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Code Graph Knowledge System

Enterprise Knowledge Management Platform

Neo4j-powered graph database with multi-interface architecture (MCP/Web/REST) and intelligent code analysis

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What is Code Graph Knowledge System?

Code Graph Knowledge System is an enterprise-grade solution that transforms unstructured development documentation and code into a structured, queryable knowledge graph. By combining vector search, graph database technology, and large language models, it provides intelligent code analysis, documentation management, and development assistance capabilities.

✨ Key Features

🎯 Deployment Modes

Choose the right deployment mode for your needs:

Feature Minimal Standard Full
Code Graph
└ Repository ingestion ✅ ✅ ✅
└ File relationship search ✅ ✅ ✅
└ Impact analysis ✅ ✅ ✅
└ Context packing ✅ ✅ ✅
Memory Store
└ Manual management ❌ ✅ ✅
└ Vector search ❌ ✅ ✅
└ Auto extraction (Git) ❌ ❌ ✅
└ Auto extraction (Conversations) ❌ ❌ ✅
Knowledge RAG
└ Document vectorization ❌ ❌ ✅
└ Intelligent Q&A ❌ ❌ ✅
Requirements
└ Neo4j ✅ ✅ ✅
└ Embedding Model ❌ ✅ ✅
└ LLM ❌ ❌ ✅
Image Size ~500MB ~600MB ~800MB
Startup Time ~5s ~8s ~15s

🚀 Core Capabilities

No LLM Required - Pure graph-based code intelligence

  • Repository Ingestion: Parse and index entire codebases
  • Relationship Discovery: Find file dependencies and imports
  • Impact Analysis: Understand the blast radius of changes
  • Context Packing: Generate AI-friendly context bundles
# Start minimal deployment
make docker-minimal

Embedding Required - Long-term project knowledge

  • Manual Memory Management: Add, search, update memories
  • Vector Search: Find relevant project decisions
  • Auto Extraction: Extract from git commits and conversations
  • Knowledge Evolution: Track decision changes over time
# Start standard deployment
make docker-standard

LLM + Embedding Required - Full AI capabilities

  • Document Processing: Index documentation and code
  • Intelligent Q&A: Answer questions about your codebase
  • Multi-format Support: Markdown, PDF, code files
  • Hybrid Search: Combine vector and graph traversal
# Start full deployment
make docker-full

🎯 Quick Start

1. Choose Your Deployment Mode

# Minimal - Code Graph only (No LLM needed)
make docker-minimal

# Standard - Code Graph + Memory (Embedding needed)
make docker-standard

# Full - All features (LLM + Embedding needed)
make docker-full

2. Access the System

3. Ingest Your Repository

// In Claude Desktop, use MCP tools:
code_graph_ingest_repo({
  "local_path": "/path/to/your/repo",
  "mode": "incremental"
})

curl -X POST http://localhost:8000/api/v1/code-graph/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "local_path": "/path/to/your/repo",
    "mode": "incremental"
  }'

🎨 Use Cases

For Individual Developers

  • Learn Large Codebases: Quickly understand unfamiliar projects
  • Code Navigation: Find relationships and dependencies
  • Impact Assessment: See what breaks before making changes

For Development Teams

  • Project Knowledge Base: Preserve team decisions and context
  • Onboarding: Help new team members get up to speed
  • Documentation: Auto-generate context for AI coding assistants

For AI-Assisted Development

  • Claude Desktop Integration: Use as MCP server for enhanced code understanding
  • VS Code Integration: Access code graph directly in your editor
  • Context Generation: Create optimal context for LLM queries

📚 Documentation

🌟 Why Code Graph Knowledge System?

🎯 Flexible Architecture

  • No Vendor Lock-in: Use Ollama, OpenAI, Gemini, or any LLM
  • Scalable: From single developer to enterprise teams
  • Modular: Only use what you need

🚀 Performance Optimized

  • Incremental Updates: 60x faster than full re-indexing
  • Smart Caching: Reduce redundant processing
  • Efficient Storage: Neo4j native vector indexes

🔒 Privacy Focused

  • Self-Hosted: Keep your code on your infrastructure
  • No Data Leaks: Optional local LLM support
  • Secure: Enterprise-grade Neo4j backend

🛠️ Technology Stack

  • Backend: Python 3.13, FastAPI
  • Database: Neo4j 5.15+ with APOC
  • AI: LlamaIndex, Multiple LLM providers
  • Protocol: Model Context Protocol (MCP)
  • Deployment: Docker, Docker Compose

🤝 Community

📝 License

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

**Ready to get started?** → [Quick Start Guide](getting-started/quickstart.md)