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langchain-middleware

Intercept LangChain/LangGraph agent tool calls for human-in-the-loop approvals.

Updated May 26, 2026
Search Tags:#middleware#langchain#checkpointing#langgraph#structured output#tool calls#human approval
One-click install
npx skills add https://github.com/anukkrit149/anukkrit-skills --skill langchain-middleware-anukkrit149
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-middleware
Source: https://github.com/anukkrit149/anukkrit-skills/tree/main/cloud/skills/langchain-middleware
Command: npx skills add https://github.com/anukkrit149/anukkrit-skills --skill langchain-middleware-anukkrit149

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangChain agents can perform risky tool calls without a reliable, auditable approval step, and teams need a consistent way to validate, intercept, and resume those actions.

Core Features & Use Cases

  • Human-in-the-loop approvals (HITL): Pause before dangerous tool calls and route decisions through an approval workflow (approve, edit, reject).
  • Custom middleware hooks: Intercept agent execution to add logging, retry/error handling, and tool-call policies.
  • Command-based resume patterns: Continue execution after human decisions using checkpointed state and thread tracking.
  • Structured output enforcement: Use middleware-driven patterns to support structured outputs validated by schemas (e.g., Pydantic/Zod).

Quick Start

Use langchain-middleware to configure a human approval gate that interrupts on the tool name send_email and resumes only after an approve, edit, or reject decision for a given thread_id.

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 approval steps to LangChain agent tool calls?▼

Human-in-the-loop approvals for LangChain agents are implemented by intercepting risky tool calls through middleware, pausing execution, and routing decisions through an approve, edit, or reject workflow.

How does LangGraph checkpointing work with human-in-the-loop workflows?▼

LangGraph checkpointing saves the agent state using a thread_id configuration, allowing execution to pause before dangerous actions and safely resume via Command after a human decision is made.

Can I intercept and edit LangChain tool arguments before execution?▼

Yes, custom middleware hooks can deterministically intercept LangChain agent execution, allowing you to edit tool arguments, apply logging, enforce tool-call policies, or reject actions with feedback before resuming.

Do I need a checkpointer to use human-in-the-loop patterns in LangGraph?▼

Yes, a checkpointer is required to satisfy operational requirements for safe, auditable middleware-driven control flow, ensuring state persistence across pauses and resumptions in human approval workflows.

What is the best way to enforce structured output schemas in LangGraph agents?▼

Middleware-driven patterns support structured outputs validated by schemas like Pydantic or Zod, intercepting agent execution to ensure tool calls and responses conform to required data structures.

How to resume LangGraph agent execution after rejecting a tool call?▼

Resume execution after rejecting a tool call by using Command-based patterns with checkpointed state and thread tracking, passing the reject decision with feedback back into the middleware control flow.

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