chore: import upstream snapshot with attribution

python/samples/concepts/agents/bedrock_agent/.env.example

@@ -0,0 +1,2 @@
+BEDROCK_AGENT_AGENT_RESOURCE_ROLE_ARN=[YOUR_AGENT_ROLE_AMAZON_RESOURCE_NAME]
+BEDROCK_AGENT_FOUNDATION_MODEL=[YOUR_FOUNDATION_MODEL]

python/samples/concepts/agents/bedrock_agent/README.md

@@ -0,0 +1,74 @@
+# Concept samples on how to use AWS Bedrock agents
+
+## Pre-requisites
+
+1. You need to have an AWS account and [access to the foundation models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-permissions.html)
+2. [AWS CLI installed](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) and [configured](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration)
+
+### Configuration
+
+Follow this [guide](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration) to configure your environment to use the Bedrock API.
+
+Please configure the `aws_access_key_id`, `aws_secret_access_key`, and `region` otherwise you will need to create custom clients for the services. For example:
+
+```python
+runtime_client=boto.client(
+    "bedrock-runtime",
+    aws_access_key_id="your_access_key",
+    aws_secret_access_key="your_secret_key",
+    region_name="your_region",
+    [...other parameters you may need...]
+)
+client=boto.client(
+    "bedrock",
+    aws_access_key_id="your_access_key",
+    aws_secret_access_key="your_secret_key",
+    region_name="your_region",
+    [...other parameters you may need...]
+)
+
+bedrock_agent = BedrockAgent.create_and_prepare_agent(
+    name="your_agent_name",
+    instructions="your_instructions",
+    runtime_client=runtime_client,
+    client=client,
+    [...other parameters you may need...]
+)
+```
+
+## Samples
+
+| Sample | Description |
+|--------|-------------|
+| [bedrock_agent_simple_chat.py](bedrock_agent_simple_chat.py) | Demonstrates basic usage of the Bedrock agent. |
+| [bedrock_agent_simple_chat_streaming.py](bedrock_agent_simple_chat_streaming.py) | Demonstrates basic usage of the Bedrock agent with streaming. |
+| [bedrock_agent_with_kernel_function.py](bedrock_agent_with_kernel_function.py) | Shows how to use the Bedrock agent with a kernel function. |
+| [bedrock_agent_with_kernel_function_streaming.py](bedrock_agent_with_kernel_function_streaming.py) | Shows how to use the Bedrock agent with a kernel function with streaming. |
+| [bedrock_agent_with_code_interpreter.py](bedrock_agent_with_code_interpreter.py) | Example of using the Bedrock agent with a code interpreter. |
+| [bedrock_agent_with_code_interpreter_streaming.py](bedrock_agent_with_code_interpreter_streaming.py) | Example of using the Bedrock agent with a code interpreter and streaming. |
+| [bedrock_mixed_chat_agents.py](bedrock_mixed_chat_agents.py) | Example of using multiple chat agents in a single script. |
+| [bedrock_mixed_chat_agents_streaming.py](bedrock_mixed_chat_agents_streaming.py) | Example of using multiple chat agents in a single script with streaming. |
+
+## Before running the samples
+
+You need to set up some environment variables to run the samples. Please refer to the [.env.example](.env.example) file for the required environment variables.
+
+### `BEDROCK_AGENT_AGENT_RESOURCE_ROLE_ARN`
+
+On your AWS console, go to the IAM service and go to **Roles**. Find the role you want to use and click on it. You will find the ARN in the summary section.
+
+### `BEDROCK_AGENT_FOUNDATION_MODEL`
+
+You need to make sure you have permission to access the foundation model. You can find the model ID in the [AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html). To see the models you have access to, find the policy attached to your role you should see a list of models you have access to under the `Resource` section.
+
+### How to add the `bedrock:InvokeModelWithResponseStream` action to an IAM policy
+
+1. Open the [IAM console](https://console.aws.amazon.com/iam/).
+2. On the left navigation pane, choose `Roles` under `Access management`.
+3. Find the role you want to edit and click on it.
+4. Under the `Permissions policies` tab, click on the policy you want to edit.
+5. Under the `Permissions defined in this policy` section, click on the service. You should see **Bedrock** if you already have access to the Bedrock agent service.
+6. Click on the service, and then click `Edit`.
+7. On the right, you will be able to add an action. Find the service and search for `InvokeModelWithResponseStream`.
+8. Check the box next to the action and then scroll all the way down and click `Next`.
+9. Follow the prompts to save the changes.

python/samples/concepts/agents/bedrock_agent/bedrock_agent_retrieval.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+import boto3
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+
+"""
+The following sample demonstrates how to use an already existing
+Bedrock Agent within Semantic Kernel. This sample requires that you
+have an existing agent created either previously in code or via the
+AWS Console.
+This sample uses the following main component(s):
+- a Bedrock agent
+You will learn how to retrieve a Bedrock agent and talk to it.
+"""
+
+# Replace "your-agent-id" with the ID of the agent you want to use
+AGENT_ID = "your-agent-id"
+
+
+async def main():
+    client = boto3.client("bedrock-agent")
+    agent_model = client.get_agent(agentId=AGENT_ID)["agent"]
+    bedrock_agent = BedrockAgent(agent_model)
+    thread: BedrockAgentThread = None
+
+    try:
+        while True:
+            user_input = input("User:> ")
+            if user_input == "exit":
+                print("\n\nExiting chat...")
+                break
+
+            # Invoke the agent
+            # The chat history is maintained in the session
+            async for response in bedrock_agent.invoke(
+                messages=user_input,
+                thread=thread,
+            ):
+                print(f"Bedrock agent: {response}")
+                thread = response.thread
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+    finally:
+        # Cleanup: Delete the thread
+        await thread.delete() if thread else None
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # User:> Hi, my name is John.
+    # Bedrock agent: Hello John. How can I help you?
+    # User:> What is my name?
+    # Bedrock agent: Your name is John.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_agent_simple_chat.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+
+"""
+This sample shows how to interact with a Bedrock agent in the simplest way.
+This sample uses the following main component(s):
+- a Bedrock agent
+You will learn how to create a new Bedrock agent and talk to it.
+"""
+
+AGENT_NAME = "semantic-kernel-bedrock-agent"
+INSTRUCTION = "You are a friendly assistant. You help people find information."
+
+
+async def main():
+    bedrock_agent = await BedrockAgent.create_and_prepare_agent(AGENT_NAME, instructions=INSTRUCTION)
+
+    # Create a thread for the agent
+    # If no thread is provided, a new thread will be
+    # created and returned with the initial response
+    thread: BedrockAgentThread = None
+
+    try:
+        while True:
+            user_input = input("User:> ")
+            if user_input == "exit":
+                print("\n\nExiting chat...")
+                break
+
+            # Invoke the agent
+            # The chat history is maintained in the session
+            response = await bedrock_agent.get_response(
+                messages=user_input,
+                thread=thread,
+            )
+            print(f"Bedrock agent: {response}")
+            thread = response.thread
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+    finally:
+        # Delete the agent
+        await bedrock_agent.delete_agent()
+        await thread.delete() if thread else None
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # User:> Hi, my name is John.
+    # Bedrock agent: Hello John. How can I help you?
+    # User:> What is my name?
+    # Bedrock agent: Your name is John.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_agent_simple_chat_streaming.py

@@ -0,0 +1,55 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+
+"""
+This sample shows how to interact with a Bedrock agent via streaming in the simplest way.
+This sample uses the following main component(s):
+- a Bedrock agent
+You will learn how to create a new Bedrock agent and talk to it.
+"""
+
+AGENT_NAME = "semantic-kernel-bedrock-agent"
+INSTRUCTION = "You are a friendly assistant. You help people find information."
+
+
+async def main():
+    bedrock_agent = await BedrockAgent.create_and_prepare_agent(AGENT_NAME, instructions=INSTRUCTION)
+    thread: BedrockAgentThread = None
+
+    try:
+        while True:
+            user_input = input("User:> ")
+            if user_input == "exit":
+                print("\n\nExiting chat...")
+                break
+
+            # Invoke the agent
+            # The chat history is maintained in the thread
+            print("Bedrock agent: ", end="")
+            async for response in bedrock_agent.invoke_stream(messages=user_input, thread=thread):
+                print(response, end="")
+                thread = response.thread
+            print()
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+    finally:
+        # Delete the agent
+        await bedrock_agent.delete_agent()
+        await thread.delete() if thread else None
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # User:> Hi, my name is John.
+    # Bedrock agent: Hello John. How can I help you?
+    # User:> What is my name?
+    # Bedrock agent: Your name is John.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_agent_with_code_interpreter.py

@@ -0,0 +1,85 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from pathlib import Path
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+from semantic_kernel.contents.binary_content import BinaryContent
+
+"""
+This sample shows how to interact with a Bedrock agent that is capable of writing and executing code.
+This sample uses the following main component(s):
+- a Bedrock agent
+You will learn how to create a new Bedrock agent and ask it a question that requires coding to answer.
+After running this sample, a bar chart will be generated and saved to a file in the same directory
+as this script.
+"""
+
+AGENT_NAME = "semantic-kernel-bedrock-agent"
+INSTRUCTION = "You are a friendly assistant. You help people find information."
+
+
+ASK = """
+Create a bar chart for the following data:
+Panda   5
+Tiger   8
+Lion    3
+Monkey  6
+Dolphin  2
+"""
+
+
+async def main():
+    bedrock_agent = await BedrockAgent.create_and_prepare_agent(AGENT_NAME, instructions=INSTRUCTION)
+    await bedrock_agent.create_code_interpreter_action_group()
+
+    thread: BedrockAgentThread = None
+
+    # Placeholder for the file generated by the code interpreter
+    binary_item: BinaryContent | None = None
+
+    try:
+        # Invoke the agent
+        async for response in bedrock_agent.invoke(
+            messages=ASK,
+            thread=thread,
+        ):
+            print(f"Response:\n{response}")
+            thread = response.thread
+            if not binary_item:
+                binary_item = next((item for item in response.items if isinstance(item, BinaryContent)), None)
+    finally:
+        # Delete the agent
+        await bedrock_agent.delete_agent()
+        await thread.delete() if thread else None
+
+    # Save the chart to a file
+    if not binary_item:
+        raise RuntimeError("No chart generated")
+
+    # Securely assemble the file path and validate it's within the expected directory
+    # This is a defense-in-depth measure against directory traversal attacks
+    output_dir = Path(__file__).parent.resolve()
+    file_path = (output_dir / binary_item.metadata["name"]).resolve()
+
+    # Verify the resolved path is within the expected directory
+    if not file_path.is_relative_to(output_dir):
+        raise RuntimeError("Invalid filename: would write outside the expected directory")
+
+    binary_item.write_to_file(file_path)
+    print(f"Chart saved to {file_path}")
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # Response:
+    # Here is the bar chart for the given data:
+    # [A bar chart showing the following data:
+    # Panda   5
+    # Tiger   8
+    # Lion    3
+    # Monkey  6
+    # Dolpin  2]
+    # Chart saved to ...
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_agent_with_code_interpreter_streaming.py

@@ -0,0 +1,87 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from pathlib import Path
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+from semantic_kernel.contents.binary_content import BinaryContent
+
+"""
+This sample shows how to interact with a Bedrock agent that is capable of writing and executing code.
+This sample uses the following main component(s):
+- a Bedrock agent
+You will learn how to create a new Bedrock agent and ask it a question that requires coding to answer.
+After running this sample, a bar chart will be generated and saved to a file in the same directory
+as this script.
+"""
+
+AGENT_NAME = "semantic-kernel-bedrock-agent"
+INSTRUCTION = "You are a friendly assistant. You help people find information."
+
+
+ASK = """
+Create a bar chart for the following data:
+Panda   5
+Tiger   8
+Lion    3
+Monkey  6
+Dolphin  2
+"""
+
+
+async def main():
+    bedrock_agent = await BedrockAgent.create_and_prepare_agent(AGENT_NAME, instructions=INSTRUCTION)
+    await bedrock_agent.create_code_interpreter_action_group()
+
+    thread: BedrockAgentThread = None
+
+    # Placeholder for the file generated by the code interpreter
+    binary_item: BinaryContent | None = None
+
+    try:
+        # Invoke the agent
+        print("Response: ")
+        async for response in bedrock_agent.invoke_stream(
+            messages=ASK,
+            thread=thread,
+        ):
+            print(response, end="")
+            thread = response.thread
+            if not binary_item:
+                binary_item = next((item for item in response.items if isinstance(item, BinaryContent)), None)
+        print()
+    finally:
+        # Delete the agent
+        await bedrock_agent.delete_agent()
+        await thread.delete() if thread else None
+
+    # Save the chart to a file
+    if not binary_item:
+        raise RuntimeError("No chart generated")
+
+    # Securely assemble the file path and validate it's within the expected directory
+    # This is a defense-in-depth measure against directory traversal attacks
+    output_dir = Path(__file__).parent.resolve()
+    file_path = (output_dir / binary_item.metadata["name"]).resolve()
+
+    # Verify the resolved path is within the expected directory
+    if not file_path.is_relative_to(output_dir):
+        raise RuntimeError("Invalid filename: would write outside the expected directory")
+
+    binary_item.write_to_file(file_path)
+    print(f"Chart saved to {file_path}")
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # Response:
+    # Here is the bar chart for the given data:
+    # [A bar chart showing the following data:
+    # Panda   5
+    # Tiger   8
+    # Lion    3
+    # Monkey  6
+    # Dolpin  2]
+    # Chart saved to ...
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_agent_with_kernel_function.py

@@ -0,0 +1,72 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from typing import Annotated
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
+from semantic_kernel.kernel import Kernel
+
+"""
+This sample shows how to interact with a Bedrock agent that is capable of using kernel functions.
+This sample uses the following main component(s):
+- a Bedrock agent
+- a kernel function
+- a kernel
+You will learn how to create a new Bedrock agent and ask it a question that requires a kernel function to answer.
+"""
+
+AGENT_NAME = "semantic-kernel-bedrock-agent"
+INSTRUCTION = "You are a friendly assistant. You help people find information."
+
+
+class WeatherPlugin:
+    """Mock weather plugin."""
+
+    @kernel_function(description="Get real-time weather information.")
+    def current(self, location: Annotated[str, "The location to get the weather"]) -> str:
+        """Returns the current weather."""
+        return f"The weather in {location} is sunny."
+
+
+def get_kernel() -> Kernel:
+    kernel = Kernel()
+    kernel.add_plugin(WeatherPlugin(), plugin_name="weather")
+
+    return kernel
+
+
+async def main():
+    # Create a kernel
+    kernel = get_kernel()
+
+    bedrock_agent = await BedrockAgent.create_and_prepare_agent(
+        AGENT_NAME,
+        INSTRUCTION,
+        kernel=kernel,
+    )
+    # Note: We still need to create the kernel function action group on the service side.
+    await bedrock_agent.create_kernel_function_action_group()
+
+    thread: BedrockAgentThread = None
+
+    try:
+        # Invoke the agent
+        async for response in bedrock_agent.invoke(
+            messages="What is the weather in Seattle?",
+            thread=thread,
+        ):
+            print(f"Response:\n{response}")
+            thread = response.thread
+    finally:
+        # Delete the agent
+        await bedrock_agent.delete_agent()
+        await thread.delete() if thread else None
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # Response:
+    # The current weather in Seattle is sunny.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_agent_with_kernel_function_simple.py

@@ -0,0 +1,63 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from typing import Annotated
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
+
+"""
+This sample shows how to interact with a Bedrock agent that is capable of using kernel functions.
+Instead of creating a kernel and adding plugins to it, you can directly pass the plugins to the
+agent when creating it.
+This sample uses the following main component(s):
+- a Bedrock agent
+- a kernel function
+- a kernel
+You will learn how to create a new Bedrock agent and ask it a question that requires a kernel function to answer.
+"""
+
+AGENT_NAME = "semantic-kernel-bedrock-agent"
+INSTRUCTION = "You are a friendly assistant. You help people find information."
+
+
+class WeatherPlugin:
+    """Mock weather plugin."""
+
+    @kernel_function(description="Get real-time weather information.")
+    def current(self, location: Annotated[str, "The location to get the weather"]) -> str:
+        """Returns the current weather."""
+        return f"The weather in {location} is sunny."
+
+
+async def main():
+    bedrock_agent = await BedrockAgent.create_and_prepare_agent(
+        AGENT_NAME,
+        INSTRUCTION,
+        plugins=[WeatherPlugin()],
+    )
+    # Note: We still need to create the kernel function action group on the service side.
+    await bedrock_agent.create_kernel_function_action_group()
+
+    thread: BedrockAgentThread = None
+
+    try:
+        # Invoke the agent
+        async for response in bedrock_agent.invoke(
+            messages="What is the weather in Seattle?",
+            thread=thread,
+        ):
+            print(f"Response:\n{response}")
+            thread = response.thread
+    finally:
+        # Delete the agent
+        await bedrock_agent.delete_agent()
+        await thread.delete() if thread else None
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # Response:
+    # The current weather in Seattle is sunny.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_agent_with_kernel_function_streaming.py

@@ -0,0 +1,73 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from typing import Annotated
+
+from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
+from semantic_kernel.kernel import Kernel
+
+"""
+This sample shows how to interact with a Bedrock agent that is capable of using kernel functions.
+This sample uses the following main component(s):
+- a Bedrock agent
+- a kernel function
+- a kernel
+You will learn how to create a new Bedrock agent and ask it a question that requires a kernel function to answer.
+"""
+
+AGENT_NAME = "semantic-kernel-bedrock-agent"
+INSTRUCTION = "You are a friendly assistant. You help people find information."
+
+
+class WeatherPlugin:
+    """Mock weather plugin."""
+
+    @kernel_function(description="Get real-time weather information.")
+    def current(self, location: Annotated[str, "The location to get the weather"]) -> str:
+        """Returns the current weather."""
+        return f"The weather in {location} is sunny."
+
+
+def get_kernel() -> Kernel:
+    kernel = Kernel()
+    kernel.add_plugin(WeatherPlugin(), plugin_name="weather")
+
+    return kernel
+
+
+async def main():
+    # Create a kernel
+    kernel = get_kernel()
+
+    bedrock_agent = await BedrockAgent.create_and_prepare_agent(
+        AGENT_NAME,
+        INSTRUCTION,
+        kernel=kernel,
+    )
+    # Note: We still need to create the kernel function action group on the service side.
+    await bedrock_agent.create_kernel_function_action_group()
+
+    thread: BedrockAgentThread = None
+
+    try:
+        # Invoke the agent
+        print("Response: ")
+        async for response in bedrock_agent.invoke_stream(
+            messages="What is the weather in Seattle?",
+            thread=thread,
+        ):
+            print(response, end="")
+            thread = response.thread
+    finally:
+        # Delete the agent
+        await bedrock_agent.delete_agent()
+        await thread.delete() if thread else None
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # Response:
+    # The current weather in Seattle is sunny.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_mixed_chat_agents.py

@@ -0,0 +1,113 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from azure.identity import AzureCliCredential
+
+from semantic_kernel.agents import AgentGroupChat, BedrockAgent, ChatCompletionAgent
+from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.kernel import Kernel
+
+"""
+This sample shows how to use a bedrock agent in a group chat that includes multiple agents of different roles.
+This sample uses the following main component(s):
+- a Bedrock agent
+- a ChatCompletionAgent
+- an AgentGroupChat
+You will learn how to create a new or connect to an existing Bedrock agent and put it in a group chat with
+another agent.
+
+Note: This sample use the `AgentGroupChat` feature of Semantic Kernel, which is
+no longer maintained. For a replacement, consider using the `GroupChatOrchestration`.
+
+Read more about the `GroupChatOrchestration` here:
+https://learn.microsoft.com/semantic-kernel/frameworks/agent/agent-orchestration/group-chat?pivots=programming-language-python
+
+Here is a migration guide from `AgentGroupChat` to `GroupChatOrchestration`:
+https://learn.microsoft.com/semantic-kernel/support/migration/group-chat-orchestration-migration-guide?pivots=programming-language-python
+"""
+
+# This will be a chat completion agent
+REVIEWER_NAME = "ArtDirector"
+REVIEWER_INSTRUCTIONS = """
+You are an art director who has opinions about copywriting born of a love for David Ogilvy.
+The goal is to determine if the given copy is acceptable to print.
+If so, state that it is approved. Only include the word "approved" if it is so.
+If not, provide insight on how to refine suggested copy without example.
+"""
+
+# This will be a bedrock agent
+COPYWRITER_NAME = "CopyWriter"
+COPYWRITER_INSTRUCTIONS = """
+You are a copywriter with ten years of experience and are known for brevity and a dry humor.
+The goal is to refine and decide on the single best copy as an expert in the field.
+Only provide a single proposal per response.
+You're laser focused on the goal at hand.
+Don't waste time with chit chat.
+Consider suggestions when refining an idea.
+"""
+
+
+class ApprovalTerminationStrategy(TerminationStrategy):
+    """A strategy for determining when an agent should terminate."""
+
+    async def should_agent_terminate(self, agent, history):
+        """Check if the agent should terminate."""
+        return "approved" in history[-1].content.lower()
+
+
+def _create_kernel_with_chat_completion() -> Kernel:
+    kernel = Kernel()
+    kernel.add_service(AzureChatCompletion(credential=AzureCliCredential()))
+    return kernel
+
+
+async def main():
+    agent_reviewer = ChatCompletionAgent(
+        kernel=_create_kernel_with_chat_completion(),
+        name=REVIEWER_NAME,
+        instructions=REVIEWER_INSTRUCTIONS,
+    )
+
+    agent_writer = await BedrockAgent.create_and_prepare_agent(
+        COPYWRITER_NAME,
+        instructions=COPYWRITER_INSTRUCTIONS,
+    )
+
+    chat = AgentGroupChat(
+        agents=[agent_writer, agent_reviewer],
+        termination_strategy=ApprovalTerminationStrategy(
+            agents=[agent_reviewer],
+            maximum_iterations=10,
+        ),
+    )
+
+    input = "A slogan for a new line of electric cars."
+
+    await chat.add_chat_message(message=input)
+    print(f"# {AuthorRole.USER}: '{input}'")
+
+    try:
+        async for message in chat.invoke():
+            print(f"# {message.role} - {message.name or '*'}: '{message.content}'")
+        print(f"# IS COMPLETE: {chat.is_complete}")
+    finally:
+        # Delete the agent
+        await agent_writer.delete_agent()
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # AuthorRole.USER: 'A slogan for a new line of electric cars.'
+    # AuthorRole.ASSISTANT - CopyWriter: 'Charge Ahead: The Future of Driving'
+    # AuthorRole.ASSISTANT - ArtDirector: 'The slogan "Charge Ahead: The Future of Driving" is compelling but could be
+    # made even more impactful. Consider clarifying the unique selling proposition of the electric cars. Focus on what
+    # sets them apart in terms of performance, eco-friendliness, or innovation. This will help create an emotional
+    # connection and a clearer message for the audience.'
+    # AuthorRole.ASSISTANT - CopyWriter: 'Charge Forward: The Electrifying Future of Driving'
+    # AuthorRole.ASSISTANT - ArtDirector: 'Approved'
+    # IS COMPLETE: True
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/agents/bedrock_agent/bedrock_mixed_chat_agents_streaming.py

@@ -0,0 +1,118 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from azure.identity import AzureCliCredential
+
+from semantic_kernel.agents import AgentGroupChat, BedrockAgent, ChatCompletionAgent
+from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.kernel import Kernel
+
+"""
+This sample shows how to use a bedrock agent in a group chat that includes multiple agents of different roles.
+This sample uses the following main component(s):
+- a Bedrock agent
+- a ChatCompletionAgent
+- an AgentGroupChat
+You will learn how to create a new or connect to an existing Bedrock agent and put it in a group chat with
+another agent.
+
+Note: This sample use the `AgentGroupChat` feature of Semantic Kernel, which is
+no longer maintained. For a replacement, consider using the `GroupChatOrchestration`.
+
+Read more about the `GroupChatOrchestration` here:
+https://learn.microsoft.com/semantic-kernel/frameworks/agent/agent-orchestration/group-chat?pivots=programming-language-python
+
+Here is a migration guide from `AgentGroupChat` to `GroupChatOrchestration`:
+https://learn.microsoft.com/semantic-kernel/support/migration/group-chat-orchestration-migration-guide?pivots=programming-language-python
+"""
+
+# This will be a chat completion agent
+REVIEWER_NAME = "ArtDirector"
+REVIEWER_INSTRUCTIONS = """
+You are an art director who has opinions about copywriting born of a love for David Ogilvy.
+The goal is to determine if the given copy is acceptable to print.
+If so, state that it is approved. Only include the word "approved" if it is so.
+If not, provide insight on how to refine suggested copy without example.
+"""
+
+# This will be a bedrock agent
+COPYWRITER_NAME = "CopyWriter"
+COPYWRITER_INSTRUCTIONS = """
+You are a copywriter with ten years of experience and are known for brevity and a dry humor.
+The goal is to refine and decide on the single best copy as an expert in the field.
+Only provide a single proposal per response.
+You're laser focused on the goal at hand.
+Don't waste time with chit chat.
+Consider suggestions when refining an idea.
+"""
+
+
+class ApprovalTerminationStrategy(TerminationStrategy):
+    """A strategy for determining when an agent should terminate."""
+
+    async def should_agent_terminate(self, agent, history):
+        """Check if the agent should terminate."""
+        return "approved" in history[-1].content.lower()
+
+
+def _create_kernel_with_chat_completion() -> Kernel:
+    kernel = Kernel()
+    kernel.add_service(AzureChatCompletion(credential=AzureCliCredential()))
+    return kernel
+
+
+async def main():
+    agent_reviewer = ChatCompletionAgent(
+        kernel=_create_kernel_with_chat_completion(),
+        name=REVIEWER_NAME,
+        instructions=REVIEWER_INSTRUCTIONS,
+    )
+
+    agent_writer = await BedrockAgent.create_and_prepare_agent(
+        COPYWRITER_NAME,
+        instructions=COPYWRITER_INSTRUCTIONS,
+    )
+
+    chat = AgentGroupChat(
+        agents=[agent_writer, agent_reviewer],
+        termination_strategy=ApprovalTerminationStrategy(
+            agents=[agent_reviewer],
+            maximum_iterations=10,
+        ),
+    )
+
+    input = "A slogan for a new line of electric cars."
+
+    await chat.add_chat_message(message=input)
+    print(f"# {AuthorRole.USER}: '{input}'")
+
+    try:
+        current_agent = "*"
+        async for message_chunk in chat.invoke_stream():
+            if current_agent != message_chunk.name:
+                current_agent = message_chunk.name or "*"
+                print(f"\n# {message_chunk.role} - {current_agent}: ", end="")
+            print(message_chunk.content, end="")
+        print()
+        print(f"# IS COMPLETE: {chat.is_complete}")
+    finally:
+        # Delete the agent
+        await agent_writer.delete_agent()
+
+    # Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
+    # AuthorRole.USER: 'A slogan for a new line of electric cars.'
+    # AuthorRole.ASSISTANT - CopyWriter: 'Charge Ahead: The Future of Driving'
+    # AuthorRole.ASSISTANT - ArtDirector: 'The slogan "Charge Ahead: The Future of Driving" is compelling but could be
+    # made even more impactful. Consider clarifying the unique selling proposition of the electric cars. Focus on what
+    # sets them apart in terms of performance, eco-friendliness, or innovation. This will help create an emotional
+    # connection and a clearer message for the audience.'
+    # AuthorRole.ASSISTANT - CopyWriter: 'Charge Forward: The Electrifying Future of Driving'
+    # AuthorRole.ASSISTANT - ArtDirector: 'Approved'
+    # IS COMPLETE: True
+
+
+if __name__ == "__main__":
+    asyncio.run(main())