langchain-middleware

Integrate human approval and middleware hooks into LangChain agents.

8|Updated Jul 26, 2026

Search Tags:#automation #middleware #langchain #langgraph #human-in-the-loop #agent-safety

Ready to run

npx skills add https://github.com/joonlab/joonlab-claudecode-setting-for-share --skill langchain-middleware-joonlab

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-middleware
Source: https://github.com/joonlab/joonlab-claudecode-setting-for-share/tree/main/claude/skills/langchain-middleware
Command: npx skills add https://github.com/joonlab/joonlab-claudecode-setting-for-share --skill langchain-middleware-joonlab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, @langchain/langgraph, @langchain/core, zod.

What problem does it solve?

This skill addresses the lack of safety and control in autonomous agents by providing structured middleware patterns for human approval, custom logic interception, and state management.

Core Features & Use Cases

Human-in-the-Loop (HITL): Pause agent execution before dangerous tool calls to require human approval, editing, or rejection.
Custom Middleware Hooks: Intercept tool calls for logging, error handling, or retry logic.
Command Resume Patterns: Seamlessly continue agent execution after human intervention using thread-safe state management.

Quick Start

Invoke the langchain-middleware skill to configure a HumanInTheLoopMiddleware that pauses the agent before executing the send_email tool.

Frequently Asked Questions about langchain-middleware

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I add human-in-the-loop approval to LangChain agents?▼

You can add human-in-the-loop approval by configuring a middleware that pauses LangChain agent execution before dangerous tool calls to require explicit human authorization, editing, or rejection.

What is the best way to intercept LangGraph tool calls for logging and error handling?▼

Intercepting LangGraph tool calls is best handled by custom middleware hooks that capture tool invocations mid-execution to apply custom logging, error handling, or retry logic.

Can I resume LangGraph agent execution after human intervention without losing state?▼

Yes, you can resume agent execution using command resume patterns that leverage thread-safe state management to seamlessly continue processing after a human intervention.

Do I need LangGraph to implement command resume patterns for autonomous agents?▼

Yes, LangGraph is required as a dependency alongside LangChain and Zod to facilitate the thread-safe state persistence needed for command resume patterns.

Why does my autonomous agent execute sensitive tool calls without asking for confirmation?▼

Autonomous agents execute sensitive calls directly when no human-in-the-loop middleware is present, making structured middleware patterns necessary to require explicit approval for dangerous tool calls.

What are the limitations of using middleware for agent safety in production environments?▼

Middleware for agent safety requires structured command-based execution and state persistence to maintain production-grade reliability, meaning poorly managed thread-safe states can disrupt the command resume workflow.