langchain-fundamentals

To create a LangChain agent, use the create_agent() function to configure agent state, wire tools using @tool or tool() decorators, and apply middleware for production-ready workflows. It guides setup across Python and TypeScript.

Yes, it demonstrates middleware patterns specifically for human-in-the-loop approval. You configure middleware within the create_agent() setup to intercept agent actions and handle HITL workflows safely across Python and TypeScript environments.

Yes, create_agent supports state management and tool definitions in both Python and TypeScript. You define tools using @tool or tool() syntax, allowing you to configure production-grade agent workflows consistently across both languages.

The best way to handle errors in LangChain agent workflows is by applying middleware patterns. These patterns enforce safe usage by providing clear structures for error handling alongside human-in-the-loop logic within your create_agent() configuration.

You use middleware patterns to build robust LangChain agents because they enforce safe usage with clear structures for human-in-the-loop approval, error handling, and memory management. This streamlines production-grade agent configuration.