langchain-fundamentals

Build LangChain agents with middleware by using the create_agent() function to abstract the agent loop, tool execution, and state management. This Skill provides ready-to-run templates in both Python and TypeScript to standardize agent workflows.

Define custom tools for LangChain agents using the @tool decorator in Python or the tool() function in TypeScript. This Skill enables reusable action definitions that integrate seamlessly with the create_agent() workflow.

Middleware handles human-in-the-loop patterns in LangChain by intercepting agent decisions for controlled, auditable execution. This ensures safe control flow and error handling before tools execute critical actions.

Yes, this LangChain agent template supports structured outputs and memory management by enforcing best practices for state retention. It standardizes memory handling alongside tool definitions within the create_agent() workflow.

Standardize error-handling patterns across LangChain agents in Python and TypeScript by applying consistent middleware logic. This Skill enforces uniform error-handling and control flow patterns across both language implementations.

Use create_agent instead of a custom LangChain agent loop when you need standardized tool integration, memory management, and middleware support. It abstracts complex state management to accelerate development.