langchain-middleware

Implement middleware patterns for LangChain agents with human-in-the-loop approval.

3|Updated Jan 20, 2026

Search Tags:#production #error handling #middleware #langchain #agent #human-in-the-loop #structured output

Ready to run

npx skills add https://github.com/Sameeh07/AGENT-SKILLS --skill langchain-middleware-sameeh07

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-middleware
Source: https://github.com/Sameeh07/AGENT-SKILLS/tree/main/skills/langchain-middleware
Command: npx skills add https://github.com/Sameeh07/AGENT-SKILLS --skill langchain-middleware-sameeh07

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the creation of robust, production-ready LangChain agents by incorporating essential middleware patterns for human oversight, custom logic, and structured data handling.

Core Features & Use Cases

Human-in-the-Loop (HITL): Implement approval workflows for sensitive tool calls (e.g., sending emails, deleting data).
Custom Middleware: Intercept and modify agent execution for error handling, logging, or custom logic.
Command Resume: Allow users to resume agent execution after an interrupt, with options to approve, edit, or reject actions.
Structured Output: Facilitate structured data output using Pydantic or Zod.

Quick Start

Use the langchain-middleware skill to set up an agent with human-in-the-loop approval before sending emails.

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 for dangerous tool calls in a LangChain agent?▼

To add human-in-the-loop approval in a LangChain agent, use HumanInTheLoopMiddleware to intercept sensitive tool calls. This pattern requires a checkpointer and thread_id to pause execution and wait for human decisions before proceeding.

How do I resume LangChain agent execution after a human-in-the-loop interrupt?▼

You resume LangChain agent execution after an interrupt by using command resume functionality. This allows users to approve, edit, or reject the paused action so the agent can continue the workflow with the human decision applied.

What do I need to set up human-in-the-loop workflows in LangChain?▼

Setting up human-in-the-loop workflows in LangChain requires a checkpointer and a thread_id. These components maintain state across pauses so the agent can suspend execution for human approval and safely resume afterward.

Can I use custom middleware for error handling and logging in LangChain agents?▼

Yes, you can use custom middleware for error handling and logging in LangChain agents. Custom middleware intercepts and modifies agent execution, allowing you to inject custom logic for robust production error management and execution tracking.

How do I get structured output from LangChain agents using Pydantic or Zod?▼

You get structured output from LangChain agents by defining schemas with Pydantic or Zod. The middleware facilitates structured data handling to ensure agent responses conform to the expected data formats for downstream processing.

When do I need middleware for production LangChain agents?▼

You need middleware for production LangChain agents when incorporating essential patterns for human oversight, custom logic, and structured data handling. It solves the problem of making agents robust by adding error handling and approval workflows.