OpenDeepWiki is an open-source project inspired by DeepWiki, developed using .NET 9 and Semantic Kernel. It aims to help developers better understand and utilize codebases by providing features such as code analysis, documentation generation, and knowledge graph creation.
- Analyze code structure
- Understand core concepts of repositories
- Generate code documentation
- Automatically create README.md for code MCP Support
OpenDeepWiki supports MCP (Model Context Protocol)
- Supports providing an MCPServer for a single repository and conducting analysis on a single repository.
Usage: The following is the usage of cursor:
{
"mcpServers": {
"OpenDeepWiki":{
"url": "http://Your OpenDeepWiki service IP:port/sse?owner=AIDotNet&name=OpenDeepWiki"
}
}
}- owner: It is the name of the organization or owner of the repository.
- name: It is the name of the repository.
After adding the repository, test by asking a question (please note that before doing this, the repository must be processed first): What is OpenDeepWiki? The effect is as shown in the picture: !
In this way, you can use OpenDeepWiki as an MCPServer, making it available for other AI models to call upon, facilitating the analysis and understanding of an open-source project.
- Quick Generation: Convert all code repositories from Github, Gitlab, Gitee, Gitea, etc., into a knowledge base in just a few minutes.
- Multi-language Support: Supports code analysis and documentation generation for all programming languages.
- Code Structure: Automatically generate Mermaid charts to understand code structure.
- Custom Models: Supports custom models and custom APIs for extension as needed.
- AI Intelligent Analysis: AI-based code analysis and understanding of code relationships.
- Easy SEO: Generate SEO-friendly documents and knowledge bases using Next.js for better search engine indexing.
- Conversational Interaction: Support for conversational interaction with AI to obtain detailed information and usage methods for code, enabling deeper understanding.
- Clone the repository
git clone https://github.com/AIDotNet/OpenDeepWiki.git
cd OpenDeepWiki- Open the
docker-compose.ymlfile and modify the following environment variables:
OpenAI:
services:
koalawiki:
environment:
- KOALAWIKI_REPOSITORIES=/repositories
- TASK_MAX_SIZE_PER_USER=5 # Maximum number of parallel document generation tasks per user by AI
- CHAT_MODEL=DeepSeek-V3 # Model must support functions
- ANALYSIS_MODEL= # Analysis model used for generating repository directory structure
- CHAT_API_KEY= # Your API key
- LANGUAGE= # Set the default language for generation as "Chinese"
- ENDPOINT=https://api.token-ai.cn/v1
- DB_TYPE=sqlite
- MODEL_PROVIDER=OpenAI # Model provider, default is OpenAI, supports AzureOpenAI and Anthropic
- DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db
- EnableSmartFilter=true # Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repositoryAzureOpenAI:
services:
koalawiki:
environment:
- KOALAWIKI_REPOSITORIES=/repositories
- TASK_MAX_SIZE_PER_USER=5 # Maximum number of parallel document generation tasks per user by AI
- CHAT_MODEL=DeepSeek-V3 # Model must support functions
- ANALYSIS_MODEL= # Analysis model used for generating repository directory structure
- CHAT_API_KEY= # Your API key
- LANGUAGE= # Set the default language for generation as "Chinese"
- ENDPOINT=https://your-azure-address.openai.azure.com/
- DB_TYPE=sqlite
- MODEL_PROVIDER=AzureOpenAI # Model provider, default is OpenAI, supports AzureOpenAI and Anthropic
- DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db
- EnableSmartFilter=true # Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repositoryAnthropic:
services:
koalawiki:
environment:
- KOALAWIKI_REPOSITORIES=/repositories
- TASK_MAX_SIZE_PER_USER=5 # Maximum number of parallel document generation tasks per user by AI
- CHAT_MODEL=DeepSeek-V3 # Model must support functions
- ANALYSIS_MODEL= # Analysis model used for generating repository directory structure
- CHAT_API_KEY= # Your API key
- LANGUAGE= # Set the default language for generation as "Chinese"
- ENDPOINT=https://api.anthropic.com/
- DB_TYPE=sqlite
- MODEL_PROVIDER=Anthropic # Model provider, default is OpenAI, supports AzureOpenAI and Anthropic
- DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db
- EnableSmartFilter=true # Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repositoryπ‘ How to get an API Key:
- Get Google API key Google AI Studio
- Get OpenAI API key OpenAI Platform
- Get CoresHub CoresHub Click here for 50 million free tokens
- Get TokenAI TokenAI
- Start the service
You can use the provided Makefile commands to easily manage the application:
# Build all Docker images
make build
# Start all services in background mode
make up
# Or start in development mode (with logs visible)
make devThen visit http://localhost:8090 to access the knowledge base.
For more commands:
make helpIf you're using Windows and don't have make available, you can use these Docker Compose commands directly:
# Build all Docker images
docker-compose build
# Start all services in background mode
docker-compose up -d
# Start in development mode (with logs visible)
docker-compose up
# Stop all services
docker-compose down
# View logs
docker-compose logs -fFor building specific architectures or services, use:
# Build only backend
docker-compose build koalawiki
# Build only frontend
docker-compose build koalawiki-web
# Build with architecture parameters
docker-compose build --build-arg ARCH=arm64
docker-compose build --build-arg ARCH=amd64OpenDeepWiki uses AI to:
- Clone code repository locally
- Analyze based on repository README.md
- Analyze code structure and read code files as needed, then generate directory json data
- Process tasks according to directory, each task is a document
- Read code files, analyze code files, generate code documentation, and create Mermaid charts representing code structure dependencies
- Generate the final knowledge base document
- Analyze repository through conversational interaction and respond to user inquiries
graph TD
A[Clone code repository] --> B[Analyze README.md]
B --> C[Analyze code structure]
C --> D[Generate directory json data]
D --> E[Process multiple tasks]
E --> F[Read code files]
F --> G[Analyze code files]
G --> H[Generate code documentation]
H --> I[Create Mermaid charts]
I --> J[Generate knowledge base document]
J --> K[Conversational interaction]
- KOALAWIKI_REPOSITORIES Path for storing repositories
- TASK_MAX_SIZE_PER_USER Maximum parallel tasks for AI document generation per user
- CHAT_MODEL Model must support functions
- ENDPOINT API Endpoint
- ANALYSIS_MODEL Analysis model for generating repository directory structure
- CHAT_API_KEY Your API key
- LANGUAGE Change the language of the generated documents
- DB_TYPE Database type, default is sqlite
- MODEL_PROVIDER Model provider, by default OpenAI, supports Azure, OpenAI and Anthropic
- DB_CONNECTION_STRING Database connection string
- EnableSmartFilter Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repository
The Makefile provides commands to build for different CPU architectures:
# Build for ARM architecture
make build-arm
# Build for AMD architecture
make build-amd
# Build only backend for ARM
make build-backend-arm
# Build only frontend for AMD
make build-frontend-amdThis project is licensed under the MIT License - see the LICENSE file for details.