Agent#
- class langchain.agents.agent.Agent[source]#
Bases:
BaseSingleActionAgent
Deprecated since version 0.1.0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc.
Agent that calls the language model and deciding the action.
This is driven by a LLMChain. The prompt in the LLMChain MUST include a variable called βagent_scratchpadβ 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: List[str] | None = None#
Allowed tools for the agent. If None, all tools are allowed.
- param output_parser: AgentOutputParser [Required]#
Output parser to use for agent.
- 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:
intermediate_steps (List[Tuple[AgentAction, str]]) β Steps the LLM has taken to date, along with observations.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to run.
**kwargs (Any) β User inputs.
- Returns:
Action specifying what tool to use.
- Return type:
- abstract classmethod create_prompt(tools: Sequence[BaseTool]) BasePromptTemplate [source]#
Create a prompt for this class.
- Parameters:
tools (Sequence[BaseTool]) β Tools to use.
- Returns:
Prompt template.
- Return type:
- classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: BaseCallbackManager | None = None, output_parser: AgentOutputParser | None = None, **kwargs: Any) Agent [source]#
Construct an agent from an LLM and tools.
- Parameters:
llm (BaseLanguageModel) β Language model to use.
tools (Sequence[BaseTool]) β Tools to use.
callback_manager (BaseCallbackManager | None) β Callback manager to use.
output_parser (AgentOutputParser | None) β Output parser to use.
kwargs (Any) β Additional arguments.
- Returns:
Agent object.
- Return type:
- get_full_inputs(intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) Dict[str, Any] [source]#
Create the full inputs for the LLMChain from intermediate steps.
- Parameters:
intermediate_steps (List[Tuple[AgentAction, str]]) β Steps the LLM has taken to date, along with observations.
**kwargs (Any) β User inputs.
- Returns:
Full inputs for the LLMChain.
- Return type:
Dict[str, Any]
- 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:
intermediate_steps (List[Tuple[AgentAction, str]]) β Steps the LLM has taken to date, along with observations.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to run.
**kwargs (Any) β User inputs.
- Returns:
Action specifying what tool to use.
- Return type:
- 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) β Method to use for early stopping.
intermediate_steps (List[Tuple[AgentAction, str]]) β Steps the LLM has taken to date, along with observations.
**kwargs (Any) β User inputs.
- Returns:
Agent finish object.
- Return type:
- Raises:
ValueError β If early_stopping_method is not in [βforceβ, βgenerateβ].
- save(file_path: Path | str) None #
Save the agent.
- Parameters:
file_path (Path | str) β 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=βpath/agent.yamlβ)
- 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.