BaseSingleActionAgent#

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

Bases: BaseModel

Base Single Action Agent class.

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.

classmethod from_llm_and_tools(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: BaseCallbackManager | None = None,
**kwargs: Any,
) → BaseSingleActionAgent[source]#

Construct an agent from an LLM and tools.

Parameters:
Returns:

Agent object.

Return type:

BaseSingleActionAgent

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

Async given input, decided what to do.

Parameters:
Returns:

Action specifying what tool to use.

Return type:

AgentAction | AgentFinish

get_allowed_tools() → list[str] | None[source]#
Return type:

list[str] | None

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

Given input, decided what to do.

Parameters:
Returns:

Action specifying what tool to use.

Return type:

AgentAction | AgentFinish

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

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[source]#

Save the agent.

Parameters:

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

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[source]#

Return logging kwargs for tool run.

Return type:

dict

property return_values: list[str]#

Return values of the agent.