Self-hosted alternatives to langgraph-docs
5 self-hosted AI Skills apps to use instead, best rated first.
Top picks
- C

claude-to-deerflow
Launchclaude-to-deerflow is an agent skill that lets an assistant send messages and questions to a running DeerFlow instance through its HTTP API. DeerFlow is an AI agent platform built on LangGraph that orchestrates sub-agents for research, code
Self-hosted
- O

open-notebook
Launchopen-notebook is an agent skill for organising research with Open Notebook, a self-hosted alternative to NotebookLM. It arranges sources, notes and AI conversations into research notebooks. Sources can be PDFs, web pages, audio, video and O
Self-hosted
- R

Reactive Resume
LaunchReactive Resume is a free and open-source resume builder for writing resumes and cover letters, checking the work, and tracking job applications. You create a free account, import an existing resume or start from scratch, and see the finish
Open sourceSelf-hosted
- S

self-hosted-funnel-launch
LaunchSelf-hosted funnel launch is an agent skill that takes a funnel from an empty install to published on infrastructure you control, using the Autonnel funnel builder, which is licensed Apache-2.0. The result covers a landing page, checkout, a
Self-hosted
- G

get-convex/convex-backend
LaunchConvex is an open-source reactive database for web app developers. It combines a database, a place to write server functions in pure TypeScript, and client libraries that keep the app's data live, so screens update as the data changes. Beca
Self-hosted
About langgraph-docs

langgraph-docs
Fetches and references LangGraph Python documentation to build stateful agents
langgraph-docs is an agent skill from the langchain-ai deepagents project, listed on skills.sh. It fetches and refers to the LangGraph Python documentation, so an agent can build stateful agents, multi-agent workflows and human-in-the-loop patterns from the current docs. The workflow has three steps. The agent first reads the documentation index from the LangChain docs site, which lists every page with a short description. It then picks two to four of the most relevant pages, favouring how-to guides for implementation questions, core concept pages for conceptual questions, tutorials for end-to-end examples and reference pages for API details. Finally it fetches those pages and applies what they say. The approach means the agent works from the documentation as it is now, rather than from what it remembers about an older version of the library. It suits developers building agents with LangGraph in Python who want accurate, current guidance for each question.
- Price
- Free
- Made by
- langchain-ai
- Category
- AI Skills
- Website
- github.com/langchain-ai/deepagents/langgraph-docs