RunnableMultiActionAgent#

class langchain.agents.agent.RunnableMultiActionAgent[source]#

Bases: BaseMultiActionAgent

Agent powered by Runnables.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param input_keys_arg: list[str] = []#
param return_keys_arg: list[str] = []#
param runnable: Runnable[dict, list[AgentAction] | AgentFinish] [Required]#

Runnable to call to get agent actions.

param stream_runnable: bool = True#

Whether to stream from the runnable or not.

If True then underlying LLM is invoked in a streaming fashion to make it possible

to get access to the individual LLM tokens when using stream_log with the Agent Executor. If False then LLM is invoked in a non-streaming fashion and individual LLM tokens will not be available in stream_log.

async aplan(
intermediate_steps: list[tuple[AgentAction, str]],
callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None,
**kwargs: Any,
) → list[AgentAction] | AgentFinish[source]#

Async based on past history and current inputs, decide what to do.

Parameters:
Returns:

Action specifying what tool to use.

Return type:

list[AgentAction] | AgentFinish

get_allowed_tools() → list[str] | None#

Get allowed tools.

Returns:

Allowed tools.

Return type:

Optional[List[str]]

plan(
intermediate_steps: list[tuple[AgentAction, str]],
callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None,
**kwargs: Any,
) → list[AgentAction] | AgentFinish[source]#

Based on past history and current inputs, decide what to do.

Parameters:
Returns:

Action specifying what tool to use.

Return type:

list[AgentAction] | AgentFinish

return_stopped_response(
early_stopping_method: str,
intermediate_steps: list[tuple[AgentAction, str]],
**kwargs: Any,
) → AgentFinish#

Return response when agent has been stopped due to max iterations.

Parameters:

  • early_stopping_method (str) – Method to use for early stopping.

  • intermediate_steps (list[tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations.

  • **kwargs (Any) – User inputs.

Returns:

Agent finish object.

Return type:

AgentFinish

Raises:

ValueError – If early_stopping_method is not supported.

save(
file_path: Path | str,
) → None#

Save the agent.

Parameters:

file_path (Path | str) – Path to file to save the agent to.

Raises:

  • NotImplementedError – If agent does not support saving.

  • ValueError – If file_path is not json or yaml.

Return type:

None

Example: .. code-block:: python

# If working with agent executor agent.agent.save(file_path=”path/agent.yaml”)

tool_run_logging_kwargs() → dict#

Return logging kwargs for tool run.

Return type:

dict

property input_keys: list[str]#

Return the input keys.

Returns:

List of input keys.

property return_values: list[str]#

Return values of the agent.