Access LangGraph documentation to build stateful agents and multi-agent workflows.
Works with
Fetches official LangGraph Python docs covering state machines, graph-based agent design, and human-in-the-loop patterns
Prioritizes relevant documentation by query type: implementation guides for how-to questions, concept pages for theory, tutorials for end-to-end examples, and API references for technical details
Automatically selects 2–4 most relevant documentation URLs and retrieves their content t
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlanggraph-docsExecute the skills CLI command in your project's root directory to begin installation:
Fetches langgraph-docs from langchain-ai/deepagents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate langgraph-docs. Access via /langgraph-docs in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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22:T48c,<h1>langgraph-docs</h1>
<h2>Workflow</h2> <h3>1. Fetch the Documentation Index</h3> <p>Use <code>fetch_url</code> to read: <a href="https://docs.langchain.com/llms.txt" target="_blank" rel="ugc nofollow noopener">https://docs.langchain.com/llms.txt</a></p> <p>This returns a structured list of all available documentation with descriptions.</p> <h3>2. Select Relevant Documentation</h3> <p>Identify 2-4 most relevant URLs from the index. Prioritize:</p> <ul> <li><strong>Implementation questions</strong> — specific how-to guides</li> <li><strong>Conceptual questions</strong> — core concept pages</li> <li><strong>End-to-end examples</strong> — tutorials</li> <li><strong>API details</strong> — reference docs</li> </ul> <h3>3. Fetch and Apply</h3> <p>Use <code>fetch_url</code> on the selected URLs, then complete the user's request using the documentation content.</p> <p>If <code>fetch_url</code> fails or returns empty content, retry once. If it fails again, inform the user and suggest checking <a href="https://langchain-ai.github.io/langgraph/" target="_blank" rel="ugc nofollow noopener">https://langchain-ai.github.io/langgraph/</a> directly.</p>1d:["$","div",null,{"className":"prose prose-invert max-w-none prose-headings:font-semibold prose-headings:tracking-tight prose-h1:text-4xl prose-h1:mb-2 prose-h2:text-2xl prose-h2:mb-2 prose-h3:text-lg prose-h3:mb-2 prose-p:text-muted-foreground prose-li:text-muted-foreground prose-code:bg-muted prose-code:text-foreground prose-code:px-1 prose-code:py-0.5 prose-code:rounded-sm prose-code:text-sm prose-code:before:content-none prose-code:after:content-none prose-pre:bg-muted prose-pre:text-foreground prose-pre:border prose-pre:border-border prose-pre:rounded-md [&_table]:!border-[color:var(--border)] [&_th]:!border-[color:var(--border)] [&_td]:!border-[color:var(--border)]","dangerouslySetInnerHTML":{"__html":"$22"}}] 18:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]] 16:nullPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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langgraph-docs fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
langgraph-docs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in langgraph-docs — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
langgraph-docs has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend langgraph-docs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
langgraph-docs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
langgraph-docs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added langgraph-docs from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: langgraph-docs is focused, and the summary matches what you get after install.
Keeps context tight: langgraph-docs is the kind of skill you can hand to a new teammate without a long onboarding doc.
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