Does this skill support custom logging?

Yes, it supports custom middleware hooks like before_model, wrap_tool_call, and after_agent for custom logging, metrics, and error handling.

What is required to use the HITL features?

You must configure a checkpointer (such as MemorySaver) and provide a thread_id in the config to persist the agent's state across pauses.

How do I resume execution after a human intervention?

You use the Command class with a resume payload containing the decision (approve, edit, or reject) and any edited tool arguments.

Can I apply HITL to specific tools only?

Yes, you can define per-tool policies, enabling approval for dangerous actions like 'delete_email' while allowing 'read_email' actions to proceed automatically.

What is the purpose of the Human-in-the-Loop middleware?

It allows you to pause an agent's execution before a tool is called, requiring a human to approve, edit, or reject the action to ensure safety.

LangChain Middleware & HITL

Name: LangChain Middleware & HITL
Author: langchain-ai

bylangchain-ai

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534

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데이터 과학 및 ML

Implements human-in-the-loop safety protocols and custom middleware patterns for production-grade LangChain agents.

The LangChain Middleware skill provides essential patterns for building reliable and safe AI agents by introducing control layers between the model and its environment. It enables developers to pause execution for human approval before high-risk actions—such as sending emails or modifying databases—and allows for custom tool-call interception to handle logging, retries, and error management. By integrating HumanInTheLoopMiddleware and the Command resume pattern, this skill ensures agents operate within predefined safety boundaries while maintaining stateful, asynchronous workflows across user sessions.

주요 기능

01534 GitHub stars

02State persistence integration using LangGraph checkpointers

03Human-in-the-loop (HITL) approval for high-risk tool calls

04Command resume patterns to continue execution after human intervention

05Custom middleware hooks for logging, error handling, and retries

06Granular per-tool policies for approval, editing, and rejection

사용 사례

01Requiring manual verification before an agent executes financial transactions or data deletions

02Manually correcting AI-generated tool parameters before they are submitted to a backend API

03Intercepting and logging tool arguments for debugging and observability in production

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add langchain-ai/langchain-skills langchain-middleware

For use in Claude.ai and ChatGPT

주요 기능

01534 GitHub stars

02State persistence integration using LangGraph checkpointers

03Human-in-the-loop (HITL) approval for high-risk tool calls

04Command resume patterns to continue execution after human intervention

05Custom middleware hooks for logging, error handling, and retries

06Granular per-tool policies for approval, editing, and rejection

사용 사례

01Requiring manual verification before an agent executes financial transactions or data deletions

02Manually correcting AI-generated tool parameters before they are submitted to a backend API

03Intercepting and logging tool arguments for debugging and observability in production

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add langchain-ai/langchain-skills langchain-middleware

For use in Claude.ai and ChatGPT