explaingit

asyncfuncai/deepwiki-open

16,307PythonAudience · developerComplexity · 3/5Setup · moderate

TLDR

A self-hosted tool that automatically reads any GitHub, GitLab, or Bitbucket repo and generates a full wiki with written docs and relationship diagrams, powered by your choice of AI model.

Mindmap

mindmap
  root((repo))
    What it does
      Auto-generates wiki
      AI-powered docs
      Visual diagrams
    AI Providers
      Google Gemini
      OpenAI
      Local Ollama
      Azure OpenAI
    Tech stack
      Python FastAPI
      JavaScript frontend
      Docker
    Use cases
      Explore codebases
      Generate project docs
      Offline with Ollama
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Code map

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Things people build with this

USE CASE 1

Instantly generate documentation for an unfamiliar open-source codebase you want to understand or use.

USE CASE 2

Create a searchable internal wiki for your own project without writing a single doc by hand.

USE CASE 3

Run it offline with a local Ollama model to document private code without sending it to any cloud service.

USE CASE 4

Point it at a private GitLab or Bitbucket repo using a personal access token to auto-generate private docs.

Tech stack

PythonFastAPIJavaScriptDockerMermaidOllamaOpenAIGemini

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Docker and an API key for your chosen AI provider, or a running Ollama instance for fully offline use.

In plain English

DeepWiki-Open is a self-hosted, open-source tool that automatically generates a structured wiki (documentation site) for any GitHub, GitLab, or Bitbucket repository. You point it at a repo, and it clones the code, analyzes the structure, generates written documentation, creates visual diagrams showing how components relate to each other, and organizes everything into an easy-to-navigate wiki, all powered by AI. The process works by creating embeddings (a way of representing text and code as numbers that capture meaning) of the repository's contents, then using those embeddings to provide the AI model with relevant context when generating documentation. This technique is called RAG (Retrieval-Augmented Generation), it lets the AI accurately describe the code by looking up the relevant parts rather than trying to hold everything in memory at once. Visual diagrams are generated using Mermaid, a text-based diagram format. It supports multiple AI model providers so you can choose what powers the generation: Google Gemini, OpenAI, OpenRouter (a gateway to many models), local Ollama models (meaning you can run it completely offline without sending code to any external service), and Azure OpenAI. Private repositories are supported via personal access tokens. The stack is Python (FastAPI) for the backend and a JavaScript frontend. You run it locally via Docker or by starting the backend and frontend separately. You would use this if you want to quickly understand an unfamiliar codebase or create documentation for your own project. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Set up DeepWiki-Open locally using Docker and point it at my GitHub repo to generate a wiki, show me the exact steps.
Prompt 2
How do I configure DeepWiki-Open to use a local Ollama model so I can document private code without sending it to the cloud?
Prompt 3
I want to use DeepWiki-Open on a private GitLab repo using a personal access token, walk me through the config.
Prompt 4
How does DeepWiki-Open use RAG to generate accurate documentation and what embedding model does it use by default?
Prompt 5
Show me how to interpret the Mermaid relationship diagrams DeepWiki-Open generates for a repo's component structure.
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