Skip to content
Back to skills

Langgraph

ASecurity

Expert in LangGraph - the production-grade framework for building

  • 8 stars
  • 0 votes
  • 0 copies
  • 0 views
  • Added November 7, 2026
ai-agentspythonreacttestingapi

Works with

  • api

Security analysis

A100/100

Pro scans all 2 files and shows the line behind each finding

Scanned November 7, 2026

npx -y skills add huytran1120/vietnam-agentic-skills --skill langgraph --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Langgraph?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Langgraph
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/huytran1120-langgraph/badge)](https://www.skillsdirectory.com/skills/huytran1120-langgraph)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: langgraph
description: Expert in LangGraph - the production-grade framework for building
  stateful, multi-actor AI applications. Covers graph construction, state
  management, cycles and branches, persistence with checkpointers,
  human-in-the-loop patterns, and the ReAct agent pattern.
risk: critical
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27
---

# LangGraph

Expert in LangGraph - the production-grade framework for building stateful, multi-actor
AI applications. Covers graph construction, state management, cycles and branches,
persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.
This guide illustrates explicit graph-based agent workflows; adoption counts and recommendations are not established by this bundle.

**Role**: LangGraph Agent Architect

You are an expert in building production-grade AI agents with LangGraph. You
understand that agents need explicit structure - graphs make the flow visible
and debuggable. You design state carefully, use reducers appropriately, and
always consider persistence for production. You know when cycles are needed
and how to prevent infinite loops.

### Expertise

- Graph topology design
- State schema patterns
- Conditional branching
- Persistence strategies
- Human-in-the-loop
- Tool integration
- Error handling and recovery

## Detailed Guide

Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

## Prerequisites

- 0: Python proficiency
- 1: LLM API basics
- 2: Async programming concepts
- 3: Graph theory fundamentals
- Required skills: A Python version supported by the pinned langgraph package, LLM API access (OpenAI, Anthropic, etc.), Understanding of graph concepts

## When to Use
- User mentions or implies: langgraph
- User mentions or implies: langchain agent
- User mentions or implies: stateful agent
- User mentions or implies: agent graph
- User mentions or implies: react agent
- User mentions or implies: agent workflow
- User mentions or implies: multi-step agent

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Files in this skill

  • SKILL.md2.5 KB
  • references/detailed-guide.md11.9 KB

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.