+
+    @classmethod
+    def _discover_handlers(cls) -> Sequence[MessageHandler[Any, Any, Any]]:
+        handlers: list[MessageHandler[Any, Any, Any]] = []
+        for attr in dir(cls):
+            if callable(getattr(cls, attr, None)):
+                # Since we are getting it from the class, self is not bound
+                handler = getattr(cls, attr)
+                if hasattr(handler, "is_message_handler"):
+                    handlers.append(cast(MessageHandler[Any, Any, Any], handler))
+        return handlers
+
+    @classmethod
+    def _handles_types(cls) -> list[tuple[type[Any], list[MessageSerializer[Any]]]]:
+        # TODO(evmattso): handle deduplication
+        handlers = cls._discover_handlers()
+        types: list[tuple[type[Any], list[MessageSerializer[Any]]]] = []
+        types.extend(cls.internal_extra_handles_types)
+        for handler in handlers:
+            for t in handler.target_types:
+                # TODO(evmattso): support different serializers
+                serializers = try_get_known_serializers_for_type(t)
+                if len(serializers) == 0:
+                    raise ValueError(f"No serializers found for type {t}.")
+
+                types.append((t, try_get_known_serializers_for_type(t)))
+        return types
+
+
+# endregion

python/semantic_kernel/agents/runtime/core/serialization.py

@@ -0,0 +1,316 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import json
+from collections.abc import Sequence
+from dataclasses import asdict, dataclass, fields
+from typing import Any, ClassVar, Protocol, TypeVar, cast, get_args, get_origin, runtime_checkable
+
+from google.protobuf import any_pb2
+from google.protobuf.message import Message
+from pydantic import BaseModel
+
+from semantic_kernel.agents.runtime.core.type_helpers import is_union
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+T = TypeVar("T")
+
+
+@experimental
+class MessageSerializer(Protocol[T]):
+    """Serializer for messages."""
+
+    @property
+    def data_content_type(self) -> str:
+        """Content type of the data being serialized."""
+        ...
+
+    @property
+    def type_name(self) -> str:
+        """Type name of the message being serialized."""
+        ...
+
+    def deserialize(self, payload: bytes) -> T:
+        """Deserialize the payload into a message."""
+        ...
+
+    def serialize(self, message: T) -> bytes:
+        """Serialize the message into a payload."""
+        ...
+
+
+@experimental
+@runtime_checkable
+class IsDataclass(Protocol):
+    """Protocol to check if a class is a dataclass."""
+
+    # as already noted in comments, checking for this attribute is currently
+    # the most reliable way to ascertain that something is a dataclass
+    __dataclass_fields__: ClassVar[dict[str, Any]]
+
+
+@experimental
+def is_dataclass(cls: type[Any]) -> bool:
+    """Check if the class is a dataclass."""
+    return hasattr(cls, "__dataclass_fields__")
+
+
+@experimental
+def has_nested_dataclass(cls: type[IsDataclass]) -> bool:
+    """Check if the dataclass has nested dataclasses."""
+    # iterate fields and check if any of them are dataclasses
+    return any(is_dataclass(f.type) for f in cls.__dataclass_fields__.values())
+
+
+@experimental
+def contains_a_union(cls: type[IsDataclass]) -> bool:
+    """Check if the dataclass contains a union type."""
+    return any(is_union(f.type) for f in cls.__dataclass_fields__.values())
+
+
+@experimental
+def has_nested_base_model(cls: type[IsDataclass]) -> bool:
+    """Check if the dataclass has nested Pydantic BaseModel."""
+    for f in fields(cls):
+        field_type = f.type
+        # Resolve forward references and other annotations
+        origin = get_origin(field_type)
+        args = get_args(field_type)
+
+        # If the field type is directly a subclass of BaseModel
+        if isinstance(field_type, type) and issubclass(field_type, BaseModel):
+            return True
+
+        # If the field type is a generic type like List[BaseModel], Tuple[BaseModel, ...], etc.
+        if origin is not None and args:
+            for arg in args:
+                # Recursively check the argument types
+                if (isinstance(arg, type) and issubclass(arg, BaseModel)) or (
+                    get_origin(arg) is not None and has_nested_base_model_in_type(arg)
+                ):
+                    return True
+        # Handle Union types
+        elif args:
+            for arg in args:
+                if (isinstance(arg, type) and issubclass(arg, BaseModel)) or (
+                    get_origin(arg) is not None and has_nested_base_model_in_type(arg)
+                ):
+                    return True
+    return False
+
+
+@experimental
+def has_nested_base_model_in_type(tp: Any) -> bool:
+    """Helper function to check if a type or its arguments is a BaseModel subclass."""
+    origin = get_origin(tp)
+    args = get_args(tp)
+
+    if isinstance(tp, type) and issubclass(tp, BaseModel):
+        return True
+    if origin is not None and args:
+        for arg in args:
+            if has_nested_base_model_in_type(arg):
+                return True
+    return False
+
+
+DataclassT = TypeVar("DataclassT", bound=IsDataclass)
+
+JSON_DATA_CONTENT_TYPE = "application/json"
+"""JSON data content type"""
+
+# TODO(evmattso): what's the correct content type? There seems to be some disagreement over what it should be
+PROTOBUF_DATA_CONTENT_TYPE = "application/x-protobuf"
+"""Protobuf data content type"""
+
+
+@experimental
+class DataclassJsonMessageSerializer(MessageSerializer[DataclassT]):
+    """Serializer for dataclass messages."""
+
+    def __init__(self, cls: type[DataclassT]) -> None:
+        """Initialize the serializer with a dataclass type."""
+        if contains_a_union(cls):
+            raise ValueError("Dataclass has a union type, which is not supported. To use a union, use a Pydantic model")
+
+        if has_nested_dataclass(cls) or has_nested_base_model(cls):
+            raise ValueError(
+                "Dataclass has nested dataclasses or base models, which are not supported. To use nested types, "
+                "use a Pydantic model"
+            )
+
+        self.cls = cls
+
+    @property
+    def data_content_type(self) -> str:
+        """Return the data content type."""
+        return JSON_DATA_CONTENT_TYPE
+
+    @property
+    def type_name(self) -> str:
+        """Return the type name."""
+        return _type_name(self.cls)
+
+    def deserialize(self, payload: bytes) -> DataclassT:
+        """Deserialize the payload into a dataclass message."""
+        message_str = payload.decode("utf-8")
+        return self.cls(**json.loads(message_str))
+
+    def serialize(self, message: DataclassT) -> bytes:
+        """Serialize the dataclass message into a payload."""
+        return json.dumps(asdict(message)).encode("utf-8")
+
+
+PydanticT = TypeVar("PydanticT", bound=BaseModel)
+
+
+@experimental
+class PydanticJsonMessageSerializer(MessageSerializer[PydanticT]):
+    """Serializer for Pydantic messages."""
+
+    def __init__(self, cls: type[PydanticT]) -> None:
+        """Initialize the serializer with a Pydantic model type."""
+        self.cls = cls
+
+    @property
+    def data_content_type(self) -> str:
+        """Return the data content type."""
+        return JSON_DATA_CONTENT_TYPE
+
+    @property
+    def type_name(self) -> str:
+        """Return the type name."""
+        return _type_name(self.cls)
+
+    def deserialize(self, payload: bytes) -> PydanticT:
+        """Deserialize the payload into a Pydantic model message."""
+        message_str = payload.decode("utf-8")
+        return self.cls.model_validate_json(message_str)
+
+    def serialize(self, message: PydanticT) -> bytes:
+        """Serialize the Pydantic model message into a payload."""
+        return message.model_dump_json().encode("utf-8")
+
+
+ProtobufT = TypeVar("ProtobufT", bound=Message)
+
+
+# This class serializes to and from a google.protobuf.Any message that has been serialized to a string
+@experimental
+class ProtobufMessageSerializer(MessageSerializer[ProtobufT]):
+    """Serializer for Protobuf messages."""
+
+    def __init__(self, cls: type[ProtobufT]) -> None:
+        """Initialize the serializer with a Protobuf message type."""
+        self.cls = cls
+
+    @property
+    def data_content_type(self) -> str:
+        """Return the data content type."""
+        return PROTOBUF_DATA_CONTENT_TYPE
+
+    @property
+    def type_name(self) -> str:
+        """Return the type name."""
+        return _type_name(self.cls)
+
+    def deserialize(self, payload: bytes) -> ProtobufT:
+        """Deserialize the payload into a Protobuf message."""
+        # Parse payload into a proto any
+        any_proto = any_pb2.Any()
+        any_proto.ParseFromString(payload)
+
+        destination_message = self.cls()
+
+        if not any_proto.Unpack(destination_message):  # type: ignore
+            raise ValueError(f"Failed to unpack payload into {self.cls}")
+
+        return destination_message
+
+    def serialize(self, message: ProtobufT) -> bytes:
+        """Serialize the Protobuf message into a payload."""
+        any_proto = any_pb2.Any()
+        any_proto.Pack(message)  # type: ignore
+        return any_proto.SerializeToString()
+
+
+@experimental
+@dataclass
+class UnknownPayload:
+    """Class to represent an unknown payload."""
+
+    type_name: str
+    data_content_type: str
+    payload: bytes
+
+
+def _type_name(cls: type[Any] | Any) -> str:
+    # If cls is a protobuf, then we need to determine the descriptor
+    if isinstance(cls, type):
+        if issubclass(cls, Message):
+            return cast(str, cls.DESCRIPTOR.full_name)  # type: ignore
+    elif isinstance(cls, Message):
+        return cast(str, cls.DESCRIPTOR.full_name)
+
+    if isinstance(cls, type):
+        return cls.__name__
+    return cast(str, cls.__class__.__name__)
+
+
+V = TypeVar("V")
+
+
+@experimental
+def try_get_known_serializers_for_type(cls: type[Any]) -> list[MessageSerializer[Any]]:
+    """Try to get known serializers for a type."""
+    serializers: list[MessageSerializer[Any]] = []
+    if issubclass(cls, BaseModel):
+        serializers.append(PydanticJsonMessageSerializer(cls))
+    elif is_dataclass(cls):
+        serializers.append(DataclassJsonMessageSerializer(cls))
+    elif issubclass(cls, Message):
+        serializers.append(ProtobufMessageSerializer(cls))
+
+    return serializers
+
+
+@experimental
+class SerializationRegistry:
+    """Serialization registry for messages."""
+
+    def __init__(self) -> None:
+        """Initialize the serialization registry."""
+        # type_name, data_content_type -> serializer
+        self._serializers: dict[tuple[str, str], MessageSerializer[Any]] = {}
+
+    def add_serializer(self, serializer: MessageSerializer[Any] | Sequence[MessageSerializer[Any]]) -> None:
+        """Add a new serializer to the registry."""
+        if isinstance(serializer, Sequence):
+            for c in serializer:
+                self.add_serializer(c)
+            return
+
+        self._serializers[serializer.type_name, serializer.data_content_type] = serializer
+
+    def deserialize(self, payload: bytes, *, type_name: str, data_content_type: str) -> Any:
+        """Deserialize a payload into a message."""
+        serializer = self._serializers.get((type_name, data_content_type))
+        if serializer is None:
+            return UnknownPayload(type_name, data_content_type, payload)
+
+        return serializer.deserialize(payload)
+
+    def serialize(self, message: Any, *, type_name: str, data_content_type: str) -> bytes:
+        """Serialize a message into a payload."""
+        serializer = self._serializers.get((type_name, data_content_type))
+        if serializer is None:
+            raise ValueError(f"Unknown type {type_name} with content type {data_content_type}")
+
+        return serializer.serialize(message)
+
+    def is_registered(self, type_name: str, data_content_type: str) -> bool:
+        """Check if a type is registered in the registry."""
+        return (type_name, data_content_type) in self._serializers
+
+    def type_name(self, message: Any) -> str:
+        """Get the type name of a message."""
+        return _type_name(message)

python/semantic_kernel/agents/runtime/core/subscription.py

@@ -0,0 +1,68 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Awaitable, Callable
+from typing import Protocol, runtime_checkable
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+@runtime_checkable
+class Subscription(Protocol):
+    """Subscriptions define the topics that an agent is interested in."""
+
+    @property
+    def id(self) -> str:
+        """Get the ID of the subscription.
+
+        Implementations should return a unique ID for the subscription. Usually this is a UUID.
+
+        Returns:
+            str: ID of the subscription.
+        """
+        ...
+
+    def __eq__(self, other: object) -> bool:
+        """Check if two subscriptions are equal.
+
+        Args:
+            other (object): Other subscription to compare against.
+
+        Returns:
+            bool: True if the subscriptions are equal, False otherwise.
+        """
+        if not isinstance(other, Subscription):
+            return False
+
+        return self.id == other.id
+
+    def is_match(self, topic_id: TopicId) -> bool:
+        """Check if a given topic_id matches the subscription.
+
+        Args:
+            topic_id (TopicId): TopicId to check.
+
+        Returns:
+            bool: True if the topic_id matches the subscription, False otherwise.
+        """
+        ...
+
+    def map_to_agent(self, topic_id: TopicId) -> AgentId:
+        """Map a topic_id to an agent. Should only be called if `is_match` returns True for the given topic_id.
+
+        Args:
+            topic_id (TopicId): TopicId to map.
+
+        Returns:
+            AgentId: ID of the agent that should handle the topic_id.
+
+        Raises:
+            CantHandleException: If the subscription cannot handle the topic_id.
+        """
+        ...
+
+
+# Helper alias to represent the lambdas used to define subscriptions
+UnboundSubscription = Callable[[], list[Subscription] | Awaitable[list[Subscription]]]

python/semantic_kernel/agents/runtime/core/telemetry/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from .propagation import (
+    EnvelopeMetadata,
+    TelemetryMetadataContainer,
+    get_telemetry_envelope_metadata,
+    get_telemetry_grpc_metadata,
+)
+from .tracing import TraceHelper
+from .tracing_config import MessageRuntimeTracingConfig
+
+__all__ = [
+    "EnvelopeMetadata",
+    "MessageRuntimeTracingConfig",
+    "TelemetryMetadataContainer",
+    "TraceHelper",
+    "get_telemetry_envelope_metadata",
+    "get_telemetry_grpc_metadata",
+]

python/semantic_kernel/agents/runtime/core/telemetry/constants.py

@@ -0,0 +1,3 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+NAMESPACE = "agent_runtime"

python/semantic_kernel/agents/runtime/core/telemetry/propagation.py

@@ -0,0 +1,132 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from typing import Optional
+
+from opentelemetry.context import Context
+from opentelemetry.propagate import extract
+from opentelemetry.trace import Link, get_current_span
+from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+@dataclass(kw_only=True)
+class EnvelopeMetadata:
+    """Metadata for an envelope."""
+
+    traceparent: str | None = None
+    tracestate: str | None = None
+    links: Sequence[Link] | None = None
+
+
+def _get_carrier_for_envelope_metadata(envelope_metadata: EnvelopeMetadata) -> dict[str, str]:
+    carrier: dict[str, str] = {}
+    if envelope_metadata.traceparent is not None:
+        carrier["traceparent"] = envelope_metadata.traceparent
+    if envelope_metadata.tracestate is not None:
+        carrier["tracestate"] = envelope_metadata.tracestate
+    return carrier
+
+
+@experimental
+def get_telemetry_envelope_metadata() -> EnvelopeMetadata:
+    """Retrieves the telemetry envelope metadata.
+
+    Returns:
+        EnvelopeMetadata: The envelope metadata containing the traceparent and tracestate.
+    """
+    carrier: dict[str, str] = {}
+    TraceContextTextMapPropagator().inject(carrier)
+    return EnvelopeMetadata(
+        traceparent=carrier.get("traceparent"),
+        tracestate=carrier.get("tracestate"),
+    )
+
+
+def _get_carrier_for_remote_call_metadata(remote_call_metadata: Mapping[str, str]) -> dict[str, str]:
+    carrier: dict[str, str] = {}
+    traceparent = remote_call_metadata.get("traceparent")
+    tracestate = remote_call_metadata.get("tracestate")
+    if traceparent:
+        carrier["traceparent"] = traceparent
+    if tracestate:
+        carrier["tracestate"] = tracestate
+    return carrier
+
+
+@experimental
+def get_telemetry_grpc_metadata(existingMetadata: Mapping[str, str] | None = None) -> dict[str, str]:
+    """Retrieves the telemetry gRPC metadata.
+
+    Args:
+        existingMetadata (Optional[Mapping[str, str]]): The existing metadata to include in the gRPC metadata.
+
+    Returns:
+        Mapping[str, str]: The gRPC metadata containing the traceparent and tracestate.
+    """
+    carrier: dict[str, str] = {}
+    TraceContextTextMapPropagator().inject(carrier)
+    traceparent = carrier.get("traceparent")
+    tracestate = carrier.get("tracestate")
+    metadata: dict[str, str] = {}
+    if existingMetadata is not None:
+        for key, value in existingMetadata.items():
+            metadata[key] = value
+    if traceparent is not None:
+        metadata["traceparent"] = traceparent
+    if tracestate is not None:
+        metadata["tracestate"] = tracestate
+    return metadata
+
+
+TelemetryMetadataContainer = Optional[EnvelopeMetadata] | Mapping[str, str]
+
+
+@experimental
+def get_telemetry_context(metadata: TelemetryMetadataContainer) -> Context:
+    """Retrieves the telemetry context from the given metadata.
+
+    Args:
+        metadata (Optional[EnvelopeMetadata]): The metadata containing the telemetry context.
+
+    Returns:
+        Context: The telemetry context extracted from the metadata, or an empty context if the metadata is None.
+    """
+    if metadata is None:
+        return Context()
+    if isinstance(metadata, EnvelopeMetadata):
+        return extract(_get_carrier_for_envelope_metadata(metadata))
+    if hasattr(metadata, "__getitem__"):
+        return extract(_get_carrier_for_remote_call_metadata(metadata))
+    raise ValueError(f"Unknown metadata type: {type(metadata)}")
+
+
+@experimental
+def get_telemetry_links(
+    metadata: TelemetryMetadataContainer,
+) -> Sequence[Link] | None:
+    """Retrieves the telemetry links from the given metadata.
+
+    Args:
+        metadata (Optional[EnvelopeMetadata]): The metadata containing the telemetry links.
+
+    Returns:
+        Optional[Sequence[Link]]: The telemetry links extracted from the metadata, or None if there are no links.
+    """
+    if metadata is None:
+        return None
+    if isinstance(metadata, EnvelopeMetadata):
+        context = extract(_get_carrier_for_envelope_metadata(metadata))
+    elif hasattr(metadata, "__getitem__"):
+        context = extract(_get_carrier_for_remote_call_metadata(metadata))
+    else:
+        return None
+    # Retrieve the extracted SpanContext from the context.
+    linked_span = get_current_span(context)
+    # Use the linked span to get the SpanContext.
+    span_context = linked_span.get_span_context()
+    # Create a Link object using the SpanContext.
+    return [Link(span_context)]

python/semantic_kernel/agents/runtime/core/telemetry/tracing.py

@@ -0,0 +1,102 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import contextlib
+from collections.abc import Iterator
+from typing import Generic
+
+from opentelemetry.trace import NoOpTracerProvider, Span, SpanKind, TracerProvider, get_tracer_provider
+from opentelemetry.util import types
+
+from semantic_kernel.agents.runtime.core.telemetry.propagation import TelemetryMetadataContainer, get_telemetry_links
+from semantic_kernel.agents.runtime.core.telemetry.tracing_config import (
+    Destination,
+    ExtraAttributes,
+    Operation,
+    TracingConfig,
+)
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class TraceHelper(Generic[Operation, Destination, ExtraAttributes]):
+    """TraceHelper is a utility class to assist with tracing operations using OpenTelemetry.
+
+    This class provides a context manager `trace_block` to create and manage spans for tracing operations,
+    following semantic conventions and supporting nested spans through metadata contexts.
+
+    """
+
+    def __init__(
+        self,
+        tracer_provider: TracerProvider | None,
+        instrumentation_builder_config: TracingConfig[Operation, Destination, ExtraAttributes],
+    ) -> None:
+        """Initialize the TraceHelper with a tracer provider and instrumentation builder config."""
+        # Evaluate in order: first try tracer_provider param, then get_tracer_provider(), finally fallback to NoOp
+        # This allows for nested tracing with a default tracer provided by the user
+        self.tracer_provider = tracer_provider or get_tracer_provider() or NoOpTracerProvider()
+        self.tracer = self.tracer_provider.get_tracer(f"agent_runtime {instrumentation_builder_config.name}")
+        self.instrumentation_builder_config = instrumentation_builder_config
+
+    @contextlib.contextmanager
+    def trace_block(
+        self,
+        operation: Operation,
+        destination: Destination,
+        parent: TelemetryMetadataContainer | None,
+        *,
+        extraAttributes: ExtraAttributes | None = None,
+        kind: SpanKind | None = None,
+        attributes: types.Attributes | None = None,
+        start_time: int | None = None,
+        record_exception: bool = True,
+        set_status_on_exception: bool = True,
+        end_on_exit: bool = True,
+    ) -> Iterator[Span]:
+        """Thin wrapper on top of start_as_current_span.
+
+        1. It helps us follow semantic conventions
+        2. It helps us get contexts from metadata so we can get nested spans
+
+        Args:
+            operation (MessagingOperation): The messaging operation being performed.
+            destination (MessagingDestination): The messaging destination being used.
+            parent (Optional[TelemetryMetadataContainer]): The parent telemetry metadata context
+            kind (SpanKind, optional): The kind of span. If not provided, it maps to PRODUCER or CONSUMER depending
+                on the operation.
+            extraAttributes (ExtraAttributes, optional): Additional defined attributes for the span. Defaults to None.
+            attributes (Optional[types.Attributes], optional): Additional non-defined attributes for the span.
+                Defaults to None.
+            start_time (Optional[int], optional): The start time of the span. Defaults to None.
+            record_exception (bool, optional): Whether to record exceptions. Defaults to True.
+            set_status_on_exception (bool, optional): Whether to set the status on exception. Defaults to True.
+            end_on_exit (bool, optional): Whether to end the span on exit. Defaults to True.
+
+        Yields:
+            Iterator[Span]: The span object.
+
+        """
+        span_name = self.instrumentation_builder_config.get_span_name(operation, destination)
+        span_kind = kind or self.instrumentation_builder_config.get_span_kind(operation)
+        context = None  # TODO(evmattso): we may need to remove other code for using custom context.
+        links = get_telemetry_links(parent) if parent else None
+        attributes_with_defaults: dict[str, types.AttributeValue] = {}
+        for key, value in (attributes or {}).items():
+            attributes_with_defaults[key] = value
+        instrumentation_attributes = self.instrumentation_builder_config.build_attributes(
+            operation, destination, extraAttributes
+        )
+        for key, value in instrumentation_attributes.items():
+            attributes_with_defaults[key] = value
+        with self.tracer.start_as_current_span(
+            span_name,
+            context,
+            span_kind,
+            attributes_with_defaults,
+            links,
+            start_time,
+            record_exception,
+            set_status_on_exception,
+            end_on_exit,
+        ) as span:
+            yield span

python/semantic_kernel/agents/runtime/core/telemetry/tracing_config.py

@@ -0,0 +1,201 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+from abc import ABC, abstractmethod
+from typing import Generic, Literal, TypedDict, TypeVar, Union
+
+from opentelemetry.trace import SpanKind
+from opentelemetry.util import types
+from typing_extensions import NotRequired
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.telemetry.constants import NAMESPACE
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+logger = logging.getLogger("agent_runtime")
+event_logger = logging.getLogger("agent_runtime.events")
+
+Operation = TypeVar("Operation", bound=str)
+Destination = TypeVar("Destination")
+ExtraAttributes = TypeVar("ExtraAttributes")
+
+
+@experimental
+class TracingConfig(ABC, Generic[Operation, Destination, ExtraAttributes]):
+    """A protocol that defines the configuration for instrumentation.
+
+    This protocol specifies the required properties and methods that any
+    instrumentation configuration class must implement. It includes a
+    property to get the name of the module being instrumented and a method
+    to build attributes for the instrumentation configuration.
+    """
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Gets the name of the module being instrumented."""
+        ...
+
+    @abstractmethod
+    def build_attributes(
+        self,
+        operation: Operation,
+        destination: Destination,
+        extraAttributes: ExtraAttributes | None,
+    ) -> dict[str, types.AttributeValue]:
+        """Builds the attributes for the instrumentation configuration.
+
+        Returns:
+            Dict[str, str]: The attributes for the instrumentation configuration.
+        """
+        ...
+
+    @abstractmethod
+    def get_span_name(
+        self,
+        operation: Operation,
+        destination: Destination,
+    ) -> str:
+        """Returns the span name based on the given operation and destination.
+
+        Parameters:
+            operation (MessagingOperation): The messaging operation.
+            destination (Optional[MessagingDestination]): The messaging destination.
+
+        Returns:
+            str: The span name.
+        """
+        ...
+
+    @abstractmethod
+    def get_span_kind(
+        self,
+        operation: Operation,
+    ) -> SpanKind:
+        """Determines the span kind based on the given messaging operation.
+
+        Parameters:
+            operation (MessagingOperation): The messaging operation.
+
+        Returns:
+            SpanKind: The span kind based on the messaging operation.
+        """
+
+
+@experimental
+class ExtraMessageRuntimeAttributes(TypedDict):
+    """A dictionary of extra attributes for message runtime instrumentation."""
+
+    message_size: NotRequired[int]
+    message_type: NotRequired[str]
+
+
+MessagingDestination = Union[AgentId, TopicId, str, None]
+MessagingOperation = Literal["create", "send", "publish", "receive", "intercept", "process", "ack"]
+
+
+@experimental
+class MessageRuntimeTracingConfig(
+    TracingConfig[MessagingOperation, MessagingDestination, ExtraMessageRuntimeAttributes]
+):
+    """A class that defines the configuration for message runtime instrumentation.
+
+    This class implements the TracingConfig protocol and provides
+    the name of the module being instrumented and the attributes for the
+    instrumentation configuration.
+    """
+
+    def __init__(self, runtime_name: str) -> None:
+        """Initialize the MessageRuntimeTracingConfig with the runtime name."""
+        self._runtime_name = runtime_name
+
+    @property
+    def name(self) -> str:
+        """Get the name of the module being instrumented."""
+        return self._runtime_name
+
+    def build_attributes(
+        self,
+        operation: MessagingOperation,
+        destination: MessagingDestination,
+        extraAttributes: ExtraMessageRuntimeAttributes | None,
+    ) -> dict[str, types.AttributeValue]:
+        """Build the attributes for the instrumentation configuration."""
+        attrs: dict[str, types.AttributeValue] = {
+            "messaging.operation": self._get_operation_type(operation),
+            "messaging.destination": self._get_destination_str(destination),
+        }
+        if extraAttributes:
+            # TODO(evmattso): Make this more pythonic?
+            if "message_size" in extraAttributes:
+                attrs["messaging.message.envelope.size"] = extraAttributes["message_size"]
+            if "message_type" in extraAttributes:
+                attrs["messaging.message.type"] = extraAttributes["message_type"]
+        return attrs
+
+    def get_span_name(
+        self,
+        operation: MessagingOperation,
+        destination: MessagingDestination,
+    ) -> str:
+        """Returns the span name based on the given operation and destination.
+
+        Semantic Conventions - https://opentelemetry.io/docs/specs/semconv/messaging/messaging-spans/#span-name
+
+        Parameters:
+            operation (MessagingOperation): The messaging operation.
+            destination (Optional[MessagingDestination]): The messaging destination.
+
+        Returns:
+            str: The span name.
+        """
+        span_parts: list[str] = [operation]
+        destination_str = self._get_destination_str(destination)
+        if destination_str:
+            span_parts.append(destination_str)
+        span_name = " ".join(span_parts)
+        return f"{NAMESPACE} {span_name}"
+
+    def get_span_kind(
+        self,
+        operation: MessagingOperation,
+    ) -> SpanKind:
+        """Determines the span kind based on the given messaging operation.
+
+        Semantic Conventions - https://opentelemetry.io/docs/specs/semconv/messaging/messaging-spans/#span-kind
+
+        Parameters:
+            operation (MessagingOperation): The messaging operation.
+
+        Returns:
+            SpanKind: The span kind based on the messaging operation.
+        """
+        if operation in ["create", "send", "publish"]:
+            return SpanKind.PRODUCER
+        if operation in ["receive", "intercept", "process", "ack"]:
+            return SpanKind.CONSUMER
+        return SpanKind.CLIENT
+
+    # TODO(evmattso): Use stringified convention
+    def _get_destination_str(self, destination: MessagingDestination) -> str:
+        if isinstance(destination, AgentId):
+            return f"{destination.type}.({destination.key})-A"
+        if isinstance(destination, TopicId):
+            return f"{destination.type}.({destination.source})-T"
+        if isinstance(destination, str):
+            return destination
+        if destination is None:
+            return ""
+        raise ValueError(f"Unknown destination type: {type(destination)}")
+
+    def _get_operation_type(self, operation: MessagingOperation) -> str:
+        if operation in ["send", "publish"]:
+            return "publish"
+        if operation in ["create"]:
+            return "create"
+        if operation in ["receive", "intercept", "ack"]:
+            return "receive"
+        if operation in ["process"]:
+            return "process"
+        return "Unknown"

python/semantic_kernel/agents/runtime/core/topic.py

@@ -0,0 +1,58 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import re
+from dataclasses import dataclass
+
+from typing_extensions import Self
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+def is_valid_topic_type(value: str) -> bool:
+    """Check if the given value is a valid topic type."""
+    return bool(re.match(r"^[\w\-\.\:\=]+\Z", value))
+
+
+@experimental
+@dataclass(eq=True, frozen=True)
+class TopicId:
+    """TopicId defines the scope of a broadcast message.
+
+    In essence, agent runtime implements a publish-subscribe model through its broadcast API: when publishing a message,
+    the topic must be specified.
+
+    See here for more information: :ref:`topic_and_subscription_topic`
+    """
+
+    type: str
+    """Type of the event that this topic_id contains. Adhere's to the cloud event spec.
+
+    Must match the pattern: ^[\\w\\-\\.\\:\\=]+\\Z
+
+    Learn more here: https://github.com/cloudevents/spec/blob/main/cloudevents/spec.md#type
+    """
+
+    source: str
+    """Identifies the context in which an event happened. Adhere's to the cloud event spec.
+
+    Learn more here: https://github.com/cloudevents/spec/blob/main/cloudevents/spec.md#source-1
+    """
+
+    def __post_init__(self) -> None:
+        """Validate the topic type and source."""
+        if is_valid_topic_type(self.type) is False:
+            raise ValueError(f"Invalid topic type: {self.type}. Must match the pattern: ^[\\w\\-\\.\\:\\=]+\\Z")
+
+    def __str__(self) -> str:
+        """Convert the TopicId to a string."""
+        return f"{self.type}/{self.source}"
+
+    @classmethod
+    def from_str(cls, topic_id: str) -> Self:
+        """Convert a string of the format ``type/source`` into a TopicId."""
+        items = topic_id.split("/", maxsplit=1)
+        if len(items) != 2:
+            raise ValueError(f"Invalid topic id: {topic_id}")
+        type, source = items[0], items[1]
+        return cls(type, source)

python/semantic_kernel/agents/runtime/core/type_helpers.py

@@ -0,0 +1,45 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Sequence
+from types import NoneType, UnionType
+from typing import Any, Optional, Union, get_args, get_origin
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+def is_union(t: object) -> bool:
+    """Check if the type is a Union or UnionType."""
+    origin = get_origin(t)
+    return origin is Union or origin is UnionType
+
+
+@experimental
+def is_optional(t: object) -> bool:
+    """Check if the type is an Optional."""
+    origin = get_origin(t)
+    return origin is Optional
+
+
+# Special type to avoid the 3.10 vs 3.11+ difference of typing._SpecialForm vs typing.Any
+@experimental
+class AnyType:
+    """Special type to represent Any."""
+
+    pass
+
+
+@experimental
+def get_types(t: object) -> Sequence[type[Any]] | None:
+    """Get the types from a Union or Optional type."""
+    if is_union(t):
+        return get_args(t)
+    if is_optional(t):
+        return tuple([*list(get_args(t)), NoneType])
+    if t is Any:
+        return (AnyType,)
+    if isinstance(t, type):
+        return (t,)
+    if isinstance(t, NoneType):
+        return (NoneType,)
+    return None

python/semantic_kernel/agents/runtime/core/validation_utils.py

@@ -0,0 +1,13 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import re
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+_AGENT_TYPE_REGEX = re.compile(r"^[\w\-\.]+\Z")
+
+
+@experimental
+def is_valid_agent_type(value: str) -> bool:
+    """Check if the agent type is valid."""
+    return bool(_AGENT_TYPE_REGEX.match(value))

python/semantic_kernel/agents/runtime/in_process/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) Microsoft. All rights reserved.

python/semantic_kernel/agents/runtime/in_process/agent_instantiation_context.py

@@ -0,0 +1,65 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Generator
+from contextlib import contextmanager
+from contextvars import ContextVar
+from typing import Any, ClassVar
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class AgentInstantiationContext:
+    """A static class that provides context for agent instantiation.
+
+    This static class can be used to access the current runtime and agent ID
+    during agent instantiation -- inside the factory function or the agent's
+    class constructor.
+    """
+
+    def __init__(self) -> None:
+        """Instantiate the AgentInstantiationContext class."""
+        raise RuntimeError(
+            "AgentInstantiationContext cannot be instantiated. It is a static class that provides context management "
+            "for agent instantiation."
+        )
+
+    _AGENT_INSTANTIATION_CONTEXT_VAR: ClassVar[ContextVar[tuple[CoreRuntime, AgentId]]] = ContextVar(
+        "_AGENT_INSTANTIATION_CONTEXT_VAR"
+    )
+
+    @classmethod
+    @contextmanager
+    def populate_context(cls, ctx: tuple[CoreRuntime, AgentId]) -> Generator[None, Any, None]:
+        """Populate the context with the current runtime and agent ID."""
+        token = AgentInstantiationContext._AGENT_INSTANTIATION_CONTEXT_VAR.set(ctx)
+        try:
+            yield
+        finally:
+            AgentInstantiationContext._AGENT_INSTANTIATION_CONTEXT_VAR.reset(token)
+
+    @classmethod
+    def current_runtime(cls) -> CoreRuntime:
+        """Get the current runtime."""
+        try:
+            return cls._AGENT_INSTANTIATION_CONTEXT_VAR.get()[0]
+        except LookupError as e:
+            raise RuntimeError(
+                "AgentInstantiationContext.runtime() must be called within an instantiation context such as when the "
+                "AgentRuntime is instantiating an agent. Mostly likely this was caused by directly instantiating an "
+                "agent instead of using the AgentRuntime to do so."
+            ) from e
+
+    @classmethod
+    def current_agent_id(cls) -> AgentId:
+        """Get the current agent ID."""
+        try:
+            return cls._AGENT_INSTANTIATION_CONTEXT_VAR.get()[1]
+        except LookupError as e:
+            raise RuntimeError(
+                "AgentInstantiationContext.agent_id() must be called within an instantiation context such as when the "
+                "AgentRuntime is instantiating an agent. Mostly likely this was caused by directly instantiating an "
+                "agent instead of using the AgentRuntime to do so."
+            ) from e

python/semantic_kernel/agents/runtime/in_process/default_subscription.py

@@ -0,0 +1,65 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Callable
+from typing import TypeVar, overload
+
+from semantic_kernel.agents.runtime.core.agent_type import AgentType
+from semantic_kernel.agents.runtime.core.base_agent import BaseAgent, subscription_factory
+from semantic_kernel.agents.runtime.core.exceptions import CantHandleException
+from semantic_kernel.agents.runtime.in_process.subscription_context import SubscriptionInstantiationContext
+from semantic_kernel.agents.runtime.in_process.type_subscription import TypeSubscription
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class DefaultSubscription(TypeSubscription):
+    """The default subscription is designed to be a default for applications that only need global scope for agents.
+
+    This topic by default uses the "default" topic type and attempts to detect the agent type to use based on the
+    instantiation context.
+
+    Args:
+        topic_type (str, optional): The topic type to subscribe to. Defaults to "default".
+        agent_type (str, optional): The agent type to use for the subscription. Defaults to None, in which case it
+            will attempt to detect the agent type based on the instantiation context.
+    """
+
+    def __init__(self, topic_type: str = "default", agent_type: str | AgentType | None = None):
+        """Initialize the DefaultSubscription."""
+        if agent_type is None:
+            try:
+                agent_type = SubscriptionInstantiationContext.agent_type().type
+            except RuntimeError as e:
+                raise CantHandleException(
+                    "If agent_type is not specified DefaultSubscription must be created within the subscription "
+                    "callback in AgentRuntime.register"
+                ) from e
+
+        super().__init__(topic_type, agent_type)
+
+
+BaseAgentType = TypeVar("BaseAgentType", bound="BaseAgent")
+
+
+@overload
+def default_subscription() -> Callable[[type[BaseAgentType]], type[BaseAgentType]]: ...
+
+
+@overload
+def default_subscription(cls: type[BaseAgentType]) -> type[BaseAgentType]: ...
+
+
+@experimental
+def default_subscription(
+    cls: type[BaseAgentType] | None = None,
+) -> Callable[[type[BaseAgentType]], type[BaseAgentType]] | type[BaseAgentType]:
+    """Create a default subscription."""
+    if cls is None:
+        return subscription_factory(lambda: [DefaultSubscription()])
+    return subscription_factory(lambda: [DefaultSubscription()])(cls)
+
+
+@experimental
+def type_subscription(topic_type: str) -> Callable[[type[BaseAgentType]], type[BaseAgentType]]:
+    """Create a type subscription for the given topic type."""
+    return subscription_factory(lambda: [DefaultSubscription(topic_type=topic_type)])

python/semantic_kernel/agents/runtime/in_process/default_topic.py

@@ -0,0 +1,31 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.agents.runtime.in_process.message_handler_context import MessageHandlerContext
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class DefaultTopicId(TopicId):
+    """DefaultTopicId provides a sensible default for the topic_id and source fields of a TopicId.
+
+    If created in the context of a message handler, the source will be set to the agent_id of the message handler,
+    otherwise it will be set to "default".
+
+    Args:
+        type (str, optional): Topic type to publish message to. Defaults to "default".
+        source (str | None, optional): Topic source to publish message to. If None, the source will be set to the
+            agent_id of the message handler if in the context of a message handler, otherwise it will be set to
+            "default". Defaults to None.
+    """
+
+    def __init__(self, type: str = "default", source: str | None = None) -> None:
+        """Initialize the DefaultTopicId."""
+        if source is None:
+            try:
+                source = MessageHandlerContext.agent_id().key
+            # If we aren't in the context of a message handler, we use the default source
+            except RuntimeError:
+                source = "default"
+
+        super().__init__(type, source)

python/semantic_kernel/agents/runtime/in_process/in_process_runtime.py

@@ -0,0 +1,854 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import inspect
+import logging
+import sys
+import uuid
+import warnings
+from asyncio import CancelledError, Future, Queue, Task
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from dataclasses import dataclass
+from typing import Any, ParamSpec, TypeVar, cast
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if sys.version_info >= (3, 13):
+    from asyncio import Queue, QueueShutDown
+else:
+    from .queue import Queue, QueueShutDown  # type: ignore
+
+from opentelemetry.trace import TracerProvider
+
+from semantic_kernel.agents.runtime.core.agent import Agent
+from semantic_kernel.agents.runtime.core.agent_id import AgentId, CoreAgentId
+from semantic_kernel.agents.runtime.core.agent_metadata import AgentMetadata
+from semantic_kernel.agents.runtime.core.agent_type import AgentType, CoreAgentType
+from semantic_kernel.agents.runtime.core.cancellation_token import CancellationToken
+from semantic_kernel.agents.runtime.core.core_runtime import CoreRuntime
+from semantic_kernel.agents.runtime.core.exceptions import MessageDroppedException
+from semantic_kernel.agents.runtime.core.intervention import DropMessage, InterventionHandler
+from semantic_kernel.agents.runtime.core.logging import (
+    AgentConstructionExceptionEvent,
+    DeliveryStage,
+    MessageDroppedEvent,
+    MessageEvent,
+    MessageHandlerExceptionEvent,
+    MessageKind,
+)
+from semantic_kernel.agents.runtime.core.message_context import MessageContext
+from semantic_kernel.agents.runtime.core.serialization import (
+    JSON_DATA_CONTENT_TYPE,
+    MessageSerializer,
+    SerializationRegistry,
+)
+from semantic_kernel.agents.runtime.core.subscription import Subscription
+from semantic_kernel.agents.runtime.core.telemetry import (
+    EnvelopeMetadata,
+    MessageRuntimeTracingConfig,
+    TraceHelper,
+    get_telemetry_envelope_metadata,
+)
+from semantic_kernel.agents.runtime.core.topic import TopicId
+
+from .agent_instantiation_context import AgentInstantiationContext
+from .message_handler_context import MessageHandlerContext
+from .runtime_impl_helpers import SubscriptionManager, get_impl
+
+logger = logging.getLogger("in_process_runtime")
+event_logger = logging.getLogger("in_process_runtime.events")
+
+# We use a type parameter in some functions which shadows the built-in `type` function.
+# This is a workaround to avoid shadowing the built-in `type` function.
+type_func_alias = type
+
+
+@experimental
+@dataclass(kw_only=True)
+class PublishMessageEnvelope:
+    """A message envelope for publishing messages to all agents that can handle the message of the type T."""
+
+    message: Any
+    cancellation_token: CancellationToken
+    sender: AgentId | None
+    topic_id: TopicId
+    metadata: EnvelopeMetadata | None = None
+    message_id: str
+
+
+@experimental
+@dataclass(kw_only=True)
+class SendMessageEnvelope:
+    """A message envelope for sending a message to a specific agent that can handle the message of the type T."""
+
+    message: Any
+    sender: AgentId | None
+    recipient: AgentId
+    future: Future[Any]
+    cancellation_token: CancellationToken
+    metadata: EnvelopeMetadata | None = None
+    message_id: str
+
+
+@experimental
+@dataclass(kw_only=True)
+class ResponseMessageEnvelope:
+    """A message envelope for sending a response to a message."""
+
+    message: Any
+    future: Future[Any]
+    sender: AgentId
+    recipient: AgentId | None
+    metadata: EnvelopeMetadata | None = None
+
+
+P = ParamSpec("P")
+T = TypeVar("T", bound=Agent)
+
+
+@experimental
+class RunContext:
+    """A context for the runtime to run in a background task."""
+
+    def __init__(self, runtime: "InProcessRuntime") -> None:
+        """Initialize the run context."""
+        self._runtime = runtime
+        self._run_task = asyncio.create_task(self._run())
+        self._stopped = asyncio.Event()
+
+    async def _run(self) -> None:
+        while True:
+            if self._stopped.is_set():
+                return
+
+            await self._runtime._process_next()  # type: ignore
+
+    async def stop(self) -> None:
+        """Stop the runtime message processing loop immediately."""
+        self._stopped.set()
+        self._runtime._message_queue.shutdown(immediate=True)  # type: ignore
+        await self._run_task
+
+    async def stop_when_idle(self) -> None:
+        """Stop the runtime message processing loop when there are no messages in the queue."""
+        await self._runtime._message_queue.join()  # type: ignore
+        self._stopped.set()
+        self._runtime._message_queue.shutdown(immediate=True)  # type: ignore
+        await self._run_task
+
+    async def stop_when(self, condition: Callable[[], bool], check_period: float = 1.0) -> None:
+        """Stop the runtime message processing loop when the condition is met."""
+
+        async def check_condition() -> None:
+            while not condition():
+                await asyncio.sleep(check_period)
+            await self.stop()
+
+        await asyncio.create_task(check_condition())
+
+
+def _warn_if_none(value: Any, handler_name: str) -> None:
+    """Utility function to check if the intervention handler returned None and issue a warning.
+
+    Args:
+        value: The return value to check
+        handler_name: Name of the intervention handler method for the warning message
+    """
+    if value is None:
+        warnings.warn(
+            f"Intervention handler {handler_name} returned None. This might be unintentional. "
+            "Consider returning the original message or DropMessage explicitly.",
+            RuntimeWarning,
+            stacklevel=2,
+        )
+
+
+@experimental
+class InProcessRuntime(CoreRuntime):
+    """A in-process runtime that processes all messages using a single asyncio queue.
+
+    Messages are delivered in the order they are received, and the runtime processes
+    each message in a separate asyncio task concurrently.
+    """
+
+    def __init__(
+        self,
+        *,
+        intervention_handlers: list[InterventionHandler] | None = None,
+        tracer_provider: TracerProvider | None = None,
+        ignore_unhandled_exceptions: bool = True,
+    ) -> None:
+        """Initialize the runtime."""
+        self._tracer_helper = TraceHelper(tracer_provider, MessageRuntimeTracingConfig("InProcessRuntime"))
+        self._message_queue: Queue[PublishMessageEnvelope | SendMessageEnvelope | ResponseMessageEnvelope] = Queue()
+        # (namespace, type) -> List[AgentId]
+        self._agent_factories: dict[
+            str, Callable[[], Agent | Awaitable[Agent]] | Callable[[CoreRuntime, AgentId], Agent | Awaitable[Agent]]
+        ] = {}
+        self._instantiated_agents: dict[AgentId, Agent] = {}
+        self._intervention_handlers = intervention_handlers
+        self._background_tasks: set[Task[Any]] = set()
+        self._subscription_manager = SubscriptionManager()
+        self._run_context: RunContext | None = None
+        self._serialization_registry = SerializationRegistry()
+        self._ignore_unhandled_handler_exceptions = ignore_unhandled_exceptions
+        self._background_exception: BaseException | None = None
+
+    @property
+    def unprocessed_messages_count(
+        self,
+    ) -> int:
+        """Get the number of unprocessed messages in the queue."""
+        return self._message_queue.qsize()
+
+    @property
+    def _known_agent_names(self) -> set[str]:
+        return set(self._agent_factories.keys())
+
+    # Returns the response of the message
+    async def send_message(
+        self,
+        message: Any,
+        recipient: AgentId,
+        *,
+        sender: AgentId | None = None,
+        cancellation_token: CancellationToken | None = None,
+        message_id: str | None = None,
+    ) -> Any:
+        """Send a message to an agent and get a response."""
+        if cancellation_token is None:
+            cancellation_token = CancellationToken()
+
+        if message_id is None:
+            message_id = str(uuid.uuid4())
+
+        event_logger.info(
+            MessageEvent(
+                payload=self._try_serialize(message),
+                sender=sender,
+                receiver=recipient,
+                kind=MessageKind.DIRECT,
+                delivery_stage=DeliveryStage.SEND,
+            )
+        )
+
+        with self._tracer_helper.trace_block(
+            "create",
+            recipient,
+            parent=None,
+            extraAttributes={"message_type": type(message).__name__},
+        ):
+            future = asyncio.get_event_loop().create_future()
+            if recipient.type not in self._known_agent_names:
+                future.set_exception(Exception("Recipient not found"))
+
+            content = message.__dict__ if hasattr(message, "__dict__") else message
+            logger.info(f"Sending message of type {type(message).__name__} to {recipient.type}: {content}")
+
+            await self._message_queue.put(
+                SendMessageEnvelope(
+                    message=message,
+                    recipient=recipient,
+                    future=future,
+                    cancellation_token=cancellation_token,
+                    sender=sender,
+                    metadata=get_telemetry_envelope_metadata(),
+                    message_id=message_id,
+                )
+            )
+
+            cancellation_token.link_future(future)
+
+            return await future
+
+    async def publish_message(
+        self,
+        message: Any,
+        topic_id: TopicId,
+        *,
+        sender: AgentId | None = None,
+        cancellation_token: CancellationToken | None = None,
+        message_id: str | None = None,
+    ) -> None:
+        """Publish a message to all agents that are subscribed to the topic."""
+        with self._tracer_helper.trace_block(
+            "create",
+            topic_id,
+            parent=None,
+            extraAttributes={"message_type": type(message).__name__},
+        ):
+            if cancellation_token is None:
+                cancellation_token = CancellationToken()
+            content = message.__dict__ if hasattr(message, "__dict__") else message
+            logger.info(f"Publishing message of type {type(message).__name__} to all subscribers: {content}")
+
+            if message_id is None:
+                message_id = str(uuid.uuid4())
+
+            event_logger.info(
+                MessageEvent(
+                    payload=self._try_serialize(message),
+                    sender=sender,
+                    receiver=topic_id,
+                    kind=MessageKind.PUBLISH,
+                    delivery_stage=DeliveryStage.SEND,
+                )
+            )
+
+            await self._message_queue.put(
+                PublishMessageEnvelope(
+                    message=message,
+                    cancellation_token=cancellation_token,
+                    sender=sender,
+                    topic_id=topic_id,
+                    metadata=get_telemetry_envelope_metadata(),
+                    message_id=message_id,
+                )
+            )
+
+    async def save_state(self) -> Mapping[str, Any]:
+        """Save the state of all instantiated agents.
+
+        This method calls the :meth:`~agent_runtime.BaseAgent.save_state` method on each agent and returns a dictionary
+        mapping agent IDs to their state.
+
+        .. note::
+            This method does not currently save the subscription state. We will add this in the future.
+
+        Returns:
+            A dictionary mapping agent IDs to their state.
+
+        """
+        state: dict[str, dict[str, Any]] = {}
+        for agent_id in self._instantiated_agents:
+            state[str(agent_id)] = dict(await (await self._get_agent(agent_id)).save_state())
+        return state
+
+    async def load_state(self, state: Mapping[str, Any]) -> None:
+        """Load the state of all instantiated agents.
+
+        This method calls the :meth:`~agent_runtime.BaseAgent.load_state` method on each agent with the state
+        provided in the dictionary. The keys of the dictionary are the agent IDs, and the values are the state
+        dictionaries returned by the :meth:`~agent_runtime.BaseAgent.save_state` method.
+
+        .. note::
+
+            This method does not currently load the subscription state. We will add this in the future.
+
+        """
+        for agent_id_str in state:
+            agent_id = CoreAgentId.from_str(agent_id_str)
+            if agent_id.type in self._known_agent_names:
+                await (await self._get_agent(agent_id)).load_state(state[str(agent_id)])
+
+    async def _process_send(self, message_envelope: SendMessageEnvelope) -> None:
+        with self._tracer_helper.trace_block("send", message_envelope.recipient, parent=message_envelope.metadata):
+            recipient = message_envelope.recipient
+
+            if recipient.type not in self._known_agent_names:
+                raise LookupError(f"Agent type '{recipient.type}' does not exist.")
+
+            try:
+                sender_id = str(message_envelope.sender) if message_envelope.sender is not None else "Unknown"
+                logger.info(
+                    f"Calling message handler for {recipient} with message type "
+                    f"{type(message_envelope.message).__name__} sent by {sender_id}"
+                )
+                event_logger.info(
+                    MessageEvent(
+                        payload=self._try_serialize(message_envelope.message),
+                        sender=message_envelope.sender,
+                        receiver=recipient,
+                        kind=MessageKind.DIRECT,
+                        delivery_stage=DeliveryStage.DELIVER,
+                    )
+                )
+                recipient_agent = await self._get_agent(recipient)
+
+                message_context = MessageContext(
+                    sender=message_envelope.sender,
+                    topic_id=None,
+                    is_rpc=True,
+                    cancellation_token=message_envelope.cancellation_token,
+                    message_id=message_envelope.message_id,
+                )
+                with (
+                    self._tracer_helper.trace_block("process", recipient_agent.id, parent=message_envelope.metadata),
+                    MessageHandlerContext.populate_context(recipient_agent.id),
+                ):
+                    response = await recipient_agent.on_message(
+                        message_envelope.message,
+                        ctx=message_context,
+                    )
+            except CancelledError as e:
+                if not message_envelope.future.cancelled():
+                    message_envelope.future.set_exception(e)
+                self._message_queue.task_done()
+                event_logger.info(
+                    MessageHandlerExceptionEvent(
+                        payload=self._try_serialize(message_envelope.message),
+                        handling_agent=recipient,
+                        exception=e,
+                    )
+                )
+                return
+            except BaseException as e:
+                message_envelope.future.set_exception(e)
+                self._message_queue.task_done()
+                event_logger.info(
+                    MessageHandlerExceptionEvent(
+                        payload=self._try_serialize(message_envelope.message),
+                        handling_agent=recipient,
+                        exception=e,
+                    )
+                )
+                return
+
+            event_logger.info(
+                MessageEvent(
+                    payload=self._try_serialize(response),
+                    sender=message_envelope.recipient,
+                    receiver=message_envelope.sender,
+                    kind=MessageKind.RESPOND,
+                    delivery_stage=DeliveryStage.SEND,
+                )
+            )
+
+            await self._message_queue.put(
+                ResponseMessageEnvelope(
+                    message=response,
+                    future=message_envelope.future,
+                    sender=message_envelope.recipient,
+                    recipient=message_envelope.sender,
+                    metadata=get_telemetry_envelope_metadata(),
+                )
+            )
+            self._message_queue.task_done()
+
+    async def _process_publish(self, message_envelope: PublishMessageEnvelope) -> None:
+        with self._tracer_helper.trace_block("publish", message_envelope.topic_id, parent=message_envelope.metadata):
+            try:
+                responses: list[Awaitable[Any]] = []
+                recipients = await self._subscription_manager.get_subscribed_recipients(message_envelope.topic_id)
+                for agent_id in recipients:
+                    # Avoid sending the message back to the sender
+                    if message_envelope.sender is not None and agent_id == message_envelope.sender:
+                        continue
+
+                    sender_agent = (
+                        await self._get_agent(message_envelope.sender) if message_envelope.sender is not None else None
+                    )
+                    sender_name = str(sender_agent.id) if sender_agent is not None else "Unknown"
+                    logger.info(
+                        f"Calling message handler for {agent_id.type} with message type "
+                        f"{type(message_envelope.message).__name__} published by {sender_name}"
+                    )
+                    event_logger.info(
+                        MessageEvent(
+                            payload=self._try_serialize(message_envelope.message),
+                            sender=message_envelope.sender,
+                            receiver=None,
+                            kind=MessageKind.PUBLISH,
+                            delivery_stage=DeliveryStage.DELIVER,
+                        )
+                    )
+                    message_context = MessageContext(
+                        sender=message_envelope.sender,
+                        topic_id=message_envelope.topic_id,
+                        is_rpc=False,
+                        cancellation_token=message_envelope.cancellation_token,
+                        message_id=message_envelope.message_id,
+                    )
+                    agent = await self._get_agent(agent_id)
+
+                    async def _on_message(agent: Agent, message_context: MessageContext) -> Any:
+                        with (
+                            self._tracer_helper.trace_block("process", agent.id, parent=message_envelope.metadata),
+                            MessageHandlerContext.populate_context(agent.id),
+                        ):
+                            try:
+                                return await agent.on_message(
+                                    message_envelope.message,
+                                    ctx=message_context,
+                                )
+                            except BaseException as e:
+                                logger.error(f"Error processing publish message for {agent.id}", exc_info=True)
+                                event_logger.info(
+                                    MessageHandlerExceptionEvent(
+                                        payload=self._try_serialize(message_envelope.message),
+                                        handling_agent=agent.id,
+                                        exception=e,
+                                    )
+                                )
+                                raise e
+
+                    future = _on_message(agent, message_context)
+                    responses.append(future)
+
+                await asyncio.gather(*responses)
+            except BaseException as e:
+                if not self._ignore_unhandled_handler_exceptions:
+                    self._background_exception = e
+            finally:
+                self._message_queue.task_done()
+            # TODO(evmattso): if responses are given for a publish
+
+    async def _process_response(self, message_envelope: ResponseMessageEnvelope) -> None:
+        with self._tracer_helper.trace_block("ack", message_envelope.recipient, parent=message_envelope.metadata):
+            content = (
+                message_envelope.message.__dict__
+                if hasattr(message_envelope.message, "__dict__")
+                else message_envelope.message
+            )
+            logger.info(
+                f"Resolving response with message type {type(message_envelope.message).__name__} for recipient "
+                f"{message_envelope.recipient} from {message_envelope.sender.type}: {content}"
+            )
+            event_logger.info(
+                MessageEvent(
+                    payload=self._try_serialize(message_envelope.message),
+                    sender=message_envelope.sender,
+                    receiver=message_envelope.recipient,
+                    kind=MessageKind.RESPOND,
+                    delivery_stage=DeliveryStage.DELIVER,
+                )
+            )
+            if not message_envelope.future.cancelled():
+                message_envelope.future.set_result(message_envelope.message)
+            self._message_queue.task_done()
+
+    async def process_next(self) -> None:
+        """Process the next message in the queue.
+
+        If there is an unhandled exception in the background task, it will be raised here. `process_next` cannot be
+        called again after an unhandled exception is raised.
+        """
+        await self._process_next()
+
+    async def _process_next(self) -> None:
+        """Process the next message in the queue."""
+        if self._background_exception is not None:
+            e = self._background_exception
+            self._background_exception = None
+            self._message_queue.shutdown(immediate=True)  # type: ignore
+            raise e
+
+        try:
+            message_envelope = await self._message_queue.get()
+        except QueueShutDown:
+            if self._background_exception is not None:
+                e = self._background_exception
+                self._background_exception = None
+                raise e from None
+            return
+
+        match message_envelope:
+            case SendMessageEnvelope(message=message, sender=sender, recipient=recipient, future=future):
+                if self._intervention_handlers is not None:
+                    for handler in self._intervention_handlers:
+                        with self._tracer_helper.trace_block(
+                            "intercept", handler.__class__.__name__, parent=message_envelope.metadata
+                        ):
+                            try:
+                                message_context = MessageContext(
+                                    sender=sender,
+                                    topic_id=None,
+                                    is_rpc=True,
+                                    cancellation_token=message_envelope.cancellation_token,
+                                    message_id=message_envelope.message_id,
+                                )
+                                temp_message = await handler.on_send(
+                                    message, message_context=message_context, recipient=recipient
+                                )
+                                _warn_if_none(temp_message, "on_send")
+                            except BaseException as e:
+                                future.set_exception(e)
+                                return
+                            if temp_message is DropMessage or isinstance(temp_message, DropMessage):
+                                event_logger.info(
+                                    MessageDroppedEvent(
+                                        payload=self._try_serialize(message),
+                                        sender=sender,
+                                        receiver=recipient,
+                                        kind=MessageKind.DIRECT,
+                                    )
+                                )
+                                future.set_exception(MessageDroppedException())
+                                return
+
+                        message_envelope.message = temp_message
+                task = asyncio.create_task(self._process_send(message_envelope))
+                self._background_tasks.add(task)
+                task.add_done_callback(self._background_tasks.discard)
+            case PublishMessageEnvelope(
+                message=message,
+                sender=sender,
+                topic_id=topic_id,
+            ):
+                if self._intervention_handlers is not None:
+                    for handler in self._intervention_handlers:
+                        with self._tracer_helper.trace_block(
+                            "intercept", handler.__class__.__name__, parent=message_envelope.metadata
+                        ):
+                            try:
+                                message_context = MessageContext(
+                                    sender=sender,
+                                    topic_id=topic_id,
+                                    is_rpc=False,
+                                    cancellation_token=message_envelope.cancellation_token,
+                                    message_id=message_envelope.message_id,
+                                )
+                                temp_message = await handler.on_publish(message, message_context=message_context)
+                                _warn_if_none(temp_message, "on_publish")
+                            except BaseException as e:
+                                # TODO(evmattso): we should raise the intervention exception to the publisher.
+                                logger.error(f"Exception raised in in intervention handler: {e}", exc_info=True)
+                                return
+                            if temp_message is DropMessage or isinstance(temp_message, DropMessage):
+                                event_logger.info(
+                                    MessageDroppedEvent(
+                                        payload=self._try_serialize(message),
+                                        sender=sender,
+                                        receiver=topic_id,
+                                        kind=MessageKind.PUBLISH,
+                                    )
+                                )
+                                return
+
+                        message_envelope.message = temp_message
+
+                task = asyncio.create_task(self._process_publish(message_envelope))
+                self._background_tasks.add(task)
+                task.add_done_callback(self._background_tasks.discard)
+            case ResponseMessageEnvelope(message=message, sender=sender, recipient=recipient, future=future):
+                if self._intervention_handlers is not None:
+                    for handler in self._intervention_handlers:
+                        try:
+                            temp_message = await handler.on_response(message, sender=sender, recipient=recipient)
+                            _warn_if_none(temp_message, "on_response")
+                        except BaseException as e:
+                            # TODO(evmattso): should we raise the exception to sender of the response instead?
+                            future.set_exception(e)
+                            return
+                        if temp_message is DropMessage or isinstance(temp_message, DropMessage):
+                            event_logger.info(
+                                MessageDroppedEvent(
+                                    payload=self._try_serialize(message),
+                                    sender=sender,
+                                    receiver=recipient,
+                                    kind=MessageKind.RESPOND,
+                                )
+                            )
+                            future.set_exception(MessageDroppedException())
+                            return
+                        message_envelope.message = temp_message
+                task = asyncio.create_task(self._process_response(message_envelope))
+                self._background_tasks.add(task)
+                task.add_done_callback(self._background_tasks.discard)
+
+        # Yield control to the message loop to allow other tasks to run
+        await asyncio.sleep(0)
+
+    def start(self) -> None:
+        """Start the runtime message processing loop. This runs in a background task."""
+        if self._run_context is not None:
+            raise RuntimeError("Runtime is already started")
+        self._run_context = RunContext(self)
+
+    async def close(self) -> None:
+        """Calls :meth:`stop` if applicable and the :meth:`Agent.close` method on all instantiated agents."""
+        # stop the runtime if it hasn't been stopped yet
+        if self._run_context is not None:
+            await self.stop()
+        # close all the agents that have been instantiated
+        for agent_id in self._instantiated_agents:
+            agent = await self._get_agent(agent_id)
+            await agent.close()
+
+    async def stop(self) -> None:
+        """Immediately stop the runtime message processing loop.
+
+        The currently processing message will be completed, but all others following it will be discarded.
+        """
+        if self._run_context is None:
+            raise RuntimeError("Runtime is not started")
+
+        try:
+            await self._run_context.stop()
+        finally:
+            self._run_context = None
+            self._message_queue = Queue()
+
+    async def stop_when_idle(self) -> None:
+        """Stop the runtime message processing loop when there is no outstanding message being processed or queued.
+
+        This is the most common way to stop the runtime.
+        """
+        if self._run_context is None:
+            raise RuntimeError("Runtime is not started")
+
+        try:
+            await self._run_context.stop_when_idle()
+        finally:
+            self._run_context = None
+            self._message_queue = Queue()
+
+    async def stop_when(self, condition: Callable[[], bool]) -> None:
+        """Stop the runtime message processing loop when the condition is met.
+
+        .. caution::
+
+            This method is not recommended to be used, and is here for legacy
+            reasons. It will spawn a busy loop to continually check the
+            condition. It is much more efficient to call `stop_when_idle` or
+            `stop` instead. If you need to stop the runtime based on a
+            condition, consider using a background task and asyncio.Event to
+            signal when the condition is met and the background task should call
+            stop.
+
+        """
+        if self._run_context is None:
+            raise RuntimeError("Runtime is not started")
+        await self._run_context.stop_when(condition)
+
+        self._run_context = None
+        self._message_queue = Queue()
+
+    async def agent_metadata(self, agent: AgentId) -> AgentMetadata:
+        """Get the metadata for an agent."""
+        return (await self._get_agent(agent)).metadata
+
+    async def agent_save_state(self, agent: AgentId) -> Mapping[str, Any]:
+        """Save the state of a single agent."""
+        return await (await self._get_agent(agent)).save_state()
+
+    async def agent_load_state(self, agent: AgentId, state: Mapping[str, Any]) -> None:
+        """Load the state of a single agent."""
+        await (await self._get_agent(agent)).load_state(state)
+
+    async def register_factory(
+        self,
+        type: str | AgentType,
+        agent_factory: Callable[[], T | Awaitable[T]],
+        *,
+        expected_class: type[T] | None = None,
+    ) -> AgentType:
+        """Register a factory for creating agents."""
+        if isinstance(type, str):
+            type = CoreAgentType(type)
+
+        if type.type in self._agent_factories:
+            raise ValueError(f"Agent with type {type} already exists.")
+
+        async def factory_wrapper() -> T:
+            maybe_agent_instance = agent_factory()
+            if inspect.isawaitable(maybe_agent_instance):
+                agent_instance = await maybe_agent_instance
+            else:
+                agent_instance = maybe_agent_instance
+
+            if expected_class is not None and type_func_alias(agent_instance) != expected_class:
+                raise ValueError("Factory registered using the wrong type.")
+
+            return agent_instance
+
+        self._agent_factories[type.type] = factory_wrapper
+
+        return type
+
+    async def _invoke_agent_factory(
+        self,
+        agent_factory: Callable[[], T | Awaitable[T]] | Callable[[CoreRuntime, AgentId], T | Awaitable[T]],
+        agent_id: AgentId,
+    ) -> T:
+        with AgentInstantiationContext.populate_context((self, agent_id)):
+            try:
+                if len(inspect.signature(agent_factory).parameters) == 0:
+                    factory_one = cast(Callable[[], T], agent_factory)
+                    agent = factory_one()
+                elif len(inspect.signature(agent_factory).parameters) == 2:
+                    warnings.warn(
+                        "Agent factories that take two arguments are deprecated. Use AgentInstantiationContext "
+                        "instead. Two arg factories will be removed in a future version.",
+                        stacklevel=2,
+                    )
+                    factory_two = cast(Callable[[CoreRuntime, AgentId], T], agent_factory)
+                    agent = factory_two(self, agent_id)
+                else:
+                    raise ValueError("Agent factory must take 0 or 2 arguments.")
+
+                if inspect.isawaitable(agent):
+                    return cast(T, await agent)
+
+                return agent
+
+            except BaseException as e:
+                event_logger.info(
+                    AgentConstructionExceptionEvent(
+                        agent_id=agent_id,
+                        exception=e,
+                    )
+                )
+                logger.error(f"Error constructing agent {agent_id}", exc_info=True)
+                raise
+
+    async def _get_agent(self, agent_id: AgentId) -> Agent:
+        if agent_id in self._instantiated_agents:
+            return self._instantiated_agents[agent_id]
+
+        if agent_id.type not in self._agent_factories:
+            raise LookupError(f"Agent with name {agent_id.type} not found.")
+
+        agent_factory = self._agent_factories[agent_id.type]
+        agent = await self._invoke_agent_factory(agent_factory, agent_id)
+        self._instantiated_agents[agent_id] = agent
+        return agent
+
+    # TODO(evmattso): uncomment out the following type ignore when this is fixed in mypy: https://github.com/python/mypy/issues/3737
+    async def try_get_underlying_agent_instance(self, id: AgentId, type: type[T] = Agent) -> T:  # type: ignore[assignment]
+        """Try to get the underlying agent instance by name and namespace."""
+        if id.type not in self._agent_factories:
+            raise LookupError(f"Agent with name {id.type} not found.")
+
+        # TODO(evmattso): check if remote
+        agent_instance = await self._get_agent(id)
+
+        if not isinstance(agent_instance, type):
+            raise TypeError(
+                f"Agent with name {id.type} is not of type {type.__name__}. "
+                f"It is of type {type_func_alias(agent_instance).__name__}"
+            )
+
+        return agent_instance
+
+    async def add_subscription(self, subscription: Subscription) -> None:
+        """Add a subscription to the runtime."""
+        await self._subscription_manager.add_subscription(subscription)
+
+    async def remove_subscription(self, id: str) -> None:
+        """Remove a subscription from the runtime."""
+        await self._subscription_manager.remove_subscription(id)
+
+    async def get(
+        self, id_or_type: AgentId | AgentType | str, /, key: str = "default", *, lazy: bool = True
+    ) -> AgentId:
+        """Get an agent by id or type."""
+        return await get_impl(
+            id_or_type=id_or_type,
+            key=key,
+            lazy=lazy,
+            instance_getter=self._get_agent,
+        )
+
+    def add_message_serializer(self, serializer: MessageSerializer[Any] | Sequence[MessageSerializer[Any]]) -> None:
+        """Add a message serializer to the runtime."""
+        self._serialization_registry.add_serializer(serializer)
+
+    def _try_serialize(self, message: Any) -> str:
+        try:
+            type_name = self._serialization_registry.type_name(message)
+            return self._serialization_registry.serialize(
+                message, type_name=type_name, data_content_type=JSON_DATA_CONTENT_TYPE
+            ).decode("utf-8")
+        except ValueError:
+            return "Message could not be serialized"

python/semantic_kernel/agents/runtime/in_process/message_handler_context.py

@@ -0,0 +1,41 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Generator
+from contextlib import contextmanager
+from contextvars import ContextVar
+from typing import Any, ClassVar
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class MessageHandlerContext:
+    """Context for message handlers."""
+
+    def __init__(self) -> None:
+        """Instantiate the MessageHandlerContext class."""
+        raise RuntimeError(
+            "MessageHandlerContext cannot be instantiated. It is a static class that provides context management for "
+            "message handling."
+        )
+
+    _MESSAGE_HANDLER_CONTEXT: ClassVar[ContextVar[AgentId]] = ContextVar("_MESSAGE_HANDLER_CONTEXT")
+
+    @classmethod
+    @contextmanager
+    def populate_context(cls, ctx: AgentId) -> Generator[None, Any, None]:
+        """Populate the context with the current agent ID."""
+        token = MessageHandlerContext._MESSAGE_HANDLER_CONTEXT.set(ctx)
+        try:
+            yield
+        finally:
+            MessageHandlerContext._MESSAGE_HANDLER_CONTEXT.reset(token)
+
+    @classmethod
+    def agent_id(cls) -> AgentId:
+        """Get the current agent ID."""
+        try:
+            return cls._MESSAGE_HANDLER_CONTEXT.get()
+        except LookupError as e:
+            raise RuntimeError("MessageHandlerContext.agent_id() must be called within a message handler.") from e

python/semantic_kernel/agents/runtime/in_process/queue.py

@@ -0,0 +1,274 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Copy of Asyncio queue: https://github.com/python/cpython/blob/main/Lib/asyncio/queues.py
+# So that shutdown can be used in <3.13
+# Modified to work outside of the asyncio package
+
+import asyncio
+import collections
+import threading
+from typing import Generic, TypeVar
+
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+_global_lock = threading.Lock()
+
+
+class _LoopBoundMixin:
+    _loop = None
+
+    def _get_loop(self) -> asyncio.AbstractEventLoop:
+        loop = asyncio.get_running_loop()
+
+        if self._loop is None:
+            with _global_lock:
+                if self._loop is None:
+                    self._loop = loop
+        if loop is not self._loop:
+            raise RuntimeError(f"{self!r} is bound to a different event loop")
+        return loop
+
+
+@experimental
+class QueueShutDown(Exception):
+    """Raised when putting on to or getting from a shut-down Queue."""
+
+    pass
+
+
+T = TypeVar("T")
+
+
+@experimental
+class Queue(_LoopBoundMixin, Generic[T]):
+    """A queue class that supports async operations."""
+
+    def __init__(self, maxsize: int = 0):
+        """Initialize the queue."""
+        self._maxsize = maxsize
+        self._getters = collections.deque[asyncio.Future[None]]()
+        self._putters = collections.deque[asyncio.Future[None]]()
+        self._unfinished_tasks = 0
+        self._finished = asyncio.Event()
+        self._finished.set()
+        self._queue = collections.deque[T]()
+        self._is_shutdown = False
+
+    # These three are overridable in subclasses.
+
+    def _get(self) -> T:
+        return self._queue.popleft()
+
+    def _put(self, item: T) -> None:
+        self._queue.append(item)
+
+    # End of the overridable methods.
+
+    def _wakeup_next(self, waiters: collections.deque[asyncio.Future[None]]) -> None:
+        # Wake up the next waiter (if any) that isn't cancelled.
+        while waiters:
+            waiter = waiters.popleft()
+            if not waiter.done():
+                waiter.set_result(None)
+                break
+
+    def __repr__(self) -> str:
+        """Generate a string representation of the Queue."""
+        return f"<{type(self).__name__} at {id(self):#x} {self._format()}>"
+
+    def __str__(self) -> str:
+        """Convert the Queue to a string."""
+        return f"<{type(self).__name__} {self._format()}>"
+
+    def _format(self) -> str:
+        result = f"maxsize={self._maxsize!r}"
+        if getattr(self, "_queue", None):
+            result += f" _queue={list(self._queue)!r}"
+        if self._getters:
+            result += f" _getters[{len(self._getters)}]"
+        if self._putters:
+            result += f" _putters[{len(self._putters)}]"
+        if self._unfinished_tasks:
+            result += f" tasks={self._unfinished_tasks}"
+        if self._is_shutdown:
+            result += " shutdown"
+        return result
+
+    def qsize(self) -> int:
+        """Number of items in the queue."""
+        return len(self._queue)
+
+    @property
+    def maxsize(self) -> int:
+        """Number of items allowed in the queue."""
+        return self._maxsize
+
+    def empty(self) -> bool:
+        """Return True if the queue is empty, False otherwise."""
+        return not self._queue
+
+    def full(self) -> bool:
+        """Return True if there are maxsize items in the queue.
+
+        Note: if the Queue was initialized with maxsize=0 (the default),
+        then full() is never True.
+        """
+        if self._maxsize <= 0:
+            return False
+        return self.qsize() >= self._maxsize
+
+    async def put(self, item: T) -> None:
+        """Put an item into the queue.
+
+        Put an item into the queue. If the queue is full, wait until a free
+        slot is available before adding item.
+
+        Raises QueueShutDown if the queue has been shut down.
+        """
+        while self.full():
+            if self._is_shutdown:
+                raise QueueShutDown
+            putter = self._get_loop().create_future()
+            self._putters.append(putter)
+            try:
+                await putter
+            except:
+                putter.cancel()  # Just in case putter is not done yet.
+                try:  # noqa: SIM105
+                    # Clean self._putters from canceled putters.
+                    self._putters.remove(putter)
+                except ValueError:
+                    # The putter could be removed from self._putters by a
+                    # previous get_nowait call or a shutdown call.
+                    pass
+                if not self.full() and not putter.cancelled():
+                    # We were woken up by get_nowait(), but can't take
+                    # the call.  Wake up the next in line.
+                    self._wakeup_next(self._putters)
+                raise
+        return self.put_nowait(item)
+
+    def put_nowait(self, item: T) -> None:
+        """Put an item into the queue without blocking.
+
+        If no free slot is immediately available, raise QueueFull.
+
+        Raises QueueShutDown if the queue has been shut down.
+        """
+        if self._is_shutdown:
+            raise QueueShutDown
+        if self.full():
+            raise asyncio.QueueFull
+        self._put(item)
+        self._unfinished_tasks += 1
+        self._finished.clear()
+        self._wakeup_next(self._getters)
+
+    async def get(self) -> T:
+        """Remove and return an item from the queue.
+
+        If queue is empty, wait until an item is available.
+
+        Raises QueueShutDown if the queue has been shut down and is empty, or
+        if the queue has been shut down immediately.
+        """
+        while self.empty():
+            if self._is_shutdown and self.empty():
+                raise QueueShutDown
+            getter = self._get_loop().create_future()
+            self._getters.append(getter)
+            try:
+                await getter
+            except:
+                getter.cancel()  # Just in case getter is not done yet.
+                try:  # noqa: SIM105
+                    # Clean self._getters from canceled getters.
+                    self._getters.remove(getter)
+                except ValueError:
+                    # The getter could be removed from self._getters by a
+                    # previous put_nowait call, or a shutdown call.
+                    pass
+                if not self.empty() and not getter.cancelled():
+                    # We were woken up by put_nowait(), but can't take
+                    # the call.  Wake up the next in line.
+                    self._wakeup_next(self._getters)
+                raise
+        return self.get_nowait()
+
+    def get_nowait(self) -> T:
+        """Remove and return an item from the queue.
+
+        Return an item if one is immediately available, else raise QueueEmpty.
+
+        Raises QueueShutDown if the queue has been shut down and is empty, or
+        if the queue has been shut down immediately.
+        """
+        if self.empty():
+            if self._is_shutdown:
+                raise QueueShutDown
+            raise asyncio.QueueEmpty
+        item = self._get()
+        self._wakeup_next(self._putters)
+        return item
+
+    def task_done(self) -> None:
+        """Indicate that a formerly enqueued task is complete.
+
+        Used by queue consumers. For each get() used to fetch a task,
+        a subsequent call to task_done() tells the queue that the processing
+        on the task is complete.
+
+        If a join() is currently blocking, it will resume when all items have
+        been processed (meaning that a task_done() call was received for every
+        item that had been put() into the queue).
+
+        shutdown(immediate=True) calls task_done() for each remaining item in
+        the queue.
+
+        Raises ValueError if called more times than there were items placed in
+        the queue.
+        """
+        if self._unfinished_tasks <= 0:
+            raise ValueError("task_done() called too many times")
+        self._unfinished_tasks -= 1
+        if self._unfinished_tasks == 0:
+            self._finished.set()
+
+    async def join(self) -> None:
+        """Block until all items in the queue have been gotten and processed.
+
+        The count of unfinished tasks goes up whenever an item is added to the
+        queue. The count goes down whenever a consumer calls task_done() to
+        indicate that the item was retrieved and all work on it is complete.
+        When the count of unfinished tasks drops to zero, join() unblocks.
+        """
+        if self._unfinished_tasks > 0:
+            await self._finished.wait()
+
+    def shutdown(self, immediate: bool = False) -> None:
+        """Shut-down the queue, making queue gets and puts raise QueueShutDown.
+
+        By default, gets will only raise once the queue is empty. Set
+        'immediate' to True to make gets raise immediately instead.
+
+        All blocked callers of put() and get() will be unblocked. If
+        'immediate', a task is marked as done for each item remaining in
+        the queue, which may unblock callers of join().
+        """
+        self._is_shutdown = True
+        if immediate:
+            while not self.empty():
+                self._get()
+                if self._unfinished_tasks > 0:
+                    self._unfinished_tasks -= 1
+            if self._unfinished_tasks == 0:
+                self._finished.set()
+        # All getters need to re-check queue-empty to raise ShutDown
+        while self._getters:
+            getter = self._getters.popleft()
+            if not getter.done():
+                getter.set_result(None)
+        while self._putters:
+            putter = self._putters.popleft()
+            if not putter.done():
+                putter.set_result(None)

python/semantic_kernel/agents/runtime/in_process/runtime_impl_helpers.py

@@ -0,0 +1,94 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections import defaultdict
+from collections.abc import Awaitable, Callable, Sequence
+from typing import DefaultDict
+
+from semantic_kernel.agents.runtime.core.agent import Agent
+from semantic_kernel.agents.runtime.core.agent_id import AgentId, CoreAgentId
+from semantic_kernel.agents.runtime.core.agent_type import AgentType
+from semantic_kernel.agents.runtime.core.subscription import Subscription
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+async def get_impl(
+    *,
+    id_or_type: AgentId | AgentType | str,
+    key: str,
+    lazy: bool,
+    instance_getter: Callable[[AgentId], Awaitable[Agent]],
+) -> AgentId:
+    """Get the implementation of an agent."""
+    if isinstance(id_or_type, AgentId):
+        if not lazy:
+            await instance_getter(id_or_type)
+
+        return id_or_type
+
+    type_str = id_or_type if isinstance(id_or_type, str) else id_or_type.type
+    id = CoreAgentId(type_str, key)
+    if not lazy:
+        await instance_getter(id)
+
+    return id
+
+
+@experimental
+class SubscriptionManager:
+    """Manages subscriptions for agents."""
+
+    def __init__(self) -> None:
+        """Initialize the SubscriptionManager."""
+        self._subscriptions: list[Subscription] = []
+        self._seen_topics: set[TopicId] = set()
+        self._subscribed_recipients: DefaultDict[TopicId, list[AgentId]] = defaultdict(list)
+
+    @property
+    def subscriptions(self) -> Sequence[Subscription]:
+        """Get the list of subscriptions."""
+        return self._subscriptions
+
+    async def add_subscription(self, subscription: Subscription) -> None:
+        """Add a subscription to the manager."""
+        # Check if the subscription already exists
+        if any(sub == subscription for sub in self._subscriptions):
+            raise ValueError("Subscription already exists")
+
+        self._subscriptions.append(subscription)
+        self._rebuild_subscriptions(self._seen_topics)
+
+    async def remove_subscription(self, id: str) -> None:
+        """Remove a subscription from the manager."""
+        # Check if the subscription exists
+        if not any(sub.id == id for sub in self._subscriptions):
+            raise ValueError("Subscription does not exist")
+
+        def is_not_sub(x: Subscription) -> bool:
+            return x.id != id
+
+        self._subscriptions = list(filter(is_not_sub, self._subscriptions))
+
+        # Rebuild the subscriptions
+        self._rebuild_subscriptions(self._seen_topics)
+
+    async def get_subscribed_recipients(self, topic: TopicId) -> list[AgentId]:
+        """Get the list of recipients subscribed to a topic."""
+        if topic not in self._seen_topics:
+            self._build_for_new_topic(topic)
+        return self._subscribed_recipients[topic]
+
+    # TODO(evmattso): optimize this...
+    def _rebuild_subscriptions(self, topics: set[TopicId]) -> None:
+        """Rebuild the subscriptions for the given topics."""
+        self._subscribed_recipients.clear()
+        for topic in topics:
+            self._build_for_new_topic(topic)
+
+    def _build_for_new_topic(self, topic: TopicId) -> None:
+        """Build the subscriptions for a new topic."""
+        self._seen_topics.add(topic)
+        for subscription in self._subscriptions:
+            if subscription.is_match(topic):
+                self._subscribed_recipients[topic].append(subscription.map_to_agent(topic))

python/semantic_kernel/agents/runtime/in_process/subscription_context.py

@@ -0,0 +1,45 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Generator
+from contextlib import contextmanager
+from contextvars import ContextVar
+from typing import Any, ClassVar
+
+from semantic_kernel.agents.runtime.core.agent_type import AgentType
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class SubscriptionInstantiationContext:
+    """Context manager for subscription instantiation."""
+
+    def __init__(self) -> None:
+        """Prevent instantiation of SubscriptionInstantiationContext."""
+        raise RuntimeError(
+            "SubscriptionInstantiationContext cannot be instantiated. It is a static class that provides context "
+            "management for subscription instantiation."
+        )
+
+    _SUBSCRIPTION_CONTEXT_VAR: ClassVar[ContextVar[AgentType]] = ContextVar("_SUBSCRIPTION_CONTEXT_VAR")
+
+    @classmethod
+    @contextmanager
+    def populate_context(cls, ctx: AgentType) -> Generator[None, Any, None]:
+        """Populate the context with the agent type."""
+        token = SubscriptionInstantiationContext._SUBSCRIPTION_CONTEXT_VAR.set(ctx)
+        try:
+            yield
+        finally:
+            SubscriptionInstantiationContext._SUBSCRIPTION_CONTEXT_VAR.reset(token)
+
+    @classmethod
+    def agent_type(cls) -> AgentType:
+        """Get the agent type from the context."""
+        try:
+            return cls._SUBSCRIPTION_CONTEXT_VAR.get()
+        except LookupError as e:
+            raise RuntimeError(
+                "SubscriptionInstantiationContext.runtime() must be called within an instantiation context such as "
+                "when the AgentRuntime is instantiating an agent. Mostly likely this was caused by directly "
+                "instantiating an agent instead of using the AgentRuntime to do so."
+            ) from e

python/semantic_kernel/agents/runtime/in_process/type_prefix_subscription.py

@@ -0,0 +1,67 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import uuid
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId, CoreAgentId
+from semantic_kernel.agents.runtime.core.agent_type import AgentType
+from semantic_kernel.agents.runtime.core.exceptions import CantHandleException
+from semantic_kernel.agents.runtime.core.subscription import Subscription
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class TypePrefixSubscription(Subscription):
+    """This subscription matches on topics based on a prefix of the type and maps to agents.
+
+    It uses the source of the topic as the agent key. This subscription causes each source to have
+    its own agent instance.
+
+    Args:
+        topic_type_prefix (str): Topic type prefix to match against
+        agent_type (str): Agent type to handle this subscription
+    """
+
+    def __init__(self, topic_type_prefix: str, agent_type: str | AgentType, id: str | None = None):
+        """Initialize the TypePrefixSubscription."""
+        self._topic_type_prefix = topic_type_prefix
+        if isinstance(agent_type, AgentType):
+            self._agent_type = agent_type.type
+        else:
+            self._agent_type = agent_type
+        self._id = id or str(uuid.uuid4())
+
+    @property
+    def id(self) -> str:
+        """Get the id of the subscription."""
+        return self._id
+
+    @property
+    def topic_type_prefix(self) -> str:
+        """Get the topic type prefix of the subscription."""
+        return self._topic_type_prefix
+
+    @property
+    def agent_type(self) -> str:
+        """Get the agent type of the subscription."""
+        return self._agent_type
+
+    def is_match(self, topic_id: TopicId) -> bool:
+        """Check if the topic_id matches the subscription."""
+        return topic_id.type.startswith(self._topic_type_prefix)
+
+    def map_to_agent(self, topic_id: TopicId) -> AgentId:
+        """Map the topic_id to an agent_id."""
+        if not self.is_match(topic_id):
+            raise CantHandleException("TopicId does not match the subscription")
+
+        return CoreAgentId(type=self._agent_type, key=topic_id.source)
+
+    def __eq__(self, other: object) -> bool:
+        """Check if two subscriptions are equal."""
+        if not isinstance(other, TypePrefixSubscription):
+            return False
+
+        return self.id == other.id or (
+            self.agent_type == other.agent_type and self.topic_type_prefix == other.topic_type_prefix
+        )

python/semantic_kernel/agents/runtime/in_process/type_subscription.py

@@ -0,0 +1,67 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import uuid
+
+from semantic_kernel.agents.runtime.core.agent_id import AgentId, CoreAgentId
+from semantic_kernel.agents.runtime.core.agent_type import AgentType
+from semantic_kernel.agents.runtime.core.exceptions import CantHandleException
+from semantic_kernel.agents.runtime.core.subscription import Subscription
+from semantic_kernel.agents.runtime.core.topic import TopicId
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+
+@experimental
+class TypeSubscription(Subscription):
+    """This subscription matches on topics based on the type and maps to agents.
+
+    It uses the source of the topic as the agent key. This subscription causes each source to have
+    its own agent instance.
+    """
+
+    def __init__(self, topic_type: str, agent_type: str | AgentType, id: str | None = None):
+        """Initialize the TypeSubscription.
+
+        Args:
+            topic_type (str): Topic type to match against
+            agent_type (str): Agent type to handle this subscription
+            id (str | None): Id of the subscription. If None, a new id will be generated.
+        """
+        self._topic_type = topic_type
+        if isinstance(agent_type, AgentType):
+            self._agent_type = agent_type.type
+        else:
+            self._agent_type = agent_type
+        self._id = id or str(uuid.uuid4())
+
+    @property
+    def id(self) -> str:
+        """Get the id of the subscription."""
+        return self._id
+
+    @property
+    def topic_type(self) -> str:
+        """Get the topic type of the subscription."""
+        return self._topic_type
+
+    @property
+    def agent_type(self) -> str:
+        """Get the agent type of the subscription."""
+        return self._agent_type
+
+    def is_match(self, topic_id: TopicId) -> bool:
+        """Check if the topic_id matches the subscription."""
+        return topic_id.type == self._topic_type
+
+    def map_to_agent(self, topic_id: TopicId) -> AgentId:
+        """Map the topic_id to an agent_id."""
+        if not self.is_match(topic_id):
+            raise CantHandleException("TopicId does not match the subscription")
+
+        return CoreAgentId(type=self._agent_type, key=topic_id.source)
+
+    def __eq__(self, other: object) -> bool:
+        """Check if two subscriptions are equal."""
+        if not isinstance(other, TypeSubscription):
+            return False
+
+        return self.id == other.id or (self.agent_type == other.agent_type and self.topic_type == other.topic_type)

python/semantic_kernel/agents/strategies/__init__.py

@@ -0,0 +1,23 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from semantic_kernel.agents.strategies.selection.kernel_function_selection_strategy import (
+    KernelFunctionSelectionStrategy,
+)
+from semantic_kernel.agents.strategies.selection.selection_strategy import SelectionStrategy
+from semantic_kernel.agents.strategies.selection.sequential_selection_strategy import SequentialSelectionStrategy
+from semantic_kernel.agents.strategies.termination.aggregator_termination_strategy import AggregatorTerminationStrategy
+from semantic_kernel.agents.strategies.termination.default_termination_strategy import DefaultTerminationStrategy
+from semantic_kernel.agents.strategies.termination.kernel_function_termination_strategy import (
+    KernelFunctionTerminationStrategy,
+)
+from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
+
+__all__ = [
+    "AggregatorTerminationStrategy",
+    "DefaultTerminationStrategy",
+    "KernelFunctionSelectionStrategy",
+    "KernelFunctionTerminationStrategy",
+    "SelectionStrategy",
+    "SequentialSelectionStrategy",
+    "TerminationStrategy",
+]

python/semantic_kernel/agents/strategies/selection/kernel_function_selection_strategy.py

@@ -0,0 +1,116 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from collections.abc import Callable
+from inspect import isawaitable
+from typing import TYPE_CHECKING, ClassVar
+
+from pydantic import Field
+
+from semantic_kernel.agents.strategies.selection.selection_strategy import SelectionStrategy
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.history_reducer.chat_history_reducer import ChatHistoryReducer
+from semantic_kernel.exceptions.agent_exceptions import AgentExecutionException
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.functions.kernel_function import KernelFunction
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.agents import Agent
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class KernelFunctionSelectionStrategy(SelectionStrategy):
+    """Determines agent selection based on the evaluation of a Kernel Function."""
+
+    DEFAULT_AGENT_VARIABLE_NAME: ClassVar[str] = "_agent_"
+    DEFAULT_HISTORY_VARIABLE_NAME: ClassVar[str] = "_history_"
+
+    agent_variable_name: str | None = Field(default=DEFAULT_AGENT_VARIABLE_NAME)
+    history_variable_name: str | None = Field(default=DEFAULT_HISTORY_VARIABLE_NAME)
+    arguments: KernelArguments | None = None
+    function: KernelFunction
+    kernel: Kernel
+    result_parser: Callable[..., str] = Field(default_factory=lambda: (lambda: ""))
+    history_reducer: ChatHistoryReducer | None = None
+
+    @override
+    async def select_agent(self, agents: list["Agent"], history: list[ChatMessageContent]) -> "Agent":
+        """Select the next agent to interact with.
+
+        Args:
+            agents: The list of agents to select from.
+            history: The history of messages in the conversation.
+
+        Returns:
+            The next agent to interact with.
+
+        Raises:
+            AgentExecutionException: If the strategy fails to execute the function or select the next agent
+        """
+        if self.history_reducer is not None:
+            self.history_reducer.messages = history
+            reduced_history = await self.history_reducer.reduce()
+            if reduced_history is not None:
+                history = reduced_history.messages
+
+        original_arguments = self.arguments or KernelArguments()
+        execution_settings = original_arguments.execution_settings or {}
+
+        messages = [message.to_dict(role_key="role", content_key="content") for message in history]
+
+        filtered_arguments = {
+            self.agent_variable_name: ",".join(agent.name for agent in agents),
+            self.history_variable_name: messages,
+        }
+
+        extracted_settings = {key: setting.model_dump() for key, setting in execution_settings.items()}
+
+        combined_arguments = {
+            **original_arguments,
+            **extracted_settings,
+            **{k: v for k, v in filtered_arguments.items()},
+        }
+
+        arguments = KernelArguments(
+            **combined_arguments,
+        )
+
+        logger.info(
+            f"Kernel Function Selection Strategy next method called, "
+            f"invoking function: {self.function.plugin_name}, {self.function.name}",
+        )
+
+        try:
+            result = await self.function.invoke(kernel=self.kernel, arguments=arguments)
+        except Exception as ex:
+            logger.error("Kernel Function Selection Strategy next method failed", exc_info=ex)
+            raise AgentExecutionException("Agent Failure - Strategy failed to execute function.") from ex
+
+        logger.info(
+            f"Kernel Function Selection Strategy next method completed: "
+            f"{self.function.plugin_name}, {self.function.name}, result: {result.value if result else None}",
+        )
+
+        agent_name = self.result_parser(result)
+        if isawaitable(agent_name):
+            agent_name = await agent_name
+
+        if agent_name is None:
+            raise AgentExecutionException("Agent Failure - Strategy unable to determine next agent.")
+
+        agent_turn = next((agent for agent in agents if agent.name == agent_name), None)
+        if agent_turn is None:
+            raise AgentExecutionException(f"Agent Failure - Strategy unable to select next agent: {agent_name}")
+
+        return agent_turn

python/semantic_kernel/agents/strategies/selection/selection_strategy.py

@@ -0,0 +1,57 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from abc import ABC
+from typing import TYPE_CHECKING
+
+from semantic_kernel.agents import Agent
+from semantic_kernel.exceptions.agent_exceptions import AgentExecutionException
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.contents.chat_message_content import ChatMessageContent
+
+
+@experimental
+class SelectionStrategy(KernelBaseModel, ABC):
+    """Base strategy class for selecting the next agent in a chat."""
+
+    has_selected: bool = False
+    initial_agent: Agent | None = None
+
+    async def next(
+        self,
+        agents: list[Agent],
+        history: list["ChatMessageContent"],
+    ) -> Agent:
+        """Select the next agent to interact with.
+
+        Args:
+            agents: The list of agents to select from.
+            history: The history of messages in the conversation.
+
+        Returns:
+            The agent who takes the next turn.
+        """
+        if not agents and self.initial_agent is None:
+            raise AgentExecutionException("Agent Failure - No agents present to select.")
+
+        # If it's the first selection and we have an initial agent, use it
+        if not self.has_selected and self.initial_agent is not None:
+            agent = self.initial_agent
+        else:
+            agent = await self.select_agent(agents, history)
+
+        self.has_selected = True
+        return agent
+
+    async def select_agent(
+        self,
+        agents: list[Agent],
+        history: list["ChatMessageContent"],
+    ) -> Agent:
+        """Determines which agent goes next. Override for custom logic.
+
+        By default, this fallback returns the first agent in the list.
+        """
+        return agents[0]

python/semantic_kernel/agents/strategies/selection/sequential_selection_strategy.py

@@ -0,0 +1,80 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import logging
+import sys
+
+if sys.version_info >= (3, 12):
+    from typing import override  # pragma: no cover
+else:
+    from typing_extensions import override  # pragma: no cover
+
+from typing import TYPE_CHECKING
+
+from pydantic import PrivateAttr
+
+from semantic_kernel.agents.strategies.selection.selection_strategy import SelectionStrategy
+from semantic_kernel.utils.feature_stage_decorator import experimental
+
+if TYPE_CHECKING:
+    from semantic_kernel.agents import Agent
+    from semantic_kernel.contents.chat_message_content import ChatMessageContent
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@experimental
+class SequentialSelectionStrategy(SelectionStrategy):
+    """Round-robin turn-taking strategy. Agent order is based on the order in which they joined."""
+
+    _index: int = PrivateAttr(default=-1)
+
+    def reset(self) -> None:
+        """Reset selection to the initial/first agent."""
+        self._index = -1
+
+    def _increment_index(self, agent_count: int) -> None:
+        """Increment the index in a circular manner."""
+        self._index = (self._index + 1) % agent_count
+
+    @override
+    async def select_agent(
+        self,
+        agents: list["Agent"],
+        history: list["ChatMessageContent"],
+    ) -> "Agent":
+        """Select the next agent in a round-robin fashion.
+
+        Args:
+            agents: The list of agents to select from.
+            history: The history of messages in the conversation.
+
+        Returns:
+            The agent who takes the next turn.
+        """
+        if self._index >= len(agents):
+            self._index = -1
+
+        if (
+            self.has_selected
+            and self.initial_agent is not None
+            and len(agents) > 0
+            and agents[0] == self.initial_agent
+            and self._index < 0
+        ):
+            # Avoid selecting the same agent twice in a row
+            self._increment_index(len(agents))
+
+        # Main index increment
+        self._increment_index(len(agents))
+
+        # Pick the agent
+        agent = agents[self._index]
+
+        logger.info(
+            "Selected agent at index %d (ID: %s, name: %s)",
+            self._index,
+            agent.id,
+            agent.name,
+        )
+        return agent