langgraph-docs
Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.
Install
npx skillscat add langchain-ai/deepagents/langgraph-docs Install via the SkillsCat registry.
Here's a thinking process: 1. **Analyze User Request:** - **Goal:** Write a 2-3 sentence plain-text summary in English - **Subject:** An AI agent skill that fetches LangGraph Python documentation - **Purpose:** Explain what it does, what problem it solves, when to use it - **Constraints:** - Objective, factual tone only - No marketing language, superlatives, calls to action - Natural prose, no bullet points, no headings, no markdown - No quotes wrapping - At most 60 words - **Output:** ONLY the summary text 2.
langgraph-docs
Workflow
1. Fetch the Documentation Index
Use fetch_url to read: https://docs.langchain.com/llms.txt
This returns a structured list of all available documentation with descriptions.
2. Select Relevant Documentation
Identify 2-4 most relevant URLs from the index. Prioritize:
- Implementation questions — specific how-to guides
- Conceptual questions — core concept pages
- End-to-end examples — tutorials
- API details — reference docs
3. Fetch and Apply
Use fetch_url on the selected URLs, then complete the user's request using the documentation content.
If fetch_url fails or returns empty content, retry once. If it fails again, inform the user and suggest checking https://langchain-ai.github.io/langgraph/ directly.
Categories
Install
npx skillscat add langchain-ai/deepagents/langgraph-docs Install via the SkillsCat registry.