Source code for langchain.agents.tool_calling_agent.base

from typing import Sequence

from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool

from langchain.agents.format_scratchpad.tools import (
    format_to_tool_messages,
)
from langchain.agents.output_parsers.tools import ToolsAgentOutputParser


[docs]def create_tool_calling_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate ) -> Runnable: """Create an agent that uses 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.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. """ missing_vars = {"agent_scratchpad"}.difference( prompt.input_variables + list(prompt.partial_variables) ) if missing_vars: raise ValueError(f"Prompt missing required variables: {missing_vars}") if not hasattr(llm, "bind_tools"): raise ValueError( "This function requires a .bind_tools method be implemented on the LLM.", ) llm_with_tools = llm.bind_tools(tools) agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_to_tool_messages(x["intermediate_steps"]) ) | prompt | llm_with_tools | ToolsAgentOutputParser() ) return agent