Code Context Graph

A multi-language code analysis tool that generates semantic graphs with LLM integration, Content-Addressable Storage (CAS), and real-time file watching capabilities.

🚀 Features

• Multi-language support: Python, Java, Kotlin, JavaScript
• Semantic graph generation: AST-based code relationships
• Real-time updates: File watching with incremental analysis
• Content-Addressable Storage: Efficient deduplication and versioning
• Connascence analysis: Coupling and cohesion metrics
• LLM integration: Optimized context for AI assistants
• AASE framework: Automated context engineering

📋 Requirements

• Rust 1.75+
• FalkorDB (Redis-compatible graph database)

🛠️ Installation

git clone https://github.com/rubentxu/code-context-graph
cd code-context-graph
cargo build --release

🎯 Quick Start

# Analyze a codebase
./target/release/ccg analyze --path /path/to/your/project

# Start real-time watching
./target/release/ccg watch --path /path/to/your/project

# Query the graph
./target/release/ccg query --question "What functions call authenticate?"

# Analyze code quality
./target/release/ccg quality --path /path/to/your/project

# Start API server
./target/release/ccg serve --port 8080

🧩 Visualization

Generate Mermaid-based class diagrams from your codebase (file or full project directory). See full guide in docs/viz.md.

Quick examples:

# Project-wide class diagram (Markdown/mermaid)
./target/release/ccg viz class --path examples/java --out /tmp/java_project.md --format md

# Project-wide HTML (render-ready with Mermaid.js)
./target/release/ccg viz class --path examples/python --out /tmp/python_project.html --format html

# Filter by class names
./target/release/ccg viz class --path . --out /tmp/filtered.md --filter-class User,UserService

🐳 FalkorDB + Dashboard (Docker Compose)

Arranca un entorno local con FalkorDB y RedisInsight (dashboard) usando Docker Compose:

# Levantar servicios (FalkorDB en 6379, RedisInsight UI en 5540)
make docker-up

# Ver logs
make docker-logs

# Parar servicios
make docker-down

Accede a RedisInsight en http://localhost:5540 y añade la base de datos:

• Host: 127.0.0.1
• Port: 6379
• Name: falkordb

Configura tu app/tests para apuntar a esta instancia:

export FALKORDB_URL=redis://127.0.0.1:6379

Si ya tienes un Redis local en 6379, modifica el mapeo de puertos en docker/docker-compose.yml (por ejemplo, "6380:6379") y ajusta FALKORDB_URL.

📁 Project Structure

code-context-graph/
├── crates/
│   ├── core/           # Core types and domain logic
│   ├── parser/         # Tree-sitter parsing
│   ├── graph/          # Graph operations (FalkorDB)
│   ├── storage/        # CAS + Merkle tree storage
│   ├── watcher/        # File system monitoring
│   ├── api/            # REST API server
│   ├── connascence/    # Coupling analysis
│   ├── aase/           # Context engineering
│   └── cli/            # Command-line interface
├── config.toml         # Default configuration
└── docs/               # Documentation

⚙️ Configuration

Create a config.toml file or use the default configuration:

[engine]
name = "my-project"
languages = ["python", "javascript", "java", "kotlin"]

[falkordb]
url = "redis://localhost:6379"
graph_name = "code_graph"

[cas]
enabled = true
storage_path = "./cas_store"

[file_watcher]
enabled = true
debounce_ms = 100

🔧 Development

# Run tests
cargo test

# Run with logging
RUST_LOG=debug cargo run -- analyze --path ./examples/python

# Check formatting
cargo fmt --check

# Run clippy
cargo clippy

📊 Architecture

The system follows Hexagonal Architecture with clear separation between:

• Domain Layer: Core types and business logic
• Application Layer: Use cases and orchestration
• Infrastructure Layer: Database, file system, external services

🧪 Testing

# Unit tests
cargo test --lib

# Integration tests  
cargo test --test integration

# Property-based tests
cargo test proptest

📈 Performance

• Analysis: <10s for 500 files
• Real-time updates: <100ms for file changes
• Storage efficiency: >85% deduplication with CAS
• Memory usage: <2GB for 500k LOC projects

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Follow conventional commits
4. Add tests for new functionality
5. Submit a pull request

📄 License

MIT OR Apache-2.0

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Status: 🚧 Under Development

This is the initial structure implementation. Core functionality is being developed incrementally following the roadmap in the PRD.