langchain-fundamentals

To add human-in-the-loop approval to LangChain agents, you configure middleware hooks within the agent creation process. This mechanism intercepts tool execution, allowing your AI assistant to safely request human approval before performing risky operations.

You can persist conversational state across LangChain sessions by using a MemorySaver checkpointer. This component manages conversation memory across invocations, ensuring your AI assistant retains context and state between different user interactions.

You define custom tools for LangChain agents using the `@tool` decorator in Python or the `tool()` function in TypeScript. This creates reusable utilities that the agent can orchestrate and execute during its operational loop.

LangChain agents provide structured output through typed responses configured via `response_format` or model-level structured output. This ensures the agent returns data in a predictable format suitable for downstream application processing.

To build a production AI assistant with LangChain, you need the LangChain library, an appropriate LLM model, defined tool functions, and optional MemorySaver checkpointer for persistence. Middleware configuration is also required for human-in-the-loop controls.