langchain-fundamentals

Build production-ready AI agents using LangChain by utilizing the `create_agent()` function to handle the core agent loop, tool execution, state persistence, and middleware integration for advanced control flows like human-in-the-loop approvals.

The best way to define custom tools for AI agents in Python is using the `@tool` decorator. In TypeScript, use the `tool()` function. This allows your agent to interact with external functions predictably.

Manage state persistence in LangChain conversations by implementing a `checkpointer` alongside a `thread_id`. This approach enables conversation memory, ensuring your AI agent maintains stateful interactions across multiple exchanges reliably.

You can enforce structured output from AI agents using Pydantic models in Python or Zod schemas in TypeScript. This ensures agents return data in a predictable, typed format for reliable data exchange between systems.

Add middleware for human-in-the-loop approvals in LangChain by integrating it directly into your agent framework. This middleware enhances agents with custom logic, human-in-the-loop approvals, and robust error handling capabilities.

You do not need external dependencies to handle error handling in LangChain agents. The framework supports integrating middleware directly to enhance agents with custom logic, human-in-the-loop approvals, and robust error handling.