langchain.agents.initialize.initialize_agent

langchain.agents.initialize.initialize_agent(tools: Sequence[BaseTool], llm: BaseLanguageModel, agent: Optional[AgentType] = None, callback_manager: Optional[BaseCallbackManager] = None, agent_path: Optional[str] = None, agent_kwargs: Optional[dict] = None, *, tags: Optional[Sequence[str]] = None, **kwargs: Any) → AgentExecutor

[Deprecated] 加载给定工具和LLM的代理执行器。

参数：

tools：代理可以访问的工具列表。 llm：用作代理的语言模型。 agent：要使用的代理类型。如果为None且agent_path也为None，则默认为AgentType.ZERO_SHOT_REACT_DESCRIPTION。 callback_manager：要使用的CallbackManager。如果未提供，则使用全局回调管理器。默认为None。 agent_path：要使用的序列化代理的路径。 agent_kwargs：传递给底层代理的额外关键字参数。 tags：要应用于跟踪运行的标记。 **kwargs：传递给代理执行器的额外关键字参数。

返回：

一个代理执行器。

Notes

Deprecated since version 0.1.0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. instead.

Parameters

• tools (Sequence[BaseTool]) –

• llm (BaseLanguageModel) –

• agent (Optional[AgentType]) –

• callback_manager (Optional[BaseCallbackManager]) –

• agent_path (Optional[str]) –

• agent_kwargs (Optional[dict]) –

• tags (Optional[Sequence[str]]) –

• kwargs (Any) –

Return type

AgentExecutor

Examples using initialize_agent

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• get from https://platform.openai.com/

• google_finance.md

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• scenario 1 - LLM

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