Module agent

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

from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools.tool import Tool

tools = [Tool(name="calculator", description="..."), Tool(name="search", description="...")]

agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=tools,
    exit_conditions=["search"],
)

# Run the agent
result = agent.run(
    messages=[ChatMessage.from_user("Find information about Haystack")]
)

assert "messages" in result  # Contains conversation history

Agent.__init__

def __init__(*,
             chat_generator: ChatGenerator,
             tools: Optional[Union[list[Tool], Toolset]] = 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: List of Tool objects or a 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.

Agent.warm_up

def warm_up() -> None

Warm up the Agent.

Agent.to_dict

def to_dict() -> dict[str, Any]

Serialize the component to a dictionary.

Returns:

Dictionary with serialized data

Agent.from_dict

@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

Agent.run

def run(messages: list[ChatMessage],
        streaming_callback: Optional[StreamingCallbackT] = None,
        *,
        break_point: Optional[AgentBreakpoint] = None,
        snapshot: Optional[AgentSnapshot] = None,
        system_prompt: Optional[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.
• 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.
• 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.

Agent.run_async

async def run_async(messages: list[ChatMessage],
                    streaming_callback: Optional[StreamingCallbackT] = None,
                    *,
                    break_point: Optional[AgentBreakpoint] = None,
                    snapshot: Optional[AgentSnapshot] = None,
                    system_prompt: Optional[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.
• 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.
• 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.

Module state/state

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:

  "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

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"}
)

State.__init__

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

State.get

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

State.set

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

State.data

@property
def data()

All current data of the state.

State.has

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

State.to_dict

def to_dict()

Convert the State object to a dictionary.

State.from_dict

@classmethod
def from_dict(cls, data: dict[str, Any])

Convert a dictionary back to a State object.