langchain-middleware

Human-in-the-loop middleware intercepts LangChain agent operations to pause execution for human approval. This allows you to review, edit, or reject sensitive tool calls before the agent resumes its process.

Custom middleware hooks intercept and modify tool calls within LangGraph workflows. They enable dynamic error handling, logging, and operational constraint enforcement during agent execution.

Yes, LangChain agent outputs can be structured using Pydantic. Middleware supports structured output formatting to enforce data schemas and validate agent-generated results.

LangChain middleware integrates with LangGraph to provide pause and resume interfaces. After human intervention, command resumes continue execution by approving, editing, or rejecting proposed tool calls.

LangChain human-in-the-loop workflows require Python libraries including langchain, langgraph, memorysaver, pydantic, and zod. These dependencies support middleware functionality, state persistence, and structured output validation.