building-langgraph-agents
LangGraph development for stateful multi-agent applications, cyclic workflows, conditional routing, human-in-the-loop patterns, and persistent state management. Use for complex AI orchestration, agent coordination, and production-grade agentic systems.
npx skills add majiayu000/claude-skill-registry --skill building-langgraph-agents --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangGraph Development Skill ## Quick Reference LangGraph is LangChain's framework for building stateful, multi-actor applications with LLMs. It enables cyclic graphs, conditional branching, persistent state management, and human-in-the-loop workflows. **Key capabilities**: StateGraph for workflow orchestration, checkpointers for persistence, prebuilt ReAct agents, streaming at multiple granularities, and LangGraph Platform for managed deployment. --- ## Table of Contents 1. [When to Use](#when-to-use) 2. [Package Ecosystem](#package-ecosystem) 3. [Quick Start - Python](#quick-start---python) 4. [Quick Start - TypeScript](#quick-start---typescript) 5. [Core Concepts](#core-concepts) 6. [Prebuilt Components](#prebuilt-components) 7. [Common Patterns](#common-patterns) 8. [Quick Persistence Setup](#quick-persistence-setup) 9. [CLI Commands](#cli-commands) 10. [Error Quick Reference](#error-quick-reference) 11. [When to Use REFERENCE.md](#when-to-use-referencemd) 12. [Agent Integration](#agent-integration) 13. [See Also](#see-also) --- ## When to Use This skill is loaded by `backend-developer` when: - `langgraph` in `requirements.txt`, `pyproject.toml`, or `setup.py` - `@langchain/la
- Quick Reference
- Table of Contents
- When to Use
- Package Ecosystem
- Quick Start - Python
- With LLM and Tools
- Quick Start - TypeScript
- Core Concepts
- StateGraph
- State Schema
- Nodes
- Edges
- Special Nodes
- Prebuilt Components
Install pip install langgraph langgraph-checkpoint-postgres Verify python -c "import langgraph; print(langgraph.__version__)" LangGraph Platform langgraph init # Initialize project langgraph dev # Start dev server langgraph build # Build for deployment langgraph up # Deploy with Docker export LANGSMITH_API_KEY="ls-..."
What does the building-langgraph-agents skill do?
LangGraph development for stateful multi-agent applications, cyclic workflows, conditional routing, human-in-the-loop patterns, and persistent state management. Use for complex AI orchestration, agent coordination, and production-grade agentic systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill building-langgraph-agents --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.