awesome-LangGraph: An index of the LangChain + LangGraph ecosystem: concepts, projects, tools, templates, and guides for LLM

by vonzosten · Agent Tool · ★ 2.0k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 60/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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Quick Facts

Stars2,034
Forks294
LanguageJavaScript
CategoryAgent Tool
LicenseCC0-1.0
Quality Score60/100
Open Issues44
Last Updated2026-10-15
Created2024-11-02
Platformsnode
Est. Tokens~25k

About awesome-LangGraph

🦜🕸️ Awesome LangGraph & LangChain Ecosystem The definitive index of frameworks, templates, and real-world projects for teams that want to build, observe, evaluate, and deploy stateful, tool-using AI agents with the LangChain + LangGraph stack.

Compatible Skills

These tools work well together with awesome-LangGraph for enhanced workflows:

  • langchain-mcp-adapters — semantic(0.36)+complementary+shared_fw(langchain)+similar_pop (55%)
  • flock — semantic(0.32)+complementary+shared_fw(langchain)+similar_pop+shared_platform (54%)
  • awesome-free-llm-apis — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (54%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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