chore: import upstream snapshot with attribution

docs-website/reference_versioned_docs/version-2.21/haystack-api/agents_api.md

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+---
+title: "Agents"
+id: agents-api
+description: "Tool-using agents with provider-agnostic chat model support."
+slug: "/agents-api"
+---
+
+<a id="agent"></a>
+
+## Module agent
+
+<a id="agent.Agent"></a>
+
+### Agent
+
+A Haystack component that implements a tool-using agent with provider-agnostic chat model support.
+
+The component processes messages and executes tools until an exit condition is met.
+The exit condition can be triggered either by a direct text response or by invoking a specific designated tool.
+Multiple exit conditions can be specified.
+
+When you call an Agent without tools, it acts as a ChatGenerator, produces one response, then exits.
+
+### Usage example
+```python
+from haystack.components.agents import Agent
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import Tool
+
+# Tool functions - in practice, these would have real implementations
+def search(query: str) -> str:
+    '''Search for information on the web.'''
+    # Placeholder: would call actual search API
+    return "In France, a 15% service charge is typically included, but leaving 5-10% extra is appreciated."
+
+def calculator(operation: str, a: float, b: float) -> float:
+    '''Perform mathematical calculations.'''
+    if operation == "multiply":
+        return a * b
+    elif operation == "percentage":
+        return (a / 100) * b
+    return 0
+
+# Define tools with JSON Schema
+tools = [
+    Tool(
+        name="search",
+        description="Searches for information on the web",
+        parameters={
+            "type": "object",
+            "properties": {
+                "query": {"type": "string", "description": "The search query"}
+            },
+            "required": ["query"]
+        },
+        function=search
+    ),
+    Tool(
+        name="calculator",
+        description="Performs mathematical calculations",
+        parameters={
+            "type": "object",
+            "properties": {
+                "operation": {"type": "string", "description": "Operation: multiply, percentage"},
+                "a": {"type": "number", "description": "First number"},
+                "b": {"type": "number", "description": "Second number"}
+            },
+            "required": ["operation", "a", "b"]
+        },
+        function=calculator
+    )
+]
+
+# Create and run the agent
+agent = Agent(
+    chat_generator=OpenAIChatGenerator(),
+    tools=tools
+)
+
+result = agent.run(
+    messages=[ChatMessage.from_user("Calculate the appropriate tip for an €85 meal in France")]
+)
+
+# The agent will:
+# 1. Search for tipping customs in France
+# 2. Use calculator to compute tip based on findings
+# 3. Return the final answer with context
+print(result["messages"][-1].text)
+```
+
+<a id="agent.Agent.__init__"></a>
+
+#### Agent.\_\_init\_\_
+
+```python
+def __init__(*,
+             chat_generator: ChatGenerator,
+             tools: Optional[ToolsType] = None,
+             system_prompt: Optional[str] = None,
+             exit_conditions: Optional[list[str]] = None,
+             state_schema: Optional[dict[str, Any]] = None,
+             max_agent_steps: int = 100,
+             streaming_callback: Optional[StreamingCallbackT] = None,
+             raise_on_tool_invocation_failure: bool = False,
+             tool_invoker_kwargs: Optional[dict[str, Any]] = None) -> None
+```
+
+Initialize the agent component.
+
+**Arguments**:
+
+- `chat_generator`: An instance of the chat generator that your agent should use. It must support tools.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset that the agent can use.
+- `system_prompt`: System prompt for the agent.
+- `exit_conditions`: List of conditions that will cause the agent to return.
+Can include "text" if the agent should return when it generates a message without tool calls,
+or tool names that will cause the agent to return once the tool was executed. Defaults to ["text"].
+- `state_schema`: The schema for the runtime state used by the tools.
+- `max_agent_steps`: Maximum number of steps the agent will run before stopping. Defaults to 100.
+If the agent exceeds this number of steps, it will stop and return the current state.
+- `streaming_callback`: A callback that will be invoked when a response is streamed from the LLM.
+The same callback can be configured to emit tool results when a tool is called.
+- `raise_on_tool_invocation_failure`: Should the agent raise an exception when a tool invocation fails?
+If set to False, the exception will be turned into a chat message and passed to the LLM.
+- `tool_invoker_kwargs`: Additional keyword arguments to pass to the ToolInvoker.
+
+**Raises**:
+
+- `TypeError`: If the chat_generator does not support tools parameter in its run method.
+- `ValueError`: If the exit_conditions are not valid.
+
+<a id="agent.Agent.warm_up"></a>
+
+#### Agent.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Warm up the Agent.
+
+<a id="agent.Agent.to_dict"></a>
+
+#### Agent.to\_dict
+
+```python
+def to_dict() -> dict[str, Any]
+```
+
+Serialize the component to a dictionary.
+
+**Returns**:
+
+Dictionary with serialized data
+
+<a id="agent.Agent.from_dict"></a>
+
+#### Agent.from\_dict
+
+```python
+@classmethod
+def from_dict(cls, data: dict[str, Any]) -> "Agent"
+```
+
+Deserialize the agent from a dictionary.
+
+**Arguments**:
+
+- `data`: Dictionary to deserialize from
+
+**Returns**:
+
+Deserialized agent
+
+<a id="agent.Agent.run"></a>
+
+#### Agent.run
+
+```python
+def run(messages: list[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        *,
+        generation_kwargs: Optional[dict[str, Any]] = None,
+        break_point: Optional[AgentBreakpoint] = None,
+        snapshot: Optional[AgentSnapshot] = None,
+        system_prompt: Optional[str] = None,
+        tools: Optional[Union[ToolsType, list[str]]] = None,
+        **kwargs: Any) -> dict[str, Any]
+```
+
+Process messages and execute tools until an exit condition is met.
+
+**Arguments**:
+
+- `messages`: List of Haystack ChatMessage objects to process.
+- `streaming_callback`: A callback that will be invoked when a response is streamed from the LLM.
+The same callback can be configured to emit tool results when a tool is called.
+- `generation_kwargs`: Additional keyword arguments for LLM. These parameters will
+override the parameters passed during component initialization.
+- `break_point`: An AgentBreakpoint, can be a Breakpoint for the "chat_generator" or a ToolBreakpoint
+for "tool_invoker".
+- `snapshot`: A dictionary containing a snapshot of a previously saved agent execution. The snapshot contains
+the relevant information to restart the Agent execution from where it left off.
+- `system_prompt`: System prompt for the agent. If provided, it overrides the default system prompt.
+- `tools`: Optional list of Tool objects, a Toolset, or list of tool names to use for this run.
+When passing tool names, tools are selected from the Agent's originally configured tools.
+- `kwargs`: Additional data to pass to the State schema used by the Agent.
+The keys must match the schema defined in the Agent's `state_schema`.
+
+**Raises**:
+
+- `RuntimeError`: If the Agent component wasn't warmed up before calling `run()`.
+- `BreakpointException`: If an agent breakpoint is triggered.
+
+**Returns**:
+
+A dictionary with the following keys:
+- "messages": List of all messages exchanged during the agent's run.
+- "last_message": The last message exchanged during the agent's run.
+- Any additional keys defined in the `state_schema`.
+
+<a id="agent.Agent.run_async"></a>
+
+#### Agent.run\_async
+
+```python
+async def run_async(messages: list[ChatMessage],
+                    streaming_callback: Optional[StreamingCallbackT] = None,
+                    *,
+                    generation_kwargs: Optional[dict[str, Any]] = None,
+                    break_point: Optional[AgentBreakpoint] = None,
+                    snapshot: Optional[AgentSnapshot] = None,
+                    system_prompt: Optional[str] = None,
+                    tools: Optional[Union[ToolsType, list[str]]] = None,
+                    **kwargs: Any) -> dict[str, Any]
+```
+
+Asynchronously process messages and execute tools until the exit condition is met.
+
+This is the asynchronous version of the `run` method. It follows the same logic but uses
+asynchronous operations where possible, such as calling the `run_async` method of the ChatGenerator
+if available.
+
+**Arguments**:
+
+- `messages`: List of Haystack ChatMessage objects to process.
+- `streaming_callback`: An asynchronous callback that will be invoked when a response is streamed from the
+LLM. The same callback can be configured to emit tool results when a tool is called.
+- `generation_kwargs`: Additional keyword arguments for LLM. These parameters will
+override the parameters passed during component initialization.
+- `break_point`: An AgentBreakpoint, can be a Breakpoint for the "chat_generator" or a ToolBreakpoint
+for "tool_invoker".
+- `snapshot`: A dictionary containing a snapshot of a previously saved agent execution. The snapshot contains
+the relevant information to restart the Agent execution from where it left off.
+- `system_prompt`: System prompt for the agent. If provided, it overrides the default system prompt.
+- `tools`: Optional list of Tool objects, a Toolset, or list of tool names to use for this run.
+- `kwargs`: Additional data to pass to the State schema used by the Agent.
+The keys must match the schema defined in the Agent's `state_schema`.
+
+**Raises**:
+
+- `RuntimeError`: If the Agent component wasn't warmed up before calling `run_async()`.
+- `BreakpointException`: If an agent breakpoint is triggered.
+
+**Returns**:
+
+A dictionary with the following keys:
+- "messages": List of all messages exchanged during the agent's run.
+- "last_message": The last message exchanged during the agent's run.
+- Any additional keys defined in the `state_schema`.
+
+<a id="state/state"></a>
+
+## Module state/state
+
+<a id="state/state.State"></a>
+
+### State
+
+State is a container for storing shared information during the execution of an Agent and its tools.
+
+For instance, State can be used to store documents, context, and intermediate results.
+
+Internally it wraps a `_data` dictionary defined by a `schema`. Each schema entry has:
+```json
+  "parameter_name": {
+    "type": SomeType,  # expected type
+    "handler": Optional[Callable[[Any, Any], Any]]  # merge/update function
+  }
+  ```
+
+Handlers control how values are merged when using the `set()` method:
+- For list types: defaults to `merge_lists` (concatenates lists)
+- For other types: defaults to `replace_values` (overwrites existing value)
+
+A `messages` field with type `list[ChatMessage]` is automatically added to the schema.
+
+This makes it possible for the Agent to read from and write to the same context.
+
+### Usage example
+```python
+from haystack.components.agents.state import State
+
+my_state = State(
+    schema={"gh_repo_name": {"type": str}, "user_name": {"type": str}},
+    data={"gh_repo_name": "my_repo", "user_name": "my_user_name"}
+)
+```
+
+<a id="state/state.State.__init__"></a>
+
+#### State.\_\_init\_\_
+
+```python
+def __init__(schema: dict[str, Any], data: Optional[dict[str, Any]] = None)
+```
+
+Initialize a State object with a schema and optional data.
+
+**Arguments**:
+
+- `schema`: Dictionary mapping parameter names to their type and handler configs.
+Type must be a valid Python type, and handler must be a callable function or None.
+If handler is None, the default handler for the type will be used. The default handlers are:
+    - For list types: `haystack.agents.state.state_utils.merge_lists`
+    - For all other types: `haystack.agents.state.state_utils.replace_values`
+- `data`: Optional dictionary of initial data to populate the state
+
+<a id="state/state.State.get"></a>
+
+#### State.get
+
+```python
+def get(key: str, default: Any = None) -> Any
+```
+
+Retrieve a value from the state by key.
+
+**Arguments**:
+
+- `key`: Key to look up in the state
+- `default`: Value to return if key is not found
+
+**Returns**:
+
+Value associated with key or default if not found
+
+<a id="state/state.State.set"></a>
+
+#### State.set
+
+```python
+def set(key: str,
+        value: Any,
+        handler_override: Optional[Callable[[Any, Any], Any]] = None) -> None
+```
+
+Set or merge a value in the state according to schema rules.
+
+Value is merged or overwritten according to these rules:
+  - if handler_override is given, use that
+  - else use the handler defined in the schema for 'key'
+
+**Arguments**:
+
+- `key`: Key to store the value under
+- `value`: Value to store or merge
+- `handler_override`: Optional function to override the default merge behavior
+
+<a id="state/state.State.data"></a>
+
+#### State.data
+
+```python
+@property
+def data()
+```
+
+All current data of the state.
+
+<a id="state/state.State.has"></a>
+
+#### State.has
+
+```python
+def has(key: str) -> bool
+```
+
+Check if a key exists in the state.
+
+**Arguments**:
+
+- `key`: Key to check for existence
+
+**Returns**:
+
+True if key exists in state, False otherwise
+
+<a id="state/state.State.to_dict"></a>
+
+#### State.to\_dict
+
+```python
+def to_dict()
+```
+
+Convert the State object to a dictionary.
+
+<a id="state/state.State.from_dict"></a>
+
+#### State.from\_dict
+
+```python
+@classmethod
+def from_dict(cls, data: dict[str, Any])
+```
+
+Convert a dictionary back to a State object.
+