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 [source]¶
Create an agent that uses OpenAI function calling.
- Parameters
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.
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"), ] )