DeepWiki-Open

DeepWiki-Open: AI-Powered Wiki Generator for Code Repositories

DeepWiki-Open is a powerful open-source tool designed to revolutionize how developers document their code repositories. By leveraging advanced AI technologies, it transforms any GitHub, GitLab, or BitBucket repository—public or private—into a beautifully structured, interactive Wiki in mere seconds. This project, developed by AsyncFuncAI, addresses a common pain point in software development: the time-consuming and often inconsistent process of creating and maintaining documentation. Instead of manual writing, DeepWiki-Open automates the entire workflow, making it accessible even for large and complex codebases.

Core Functionality

At its heart, DeepWiki-Open clones the target repository (with secure token-based access for private repos), analyzes the code's structure and relationships, and uses AI to generate human-readable explanations. Key steps include:

• Code Analysis: Parses files, identifies modules, dependencies, and data flows using intelligent AI understanding.
• Documentation Generation: Produces detailed descriptions, function breakdowns, and architecture overviews powered by large language models (LLMs).
• Visual Aids: Automatically generates Mermaid diagrams to illustrate architecture, workflows, and interactions, enhancing comprehension.
• Wiki Organization: Structures the output into a navigable format with sections, indexes, and search capabilities.

This results in a professional Wiki that serves as an on-demand knowledge base for teams, contributors, and users.

Advanced Features

DeepWiki-Open goes beyond basic documentation with innovative AI-driven interactions:

• Ask Feature: Engage in natural language conversations with your repository using Retrieval-Augmented Generation (RAG). Query specific code elements, get explanations, or troubleshoot issues based on real codebase context.
• DeepResearch: For complex queries, this multi-turn process conducts iterative investigations—up to five rounds—building a research plan, gathering insights, and delivering a comprehensive conclusion. Ideal for in-depth explorations of architecture or optimization strategies.
• Multi-Provider Support: Flexibility is key. Choose from Google Gemini (default: gemini-2.5-flash), OpenAI (e.g., gpt-4o), OpenRouter (access to hundreds of models like Claude or Llama), Azure OpenAI, or local Ollama for privacy-focused deployments. Embeddings can switch between OpenAI, Google AI (text-embedding-004), or Ollama for optimal retrieval.
• Private Repository Handling: Securely access non-public repos via personal access tokens, ensuring enterprise compatibility.
• Customization: JSON config files (generator.json, embedder.json, repo.json) allow fine-tuning models, filters, chunking, and limits without code changes. Environment variables control API keys, logging, and auth modes.

Deployment and Setup

Getting started is straightforward with two primary options:

Docker (Quick and Isolated)

Use Docker Compose for one-command setup:

git clone https://github.com/AsyncFuncAI/deepwiki-open.git
cd deepwiki-open
echo "GOOGLE_API_KEY=your_key" > .env  # Add other keys as needed
docker-compose up

This runs the backend API on port 8001 and frontend on 3000. Data persists via mounted volumes (~/.adalflow for repos, embeddings, and cache).

Manual Setup (For Customization)

1. Set up .env with API keys (e.g., GOOGLE_API_KEY, OPENAI_API_KEY).
2. Backend: poetry install -C api && python -m api.main.
3. Frontend: npm install && npm run dev. Access at http://localhost:3000, input a repo URL (e.g., https://github.com/openai/codex), and generate.

Advanced options include self-signed certs for enterprise, custom base URLs for private APIs, and authorization mode (DEEPWIKI_AUTH_MODE=true) to restrict access with a secret code.

Technical Architecture

The project is split into backend (FastAPI in Python) and frontend (Next.js with React). Backend handles cloning, embedding creation (via vector stores), RAG pipelines, and LLM integrations. Frontend provides the UI for repo input, token auth, model selection, and interactive features like chat and diagrams (rendered with Mermaid). Logging (via Python's logging module) aids debugging, with configurable levels and file paths.

Use Cases and Benefits

• Solo Developers: Quickly document personal projects to attract contributors.
• Teams: Standardize docs for onboarding and maintenance.
• Open Source Maintainers: Enhance repo discoverability with AI-generated wikis.
• Enterprises: Deploy on-premises with Ollama for data privacy; integrate with CI/CD for auto-updates.

With 12k+ GitHub stars, DeepWiki-Open has gained traction for its ease, extensibility, and cost-efficiency—especially when pairing free local models with cloud options. It's MIT-licensed, encouraging community contributions for new providers or features.

In summary, DeepWiki-Open democratizes high-quality documentation, saving hours while delivering insightful, visual, and interactive results tailored to your codebase.