create_tool_calling_agent

Example:

from langchain_classic.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-opus-4-1-20250805")

@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.

Troubleshooting:

• If you encounter invalid_tool_calls errors, ensure that your tool functions return properly formatted responses. Tool outputs should be serializable to JSON. For custom objects, implement proper str or to_dict methods.