Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LiHongwei-cn/lihongwei-cn --skill langgraphgit clone --depth 1 https://github.com/LiHongwei-cn/lihongwei-cnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lihongwei-cn/lihongwei-cn/langgraph)<a href="https://agentmods.dev/skills/lihongwei-cn/lihongwei-cn/langgraph"><img src="https://agentmods.dev/badge/skills/lihongwei-cn/lihongwei-cn/langgraph/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lihongwei-cn/lihongwei-cn/langgraph"><img src="https://agentmods.dev/badge/skills/lihongwei-cn/lihongwei-cn/langgraph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.03152 |
| Opus 5 | $0.00026 | $0.01576 |
| Sonnet 5 | $0.00011 | $0.00630 |
| Haiku 4.5 | $0.00005 | $0.00315 |
Grade A, and why
langgraph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to langgraph — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 506 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents.
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
Capabilities
- Graph construction (StateGraph)
- State management and reducers
- Node and edge definitions
- Conditional routing
- Checkpointers and persistence
- Human-in-the-loop patterns
- Tool integration
- Streaming and async execution
Prerequisites
- 0: Python proficiency
- 1: LLM API basics
- 2: Async programming concepts
- 3: Graph theory fundamentals
- Required skills: Python 3.9+, langgraph package, LLM API access (OpenAI, Anthropic, etc.), Understanding of graph concepts
Scope
- 0: Python-only (TypeScript in early stages)
- 1: Learning curve for graph concepts
- 2: State management complexity
- 3: Debugging can be challenging
Ecosystem
Primary
- LangGraph
- LangChain
- LangSmith (observability)
Common_integrations
- OpenAI / Anthropic / Google
- Tavily (search)
- SQLite / PostgreSQL (persistence)
- Redis (state store)
Platforms
- Python applications
- FastAPI / Flask backends
- Cloud deployments
Patterns
Basic Agent Graph
Simple ReAct-style agent with tools
When to use: Single agent with tool calling
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 506 lines · 53 tokens per session scan A c208e453c478
langgraph is a skill published in the GitHub repository LiHongwei-cn/lihongwei-cn (5 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 3,152 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
ai-provider-anthropic-sdk
Official Anthropic SDK patterns for TypeScript/Node.js — client setup, Messages API, streaming, tool use, vision, extended thinking, structured outputs, prompt caching, batch API, and production best practices.
ai-infrastructure-huggingface-inference
Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription, translation, summarization, and Inference Endpoints.
ai-infrastructure-ollama
Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint.
ai-infrastructure-replicate
Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training.
ai-infrastructure-together-ai
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and OpenAI-compatible endpoints.
ai-observability-langfuse
LLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices.