DeepWiki - Technical Overview

Overview

DeepWiki is an AI-powered documentation platform that automatically converts code repositories into comprehensive, interactive wiki-style documentation. Originally launched by Cognition AI (creators of Devin) on April 25, 2025, the platform now has multiple open-source implementations available for self-hosting.

The ecosystem includes:

DeepWiki (Cognition AI): The original hosted service at deepwiki.com
DeepWiki-Open (AsyncFuncAI): Python/FastAPI + Next.js open-source implementation
OpenDeepWiki (AIDotNet): .NET 9 + Semantic Kernel open-source implementation

High-Level Architecture

How It Works

Processing Pipeline

Key Concepts

RAG (Retrieval Augmented Generation)

The core of DeepWiki's Q&A capability. When users ask questions:

Query is converted to embedding vector
Similar code chunks retrieved from vector database
Retrieved context added to LLM prompt
LLM generates answer grounded in actual code

Vector Embeddings

Code is split into overlapping chunks and converted to high-dimensional vectors that capture semantic meaning. This enables:

Semantic search (find conceptually related code)
Context retrieval for Q&A
Code relationship mapping

Multi-Provider Architecture

All implementations support multiple LLM and embedding providers:

Swap between OpenAI, Google, Anthropic, Ollama without code changes
Configure via JSON or environment variables
Support for OpenAI-compatible APIs (Alibaba Qwen, Azure, etc.)

Ecosystem Comparison

Technical Details

DeepWiki-Open (AsyncFuncAI) Stack

Component	Technology
Backend	Python 3.11+, FastAPI
Frontend	Next.js, React, TypeScript
RAG Framework	AdalFlow
Vector Store	FAISS
Diagrams	Mermaid
Container	Docker, Docker Compose

Supported LLM Providers:

Google Gemini (default: gemini-2.5-flash)
OpenAI (gpt-5-nano, gpt-4o)
OpenRouter (Claude, Llama, Mistral)
Azure OpenAI
Ollama (local models)

Embedding Options:

OpenAI: text-embedding-3-small
Google AI: text-embedding-004
Ollama: Local embeddings

OpenDeepWiki (AIDotNet) Stack

Component	Technology
Backend	C# .NET 9, Semantic Kernel
Frontend	TypeScript, Next.js
Databases	SQLite, PostgreSQL, MySQL, SQL Server
Git	LibGit2Sharp
Container	Docker (ARM64/AMD64)

Supported AI Providers:

OpenAI
Azure OpenAI
Anthropic Claude
DeepSeek
Custom API endpoints

Provider Architecture

Configuration System

DeepWiki-Open Configuration Files

Key Environment Variables

DeepWiki-Open:

bash

GOOGLE_API_KEY          # For Gemini models
OPENAI_API_KEY          # For OpenAI models
DEEPWIKI_EMBEDDER_TYPE  # 'openai', 'google', or 'ollama'
OLLAMA_HOST             # Default: http://localhost:11434
PORT                    # API server port (default: 8001)

OpenDeepWiki:

bash

CHAT_MODEL              # Model for chat interactions
ANALYSIS_MODEL          # Model for code analysis
DEEP_RESEARCH_MODEL     # Model for deep analysis
DB_TYPE                 # sqlite, postgresql, mysql, sqlserver
DB_CONNECTION_STRING    # Database connection
AUTO_CONTEXT_COMPRESS   # Enable 90%+ token reduction

Key Facts (2025)

50,000+ public repositories indexed by Cognition AI's DeepWiki
4 billion+ lines of code processed
$300,000+ compute costs for initial indexing
12,900+ GitHub stars for DeepWiki-Open
2,500+ GitHub stars for OpenDeepWiki
Free for public repositories (hosted version)
MIT License for both open-source implementations
MCP Server available for programmatic access

Use Cases

For Developers

Onboarding: Quickly understand unfamiliar codebases
Documentation: Auto-generate docs for undocumented projects
Code Review: Get AI analysis of code structure and patterns
Bug Finding: Use DeepResearch for potential issue detection

For Teams

Private Repos: Self-host for proprietary code analysis
Knowledge Base: Convert internal repos into searchable wikis
Technical Writing: Bootstrap documentation from code

For Open Source

Community: Make projects more accessible to contributors
Discovery: Help users understand project architecture
Comparison: Analyze how different projects solve similar problems

Integration Options

MCP Server Tools (Cognition AI)

ask_question: Query any indexed repository
read_wiki_structure: Get documentation structure
read_wiki_contents: Read specific wiki pages

Security Considerations

Self-Hosting Benefits

Privacy: Code never leaves your infrastructure
Control: Full control over data retention and access
Compliance: Meet regulatory requirements for sensitive code

API Key Management

Use environment variables, never hardcode keys
Support for Azure Key Vault and similar services
Role-based access for team deployments

Data Handling

Repository clones stored in configurable locations
Embeddings cached locally (~/.adalflow/)
Option to clear cache after processing

Quick Start Comparison

DeepWiki (Hosted)

# Just change the URL
https://github.com/user/repo
→
https://deepwiki.com/user/repo

DeepWiki-Open (Docker)

bash

git clone https://github.com/AsyncFuncAI/deepwiki-open
cd deepwiki-open
cp .env.example .env
# Configure API keys in .env
docker-compose up
# Access at http://localhost:3000

OpenDeepWiki (Docker)

bash

git clone https://github.com/AIDotNet/OpenDeepWiki
cd OpenDeepWiki
# Configure docker-compose.yml
make build && make up
# Access at http://localhost:3000

DeepWiki - Technical Overview ​

Overview ​

High-Level Architecture ​

How It Works ​

Processing Pipeline ​

Key Concepts ​

RAG (Retrieval Augmented Generation) ​

Vector Embeddings ​

Multi-Provider Architecture ​

Ecosystem Comparison ​

Technical Details ​

DeepWiki-Open (AsyncFuncAI) Stack ​

OpenDeepWiki (AIDotNet) Stack ​

Provider Architecture ​

Configuration System ​

DeepWiki-Open Configuration Files ​

Key Environment Variables ​

Key Facts (2025) ​

Use Cases ​

For Developers ​

For Teams ​

For Open Source ​

Integration Options ​

MCP Server Tools (Cognition AI) ​

Security Considerations ​

Self-Hosting Benefits ​

API Key Management ​

Data Handling ​

Quick Start Comparison ​

DeepWiki (Hosted) ​

DeepWiki-Open (Docker) ​

OpenDeepWiki (Docker) ​

Sources ​

DeepWiki - Technical Overview

Overview

High-Level Architecture

How It Works

Processing Pipeline

Key Concepts

RAG (Retrieval Augmented Generation)

Vector Embeddings

Multi-Provider Architecture

Ecosystem Comparison

Technical Details

DeepWiki-Open (AsyncFuncAI) Stack

OpenDeepWiki (AIDotNet) Stack

Provider Architecture

Configuration System

DeepWiki-Open Configuration Files

Key Environment Variables

Key Facts (2025)

Use Cases

For Developers

For Teams

For Open Source

Integration Options

MCP Server Tools (Cognition AI)

Security Considerations

Self-Hosting Benefits

API Key Management

Data Handling

Quick Start Comparison

DeepWiki (Hosted)

DeepWiki-Open (Docker)

OpenDeepWiki (Docker)

Sources