**kwargs – User inputs.
Returns
Actions specifying what tool to use.
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish[source]#
Return response when agent has been stopped due to max iterations.
save(file_path: Union[pathlib.Path, str]) → None[source]#
Save the agent.
Parameters
file_path – Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path=”path/agent.yaml”)
tool_run_logging_kwargs() → Dict[source]#
property return_values: List[str]#
Return values of the agent.
pydantic model langchain.agents.BaseSingleActionAgent[source]#
Base Agent class.
abstract async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
callbacks – Callbacks to run.
**kwargs – User inputs.
Returns
Action specifying what tool to use.
dict(**kwargs: Any) → Dict[source]#
Return dictionary representation of agent.
classmethod from_llm_and_tools(llm: langchain.base_language.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → langchain.agents.agent.BaseSingleActionAgent[source]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html