OpenDeepWiki

AI-Driven Code Knowledge Base

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Function

Quick Conversion: All Github, Gitlab, Gitee, Gitea and other code repositories can be converted into knowledge bases in just a few minutes.
Multi-language Support: Code analysis and documentation generation are supported for all programming languages.
Code Structure: Automatic Mermaid diagrams are generated to understand the code structure.
Custom Models: Custom models and custom APIs are supported, allowing for expansion as needed.
AI Intelligent Analysis: Code analysis and understanding of code relationships based on AI.
Easy SEO: Generate SEO-friendly documents and knowledge bases using Next.js, making it easier for search engines to index.
Dialogic Interaction: Supports dialogic interaction with AI to obtain detailed information and usage methods of the code, and to deeply understand the code.

Feature list:

[x] Supports multiple code repositories (Github, Gitlab, Gitee, Gitea, etc.)
[x] Supports multiple programming languages (Python, Java, C#, JavaScript, etc.)
[x] Supports repository management, providing functions for adding, deleting, modifying, and querying repositories
[x] Supports multiple AI providers (OpenAI, AzureOpenAI, Anthropic, etc.)
[x] Supports multiple databases (SQLite, PostgreSQL, SqlServer, etc.)
[x] Supports multiple languages (Chinese, English, French, etc.)
[x] Supports uploading ZIP files, and uploading local files
[x] provides a data fine-tuning platform to generate fine-tuning datasets
[x] Supports directory-level management of repositories, allowing for custom directory generation and dynamic documentation creation
[x] Supports repository directory management, allowing for modification of repository directories
[x] Supports user-level management, providing user management functions for adding, deleting, modifying, and querying users
[ ] Supports user permission management, providing user permission management functions for adding, deleting, modifying, and querying user permissions
[x] Supports generating different fine-tuning framework datasets at the repository level

Project Introduction

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 Start

Clone the repository

git clone https://github.com/AIDotNet/OpenDeepWiki.git
cd OpenDeepWiki

Open the docker-compose.yml file and modify the following environment variables:

Ollama:

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=qwen2.5:32b # Model must support functions
      ANALYSIS_MODEL=qwen2.5:32b # Analysis model used for generating repository directory structure
      CHAT_API_KEY=sk-xxxxx # Your API key
      LANGUAGE= # Set the default language for generation as "Chinese"
      ENDPOINT=https://Your Ollama's IP: Port/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 repository
      UPDATE_INTERVAL # Warehouse increment update interval, unit: days
      MAX_FILE_LIMIT=100 # The maximum limit for uploading files, in MB
      DEEP_RESEARCH_MODEL= # Conduct in-depth research on the model and use CHAT_MODEL for the empty
      ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates 
      ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis,This might have an impact on the quality of the code.
      ENABLE_WAREHOUSE_FUNCTION_PROMPT_TASK=false # Whether to enable MCP Prompt generation or not.
      ENABLE_WAREHOUSE_DESCRIPTION_TASK=false # Whether to enable the generation of warehouse Description

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 repository
      UPDATE_INTERVAL # Warehouse increment update interval, unit: days
      MAX_FILE_LIMIT=100 # The maximum limit for uploading files, in MB
      DEEP_RESEARCH_MODEL= # Conduct in-depth research on the model and use CHAT_MODEL for the empty
      ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates 
      ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis,This might have an impact on the quality of the code.
      ENABLE_WAREHOUSE_FUNCTION_PROMPT_TASK=false # Whether to enable MCP Prompt generation or not.
      ENABLE_WAREHOUSE_DESCRIPTION_TASK=false # Whether to enable the generation of warehouse Description

AzureOpenAI:

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 repository
      UPDATE_INTERVAL # Warehouse increment update interval, unit: days
      MAX_FILE_LIMIT=100 # The maximum limit for uploading files, in MB
      DEEP_RESEARCH_MODEL= # Conduct in-depth research on the model and use CHAT_MODEL for the empty
      ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates
      ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis,This might have an impact on the quality of the code.
      ENABLE_WAREHOUSE_FUNCTION_PROMPT_TASK=false # Whether to enable MCP Prompt generation or not.
      ENABLE_WAREHOUSE_DESCRIPTION_TASK=false # Whether to enable the generation of warehouse Description

Anthropic:

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 repository
      UPDATE_INTERVAL # Warehouse increment update interval, unit: days
      MAX_FILE_LIMIT=100 # The maximum limit for uploading files, in MB
      DEEP_RESEARCH_MODEL= # Conduct in-depth research on the model and use CHAT_MODEL for the empty
      ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates
      ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis,This might have an impact on the quality of the code.
      ENABLE_WAREHOUSE_FUNCTION_PROMPT_TASK=false # Whether to enable MCP Prompt generation or not.
      ENABLE_WAREHOUSE_DESCRIPTION_TASK=false # Whether to enable the generation of warehouse Description

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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 upOr start in development mode (with logs visible)

make dev

Then visit http://localhost:8090 to access the knowledge base.

For more commands:

bash make help

For Windows Users (without make)

If you're using Windows and don't have make available, you can use these Docker Compose commands directly:

bash

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 -f


For building specific architectures or services, use:

bash

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=amd64


Deploy to Sealos with Public Internet Access

For detailed steps, refer to: One-Click Deployment of OpenDeepWiki as a Sealos Application Exposed to the Public Network Using Templates
🔍 How It Works
OpenDeepWiki 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

mermaid 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]

## Advanced Configuration
Environment Variables
  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
  UPDATE_INTERVAL Warehouse increment update interval, unit: days
  MAX_FILE_LIMIT The maximum limit for uploading files, in MB
  DEEP_RESEARCH_MODEL Conduct in-depth research on the model and use CHAT_MODEL for the empty
  ENABLE_INCREMENTAL_UPDATE Whether to enable incremental updates
  ENABLE_CODED_DEPENDENCY_ANALYSIS Whether to enable code dependency analysis,This might have an impact on the quality of the code.
  ENABLE_WAREHOUSE_FUNCTION_PROMPT_TASK  # Whether to enable MCP Prompt generation or not.
  ENABLE_WAREHOUSE_DESCRIPTION_TASK # Whether to enable the generation of warehouse Description
Build for Different Architectures
The Makefile provides commands to build for different CPU architectures:

bash

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-amd ```

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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--- Tranlated By Open Ai Tx | Last indexed: 2025-06-11 ---