langchain.agents.agent.Agent

class langchain.agents.agent.Agent(*, llm_chain: LLMChain, output_parser: AgentOutputParser, allowed_tools: Optional[List[str]] = None)

Bases: BaseSingleActionAgent

Agent that calls the language model and deciding the action.

This is driven by an LLMChain. The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work.

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 allowed_tools: Optional[List[str]] = None

param llm_chain: langchain.chains.llm.LLMChain [Required]

param output_parser: langchain.agents.agent.AgentOutputParser [Required]

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

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.

abstract classmethod create_prompt(tools: Sequence[BaseTool]) → BasePromptTemplate

Create a prompt for this class.

dict(**kwargs: Any) → Dict

Return dictionary representation of agent.

classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, **kwargs: Any) → Agent

Construct an agent from an LLM and tools.

get_allowed_tools() → Optional[List[str]]

get_full_inputs(intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) → Dict[str, Any]

Create the full inputs for the LLMChain from intermediate steps.

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

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.

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.

save(file_path: Union[Path, str]) → None

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

validator validate_prompt  »  all fields

Validate that prompt matches format.

abstract property llm_prefix: str

Prefix to append the LLM call with.

abstract property observation_prefix: str

Prefix to append the observation with.

property return_values: List[str]

Return values of the agent.