create_openai_tools_agent#

langchain.agents.openai_tools.base.create_openai_tools_agent(llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate) → Runnable[source]#

Create an agent that uses OpenAI tools.

Parameters:

llm (BaseLanguageModel) – LLM to use as the agent.
tools (Sequence[BaseTool]) – Tools this agent has access to.
prompt (ChatPromptTemplate) – 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.

Raises:

ValueError – If the prompt is missing required variables.

Return type:

Runnable

Example

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:

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"),
    ]
)

Examples using create_openai_tools_agent