langchain-fundamentals

Build production LangChain agents with create_agent(), tools, and middleware patterns. create_agent() is the recommended way to build agents. It handles the agent loop, tool execution, and state management. @tool def get_weather(location: str) -> str: """Get current weather for a location. agent = create_agent( model="anthropic:claude-sonnet-4-5", tools=[get_weather], system_prompt="You are a helpful assistant." ) result = agent.invoke({ "messages": [{"role": "user", "content": "What's the weather in Paris?"}] }) print(result["messages"][-1].content) checkpointer = MemorySaver() agent = create_agent( model="anthropic:claude-sonnet-4-5", tools=[search], checkpointer=checkpointer, ) config = {"configurable": {"thread_id": "user-123"}} agent.invoke({"messages": [{"role": "user", "content": "My name is Alice"}]}, config=config) result = agent.invoke({"messages": [{"role": "user", "content": "What's my name?"}]}, config=config) Tools are functions that agents can call. Use the @tool decorator (Python) or tool() function (TypeScript). @tool def add(a: float, b: float) -> float: """Add two numbers. Middleware intercepts the agent loop to add human approval, error handling, logging, and more. A deep understanding of middleware is essential for production agents — use HumanInTheLoopMiddleware (Python) / humanInTheLoopMiddleware (TypeScript) for approval workflows, and @wrap_tool_call...