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LangChainKnown publisherlangchain-ai/docs/src/code-samples/deepagents/skills/langgraph-docs/SKILL.md

langgraph-docs

Skill445 starsChanged 10 days agoMITView on GitHub

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.

4 other projects use a version of this, 3 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use fetch_url to read: https://docs.langchain.com/llms.txt

    line 12
  2. Very specific:

    Use fetch_url on the selected URLs, then complete the user's request using the documentation content.

    line 26
  3. Very specific:

    If fetch_url fails or returns empty content, retry once.

    line 28

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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://docs.langchain.com/oss/python/langgraph/overview directly.

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