---
title: Agents
id: agents-api
description: Tool-using agents with provider-agnostic chat model support.
---
# 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
```python
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\_\_
```python
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
```python
def warm_up() -> None
```
Warm up the Agent.
#### Agent.to\_dict
```python
def to_dict() -> dict[str, Any]
```
Serialize the component to a dictionary.
**Returns**:
Dictionary with serialized data
#### 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
#### Agent.run
```python
def run(messages: list[ChatMessage],
streaming_callback: Optional[StreamingCallbackT] = None,
*,
break_point: Optional[AgentBreakpoint] = None,
snapshot: Optional[AgentSnapshot] = None,
system_prompt: Optional[str] = None,
tools: Optional[Union[list[Tool], Toolset, 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.
- `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`.
#### Agent.run\_async
```python
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,
tools: Optional[Union[list[Tool], Toolset,
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.
- `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`.
# 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:
```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"}
)
```
#### 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
#### 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
#### 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
#### State.data
```python
@property
def data()
```
All current data of the state.
#### 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
#### State.to\_dict
```python
def to_dict()
```
Convert the State object to a dictionary.
#### State.from\_dict
```python
@classmethod
def from_dict(cls, data: dict[str, Any])
```
Convert a dictionary back to a State object.