create_openai_functions_agent() — langchain Function Reference

Source Code

libs/langchain/langchain_classic/agents/openai_functions_agent/base.py lines 287–382

def create_openai_functions_agent(
    llm: BaseLanguageModel,
    tools: Sequence[BaseTool],
    prompt: ChatPromptTemplate,
) -> Runnable:
    """Create an agent that uses OpenAI function calling.

    Args:
        llm: LLM to use as the agent. Should work with OpenAI function calling,
            so either be an OpenAI model that supports that or a wrapper of
            a different model that adds in equivalent support.
        tools: Tools this agent has access to.
        prompt: The prompt to use. See Prompt section below for more.

    Returns:
        A Runnable sequence representing an agent. It takes as input all the same input
            variables as the prompt passed in does. It returns as output either an
            AgentAction or AgentFinish.

    Raises:
        ValueError: If `agent_scratchpad` is not in the prompt.

    Example:
        Creating an agent with no memory

        ```python
        from langchain_openai import ChatOpenAI
        from langchain_classic.agents import (
            AgentExecutor,
            create_openai_functions_agent,
        )
        from langchain_classic import hub

        prompt = hub.pull("hwchase17/openai-functions-agent")
        model = ChatOpenAI()
        tools = ...

        agent = create_openai_functions_agent(model, tools, prompt)
        agent_executor = AgentExecutor(agent=agent, tools=tools)

        agent_executor.invoke({"input": "hi"})

        # Using with chat history
        from langchain_core.messages import AIMessage, HumanMessage

        agent_executor.invoke(
            {
                "input": "what's my name?",
                "chat_history": [
                    HumanMessage(content="hi! my name is bob"),
                    AIMessage(content="Hello Bob! How can I assist you today?"),
                ],
            }
        )
        ```

    Prompt:

        The agent prompt must have an `agent_scratchpad` key that is a
            `MessagesPlaceholder`. Intermediate agent actions and tool output
            messages will be passed in here.

        Here's an example:

        ```python
        from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

        prompt = ChatPromptTemplate.from_messages(
            [
                ("system", "You are a helpful assistant"),
                MessagesPlaceholder("chat_history", optional=True),
                ("human", "{input}"),
                MessagesPlaceholder("agent_scratchpad"),
            ]
        )
        ```
    """
    if "agent_scratchpad" not in (
        prompt.input_variables + list(prompt.partial_variables)
    ):
        msg = (