How does this skill help with AI agent safety?

It provides patterns to insert 'interrupt' points before critical actions, ensuring an agent cannot proceed with high-risk tasks without manual human verification.

Can I integrate these patterns with a web UI?

Yes, the skill includes API integration patterns using FastAPI that allow external applications to fetch the current state, provide feedback, and resume the workflow.

What is an 'interrupt_before' node in LangGraph?

It is a configuration that forces the workflow to pause and save its state to a checkpointer before executing a specific node, allowing for manual inspection or intervention.

How does the agent resume after being paused?

The workflow is resumed by calling the invoke method with the same configuration (thread_id) after the state has been updated with the human's decision or feedback.

LangGraph Human-in-the-Loop

Name: LangGraph Human-in-the-Loop
Author: yonatangross

byyonatangross

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Data Science & ML

Implements robust human-in-the-loop patterns for LangGraph workflows to enable manual review gates and approval-based agent supervision.

About

This skill provides production-ready patterns for integrating human intervention into LangGraph-driven AI workflows. It enables developers to pause agent execution at critical nodes using interrupt points, collect user feedback, and resume processing only after explicit approval or state modification. By facilitating manual review gates, interactive supervision, and feedback loops, it ensures that high-stakes agentic decisions remain under human control while maintaining workflow state and persistence via thread-based checkpointers.

Key Features

Feedback loop integration for iterative human-agent collaboration and refinement
29 GitHub stars
FastAPI-ready patterns for building production approval gate endpoints
Multi-point approval workflows for complex, multi-stage supervised processes
Interrupt-before patterns to pause workflows at critical decision nodes
Workflow resumption mechanisms with state updates after human approval

Use Cases

Collecting human feedback on agent-generated content and allowing the agent to refine work based on that input
Developing interactive supervision tools for complex multi-agent systems requiring human oversight at specific milestones
Implementing manual approval gates before an agent executes a financial transaction or production code deployment

About

Key Features

Feedback loop integration for iterative human-agent collaboration and refinement
29 GitHub stars
FastAPI-ready patterns for building production approval gate endpoints
Multi-point approval workflows for complex, multi-stage supervised processes
Interrupt-before patterns to pause workflows at critical decision nodes
Workflow resumption mechanisms with state updates after human approval

Use Cases

Collecting human feedback on agent-generated content and allowing the agent to refine work based on that input
Developing interactive supervision tools for complex multi-agent systems requiring human oversight at specific milestones
Implementing manual approval gates before an agent executes a financial transaction or production code deployment