langchain.agents.tool_calling_agent.base
.create_tool_calling_agent¶
- langchain.agents.tool_calling_agent.base.create_tool_calling_agent(llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate) Runnable [source]¶
Create an agent that uses 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.
- Return type
Example
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool from langchain_anthropic import ChatAnthropic from langchain_core.prompts import ChatPromptTemplate prompt = ChatPromptTemplate.from_messages( [ ("system", "You are a helpful assistant"), ("placeholder", "{chat_history}"), ("human", "{input}"), ("placeholder", "{agent_scratchpad}"), ] ) model = ChatAnthropic(model="claude-3-opus-20240229") @tool def magic_function(input: int) -> int: """Applies a magic function to an input.""" return input + 2 tools = [magic_function] agent = create_tool_calling_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) agent_executor.invoke({"input": "what is the value of magic_function(3)?"}) # 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.