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Lauren Hirata SinghandGitHub
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## Why `docs/llms.txt` is written by hand and was last touched in February, so the index served at <https://langchain-ai.github.io/langgraph/llms.txt> had drifted badly: - 12 pages listed, against the 43 the docs site publishes. - One entry, `/oss/python/langgraph/why-langgraph`, now 404s. The deploy workflow only fires on `docs/**` pushes, so nothing brought the file back in line as the docs site changed. It is served verbatim: `generate_redirects.py` copies it into `docs/_site` via a `static_files` list, and `deploy-redirects.yml` uploads that directory to Pages. The docs site already generates exactly this index, and regenerates it on every deploy: ``` https://docs.langchain.com/oss/python/langgraph/llms.txt ``` ## What changed `generate_redirects.py` now fetches that index at build time and writes it into `_site`, so the published file cannot drift from the docs site. The committed `docs/llms.txt` stays as a fallback only, refreshed here to current content. `deploy-redirects.yml` gains a weekly `schedule:` trigger, so docs changes that never touch this repo still reach the deployed file. The `permissions:` block is unchanged. ## Fetching remote content in CI The fetched body is published on a public Pages site, so it is validated before it is written: - The URL is a hardcoded module constant, never built from input. - Both that URL and the post-redirect `response.url` are checked against an HTTPS-plus-single-host allowlist. `urlopen` follows redirects, so checking only the request URL would not be enough. - 30 second timeout, response capped at 1MB, decoded as UTF-8. - The body must open with a markdown heading and contain a docs.langchain.com link, which rejects an error page or a truncated response. Any failure returns `None` and falls back to the committed copy, so a docs.langchain.com outage degrades to a stale file rather than a broken deploy or a published error page. ## Verification Ran `python docs/generate_redirects.py`: 294 redirect files, `llms.txt` fetched, 43 entries. Each rejection path was exercised against a stubbed `urlopen` and all fall back to the committed file: | Case | Result | |---|---| | Network error | falls back | | Timeout | falls back | | Redirect to another host | falls back | | Body over 1MB | falls back | | Invalid UTF-8 | falls back | | HTML error page | falls back | | Empty body | falls back | | Valid index | published | Allowlist rejects `http://docs.langchain.com/...`, `https://evil.com/...`, and `https://docs.langchain.com.evil.com/...`. ## Note on ordering The fallback committed here is labelled "LangGraph (Python)", which is what the docs site will serve once langchain-ai/docs#6032 deploys. Until then the fetched file reads "Open source (Python)". No action needed; it self-corrects. --- Written with Claude Code; I reviewed the diff and ran the verification above.
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph
Tip
If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent JS/TS library, check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
- LangGraph Quickstart – Get started building with LangGraph
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- LangChain Academy – Learn the basics of LangGraph in our free, structured course.
- Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.