Source code for langchain.agents.openai_tools.base

from typing import Sequence

from langchain_core.language_models import BaseLanguageModel
from import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from import BaseTool
from langchain_core.utils.function_calling import convert_to_openai_tool

from langchain.agents.format_scratchpad.openai_tools import (
from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser

[docs]def create_openai_tools_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate ) -> Runnable: """Create an agent that uses OpenAI tools. Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more on the expected input variables. 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: .. code-block:: python from langchain import hub from langchain_community.chat_models import ChatOpenAI from langchain.agents import AgentExecutor, create_openai_tools_agent prompt = hub.pull("hwchase17/openai-tools-agent") model = ChatOpenAI() tools = ... agent = create_openai_tools_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: .. code-block:: 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"), ] ) """ missing_vars = {"agent_scratchpad"}.difference( prompt.input_variables + list(prompt.partial_variables) ) if missing_vars: raise ValueError(f"Prompt missing required variables: {missing_vars}") llm_with_tools = llm.bind(tools=[convert_to_openai_tool(tool) for tool in tools]) agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_to_openai_tool_messages( x["intermediate_steps"] ) ) | prompt | llm_with_tools | OpenAIToolsAgentOutputParser() ) return agent