langchain.agents.agent.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 if the input data cannot be parsed to form a valid model.

param input_keys_arg: List[str] = []
param return_keys_arg: List[str] = []
param runnable: Runnable[dict, Union[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: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[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

Union[List[AgentAction], AgentFinish]

get_allowed_tools() → Optional[List[str]]

Get allowed tools.

Returns

Allowed tools.

Return type

Optional[List[str]]

plan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[List[AgentAction], AgentFinish][source]

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

Parameters

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

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run.

  • **kwargs (Any) – User inputs.

Returns

Action specifying what tool to use.

Return type

Union[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: Union[Path, str]) → None

Save the agent.

Parameters

file_path (Union[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.