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langchain-middleware

Implement middleware for LangChain agents with human approval and structured outputs.

2|Updated Mar 7, 2026
Search Tags:#production#middleware#langchain#agent#human-in-the-loop#structured output
One-click install
npx skills add https://github.com/Lincyaw/AgentM --skill langchain-middleware-lincyaw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-middleware
Source: https://github.com/Lincyaw/AgentM/tree/main/.claude/skills/langchain-middleware
Command: npx skills add https://github.com/Lincyaw/AgentM --skill langchain-middleware-lincyaw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the need for robust, production-ready LangChain agents by providing essential middleware for human-in-the-loop approval, custom logic interception, and structured output generation.

Core Features & Use Cases

  • Human-in-the-Loop (HITL): Implement safety checks for dangerous tool calls, requiring human approval before execution.
  • Custom Middleware: Intercept and modify agent behavior for error handling, logging, and retry mechanisms.
  • Structured Output: Ensure agent outputs conform to predefined schemas using Pydantic or Zod.
  • Command Resume Patterns: Allow agents to continue execution after human decisions (approve, edit, reject).

Quick Start

Set up an agent with HITL middleware that pauses before sending emails for approval.

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 a LangChain agent?▼

Human-in-the-loop approval for a LangChain agent is implemented by intercepting dangerous tool calls with middleware that pauses execution, requiring human authorization before the action proceeds. This ensures safety checks for critical operations.

What is the best way to enforce structured output from a LangChain agent?▼

Structured output generation for LangChain agents is enforced using Pydantic or Zod schemas within middleware. This ensures agent outputs consistently conform to predefined data formats required for production applications.

How do I resume a LangChain agent after a human approves or rejects an action?▼

LangChain agent execution resumes after a human decision using command resume patterns. This middleware capability allows the workflow to continue automatically following an approval, edit, or rejection of an intercepted tool call.

Can I intercept LangChain agent errors and add custom logging logic?▼

Custom interception logic for LangChain agents is supported through middleware to handle errors, implement retry mechanisms, and add logging. This modifies agent behavior dynamically during execution without altering core logic.

Does this middleware support production-ready LangChain deployments?▼

Production-ready LangChain agents are supported by providing advanced middleware patterns for safety, error handling, and structured output. It equips agents with the robust logic interception required for live deployment environments.

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