langchain.agents.openai_functions_agent.base.create_openai_functions_agent

langchain.agents.openai_functions_agent.base.create_openai_functions_agent(llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate) → Runnable

Create an agent that uses OpenAI function calling.

Parameters

• llm (BaseLanguageModel) – 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 (Sequence[BaseTool]) – Tools this agent has access to.

• prompt (ChatPromptTemplate) – 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.

Return type

Runnable

Example

Creating an agent with no memory

from langchain_community.chat_models import ChatOpenAI
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain 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:

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"),
    ]
)