langchain-fundamentals

Build production LangChain agents by using create_agent to configure tool sets, system prompts, and persistence. Define custom tools using the Python @tool decorator or TypeScript tool() function with clear descriptions and schemas for agent integration.

Middleware patterns in LangChain handle approval flows, logging, and error handling across agent lifecycles. You need middleware for human-in-the-loop interactions and structured error management when building autonomous workflows or QA assistants.

Yes, LangChain create_agent applies to both Python and TypeScript ecosystems. Python uses the @tool decorator for tool definitions, while TypeScript uses the tool() function, allowing you to build agents in either language environment.

Enable stateful sessions for LangChain agents by configuring a memory checkpointer with create_agent. This persistence mechanism allows the agent to maintain context and state across multiple interactions within a user session.

Yes, LangChain middleware supports human-in-the-loop approval flows. You can apply middleware patterns across agent lifecycles to intercept actions, require manual approval, handle errors, and log activities before the agent proceeds.

Structure LangChain tool definitions using the @tool decorator in Python or tool() in TypeScript, providing clear descriptions and schemas. This approach ensures agents can properly understand and invoke tools during autonomous data workflows or research tasks.