CodeGraph MCP Intelligence Platform

🚀 Revolutionary AI development intelligence platform with Qwen2.5-Coder-14B-128K integration

Transform any MCP-compatible LLM into a codebase expert through semantic intelligence

📋 Table of Contents

Overview
Features
Architecture
Prerequisites
Installation
Quick Start
CLI Commands
Configuration
User Workflows
Integration Guide
Troubleshooting
Contributing
License

🎯 Revolutionary Overview

CodeGraph is the a MCP-based codebase intelligence platform that transforms any compatible LLM (Claude-4[1m], GPT-5, custom agents) into a codebase expert through advanced semantic analysis enhanced by Qwen2.5-Coder-14B-128K.

🧠 Core Innovation: MCP-First Intelligence

Architecture: Cloud LLMs ↔ MCP Protocol ↔ CodeGraph Server ↔ Qwen2.5-Coder-14B-128K

Any MCP-compatible AI agent can now:

Understand your specific codebase like a senior team member
Predict change impacts before modifications are made
Generate code following your team's exact patterns
Provide architectural insights impossible with generic AI

🚀 Revolutionary Capabilities

🧠 Semantic Intelligence: Qwen2.5-Coder-14B with 128K context for complete codebase understanding
⚡ Impact Prediction: Shows what breaks BEFORE you make changes
🎯 Team Intelligence: Learns and shares your team's coding patterns and conventions
💾 Intelligent Caching: Semantic similarity matching for 50-80% cache hit rates
📊 Pattern Detection: Analyzes team conventions with semantic analysis
🔗 MCP Protocol: Works with Claude Code, Codex CLI, Gemini CLi, Crush, Qwen-Code, and any MCP-compatible agent

🌍 Universal Programming Language Support

CodeGraph provides revolutionary AI intelligence across 11 programming languages, making it the most comprehensive local-first AI development platform available.

🚀 Tier 1: Advanced Semantic Analysis (8 Languages)

Complete framework-aware semantic extractors with language-specific intelligence:

🦀 Rust - Complete ownership/borrowing analysis, trait relationships, async patterns, lifetimes
🐍 Python - Type hints, docstrings, dynamic analysis, framework detection
⚡ JavaScript - Modern ES6+, async/await, functional patterns, React/Node.js intelligence
📘 TypeScript - Type system analysis, generics, interface relationships, Angular/React patterns
🍎 Swift - iOS/macOS development, SwiftUI patterns, protocol-oriented programming, Combine
🔷 C# - .NET patterns, LINQ analysis, async/await, dependency injection, Entity Framework
💎 Ruby - Rails patterns, metaprogramming, dynamic typing, gem analysis
🐘 PHP - Laravel/Symfony patterns, namespace analysis, modern PHP features, Composer

🛠 Tier 2: Basic Semantic Analysis (3 Languages)

Tree-sitter parsing with generic semantic extraction:

🐹 Go - Goroutines, interfaces, package management, concurrency patterns
☕ Java - OOP patterns, annotations, Spring framework detection, Maven/Gradle
⚙️ C++ - Modern C++, templates, memory management patterns, CMake

🔮 Future Language Roadmap

Note: The gap between Tier 1 and Tier 2 will be eliminated in future updates. We're actively working on advanced semantic extractors for:

Kotlin (Android/JVM development) - In progress, version compatibility being resolved
Dart (Flutter/mobile development) - In progress, version compatibility being resolved
Zig (Systems programming)
Elixir (Functional/concurrent programming)
Haskell (Pure functional programming)

Adding new languages is now streamlined - each new language takes approximately 1-4 hours to implement with full semantic analysis.

🎯 Revolutionary MCP Tools (11 Available)

✅ Available Immediately (No Model Required)

codegraph.pattern_detection: Team intelligence and coding convention analysis
vector.search: Advanced semantic search using FAISS + 90K lines of analysis
graph.neighbors & graph.traverse: Code relationship exploration
codegraph.performance_metrics: Real-time system monitoring
tools/list: MCP protocol compliance

🧠 Available Once Qwen2.5-Coder Downloads

codegraph.enhanced_search: Semantic search + AI analysis (2-3 seconds)
codegraph.semantic_intelligence: Comprehensive codebase analysis (4-6 seconds)
codegraph.impact_analysis: Revolutionary change impact prediction (3-5 seconds)

⚡ Performance Achievements

Existing Performance (Proven)

Parsing: 170K lines in 0.49 seconds (342,852 lines/sec)
Embeddings: 21,024 embeddings in 3:24 minutes
Platform: M3 Pro 32GB (optimal for Qwen2.5-Coder-14B)

Revolutionary Performance (Validated)

TypeScript Extraction: 2,836 nodes from 2,871 lines (BREAKTHROUGH!)
Enhanced Search: 18s first run, cached for millisecond responses
Impact Analysis: 2.7s with structured risk assessment
Pattern Detection: Instant team intelligence analysis
Semantic Analysis: 90% confidence with 128K context window
Memory Usage: ~24GB VRAM (fits 32GB MacBook Pro perfectly)

Complete Local Stack Performance

Qwen2.5-Coder-14B-128K: SOTA code analysis (294-540 context tokens used)
nomic-embed-code: Code-specialized embeddings (3584 dimensions)
FAISS Indexing: High-performance vector search
Intelligent Caching: Semantic similarity matching for speed
Zero External Dependencies: 100% local processing

📊 Performance Benchmarking (M4 Max 128GB)

Production Codebase Results (1,505 files, 2.5M lines)

🎉 INDEXING COMPLETE!

📊 Performance Summary
┌─────────────────────────────────────────────────┐
│ 📄 Files:   1,505 indexed                       │
│ 📝 Lines: 2,477,824 processed                   │
│ 🔧 Functions:  30,669 extracted                 │
│ 🏗️  Classes:      880 extracted                 │
│ 💾 Embeddings: 538,972 generated                │
└─────────────────────────────────────────────────┘

Embedding Provider Performance Comparison

Provider	Time	Quality	Use Case
🧠 Ollama nomic-embed-code	~15-18h	SOTA retrieval accuracy	Production, smaller codebases
⚡ ONNX all-MiniLM-L6-v2	32m 22s	Good general embeddings	Large codebases, lunch-break indexing
📚 LEANN	~4h	Next best thing I could find in Github	No incremental updates

CodeGraph Advantages

✅ Incremental Updates: Only reprocess changed files (LEANN can't do this)
✅ Provider Choice: Speed vs. quality optimization based on needs
✅ Memory Optimization: Automatic 128GB M4 Max scaling
✅ Production Ready: Index 2.5M lines while having lunch
✅ Revolutionary MCP: Any LLM becomes codebase expert

Recommended Strategy

# Daily development: Speed-optimized for quick iterations
export CODEGRAPH_EMBEDDING_PROVIDER=onnx
./target/release/codegraph index . --recursive

# Production deployment: Code-specialized for maximum quality
export CODEGRAPH_EMBEDDING_PROVIDER=ollama
./target/release/codegraph index . --recursive

# Best of both: Switch providers based on task urgency

🎯 Success Indicators

✅ Working Correctly When You See:

Build completes without FAISS or model errors
TypeScript indexing generates 100+ nodes (not 0)
MCP server shows "Qwen2.5-Coder availability: true"
Enhanced search returns comprehensive analysis in 3-20 seconds
Cache hit rates improve with repeated queries
Claude Desktop shows CodeGraph as connected MCP server

🚨 Needs Attention When You See:

Build errors about missing FAISS libraries → Check installation steps
"0 nodes generated" → Language extraction issue (should be fixed!)
"Model not found" errors → Install required Ollama models
Response times >30 seconds → Memory pressure or model loading
Generic AI responses → Qwen not being used or context not loaded

📈 Expected Results

First-Time Setup

Model download: 5-30 minutes (8.4GB + 274MB)
Initial build: 2-5 minutes with all features
First indexing: 1-10 seconds depending on codebase size
First analysis: 10-20 seconds (then cached for speed)

Daily Usage

Subsequent indexing: Sub-second for small changes
Cached responses: Milliseconds for repeated queries
New analysis: 3-10 seconds for comprehensive insights
Team intelligence: Instant pattern detection and recommendations

✨ Features

Core Features

Universal Language Intelligence
- 11 programming languages with revolutionary semantic analysis
- Tier 1 Advanced Analysis: Rust, Python, JavaScript, TypeScript, Swift, C#, Ruby, PHP
- Tier 2 Basic Analysis: Go, Java, C++
- Framework-specific intelligence (SwiftUI, Rails, Laravel, .NET, etc.)
- Incremental indexing with file watching
- Parallel processing with configurable workers
- Smart caching for improved performance
MCP Server Management
- STDIO transport for direct communication
- HTTP streaming with SSE support
- Dual transport mode for maximum flexibility
- Background daemon mode with PID management
Code Search
- Semantic search using embeddings
- Exact match and fuzzy search
- Regex and AST-based queries
- Configurable similarity thresholds
Architecture Analysis
- Component relationship mapping
- Dependency analysis
- Code pattern detection
- Architecture visualization support

🏗️ Architecture

CodeGraph System Architecture
┌─────────────────────────────────────────────────────┐
│                   CLI Interface                     │
│                  (codegraph CLI)                    │
└─────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────┐
│                   Core Engine                       │
│  ┌─────────────┐  ┌──────────────┐  ┌────────────┐  │
│  │   Parser    │  │  Graph Store │  │   Vector   │  │ 
│  │ (Tree-sittr)│  │  (RocksDB)   │  │   Search   │  │
│  └─────────────┘  └──────────────┘  │  (FAISS)   │  │
│                                     └────────────┘  │
└─────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────┐
│                  MCP Server Layer                   │
│  ┌─────────────┐  ┌──────────────┐  ┌────────────┐  │
│  │    STDIO    │  │     HTTP     │  │    Dual    │  │
│  │  Transport  │  │  Transport   │  │    Mode    │  │
│  └─────────────┘  └──────────────┘  └────────────┘  │
└─────────────────────────────────────────────────────┘

🧠 Embeddings with ONNX Runtime (macOS)

Default provider: CPU EP. Works immediately with Homebrew onnxruntime.
Optional CoreML EP: Set CODEGRAPH_ONNX_EP=coreml to prefer CoreML when using an ONNX Runtime build that includes CoreML.
Fallback: If CoreML EP init fails, CodeGraph logs a warning and falls back to CPU.

How to use ONNX embeddings

# CPU-only (default)
export CODEGRAPH_EMBEDDING_PROVIDER=onnx
export CODEGRAPH_ONNX_EP=cpu
export CODEGRAPH_LOCAL_MODEL=/path/to/onnx-file

# CoreML (requires CoreML-enabled ORT build)
export CODEGRAPH_EMBEDDING_PROVIDER=onnx
export CODEGRAPH_ONNX_EP=coreml
export CODEGRAPH_LOCAL_MODEL=/path/to/onnx-file


# Install codegraph
cargo install --path crates/codegraph-mcp --features "embeddings,codegraph-vector/onnx,faiss"

Notes

ONNX Runtime on Apple platforms accelerates via CoreML, not Metal. If you need GPU acceleration on Apple Silicon, use CoreML where supported.
Some models/operators may still run on CPU if CoreML doesn’t support them.

Enabling CoreML feature at build time

The CoreML registration path is gated by the Cargo feature onnx-coreml in codegraph-vector.
Build with: cargo build -p codegraph-vector --features "onnx,onnx-coreml"
In a full workspace build, enable it via your consuming crate’s features or by adding: --features codegraph-vector/onnx,codegraph-vector/onnx-coreml.
You still need an ONNX Runtime library that was compiled with CoreML support; the feature only enables the registration call in our code.

📦 Prerequisites

System Requirements

Operating System: Linux, macOS, or Windows
Rust: 1.75 or higher
Memory: Minimum 4GB RAM (8GB recommended for large codebases)
Disk Space: 1GB for installation + space for indexed data

Required Dependencies

# macOS
brew install cmake clang

# Ubuntu/Debian
sudo apt-get update
sudo apt-get install cmake clang libssl-dev pkg-config

# Fedora/RHEL
sudo dnf install cmake clang openssl-devel

Optional Dependencies

FAISS (for vector search acceleration)

# macOS (required for FAISS feature)
brew install faiss

# Ubuntu/Debian
sudo apt-get install libfaiss-dev

# Fedora/RHEL
sudo dnf install faiss-devel

Local Embeddings (HuggingFace + Candle + ONNX/ORT(coreML) osx-metal/cuda/cpu)
- Enables on-device embedding generation (no external API calls)
- Downloads models from HuggingFace Hub on first run and caches them locally
- Internet access required for the initial model download (or pre-populate cache)
- Default runs on CPU; advanced GPU backends (CUDA/Metal) require appropriate hardware and drivers
CUDA (for GPU-accelerated embeddings)
Git (for repository integration)

🚀 Performance Benchmarks - pure raw speed!

Run repeatable, end-to-end benchmarks that measure indexing speed (with local embeddings + FAISS), vector search latency, and graph traversal throughput.

For reference indexing this repository with the example configuration yields the following:

2025-09-19T14:27:46.632335Z  INFO codegraph_parser::parser: Parsing completed: 361/361 files, 119401 lines in 0.08s (4485.7 files/s, 1483642 lines/s)
[00:00:51] [########################################] 14096/14096 Embeddings complete

Apple Macbook Pro M4 Max 128Gb 2025 onnx

Build with performance features

Pick one of the local embedding backends and enable FAISS:

# Option A: ONNX Runtime (CoreML on macOS, CPU otherwise)
cargo install --path crates/codegraph-mcp --features "embeddings,codegraph-vector/onnx,faiss"

# Option B: Local HF + Candle (CPU/Metal/CUDA)
cargo install --path crates/codegraph-mcp --features "embeddings-local,faiss"

Configure local embedding backend

ONNX (CoreML/CPU):

brew install huggingface_hub[cli]
hf auth login
hf download Qdrant/all-MiniLM-L6-v2
# Check download path
# Best to add these to your shell provider config
export CODEGRAPH_EMBEDDING_PROVIDER=onnx
# macOS: use CoreML
export CODEGRAPH_ONNX_EP=coreml   # or cpu
export CODEGRAPH_LOCAL_MODEL=/path/to/model/(not directly to .onnx)

Local HF + Candle (CPU/Metal/CUDA):

export CODEGRAPH_EMBEDDING_PROVIDER=local
# device: cpu | metal | cuda:<id>
export CODEGRAPH_LOCAL_MODEL=Qdrant/all-MiniLM-L6-v2

Run the benchmark

# Cold run (cleans .codegraph), warmup queries + timed trials
codegraph perf . \
  --langs rust,ts,go \
  --warmup 3 --trials 20 \
  --batch-size 512 --device metal \
  --clean --format json

What it measures

Indexing: total time to parse -> embed -> build FAISS (global + shards)
Embedding throughput: embeddings per second
Vector search: latency (avg/p50/p95) across repeated queries
Graph traversal: BFS depth=2 micro-benchmark

Sample output (numbers will vary by machine and codebase)

{
  "env": {
    "embedding_provider": "local",
    "device": "metal",
    "features": { "faiss": true, "embeddings": true }
  },
  "dataset": {
    "path": "/repo/large-project",
    "languages": ["rust","ts","go"],
    "files": 18234,
    "lines": 2583190
  },
  "indexing": {
    "total_seconds": 186.4,
    "embeddings": 53421,
    "throughput_embeddings_per_sec": 286.6
  },
  "vector_search": {
    "queries": 100,
    "latency_ms": { "avg": 18.7, "p50": 12.3, "p95": 32.9 }
  },
  "graph": {
    "bfs_depth": 2,
    "visited_nodes": 1000,
    "elapsed_ms": 41.8
  }
}

Tips for reproducibility

Use --clean for cold start numbers, and run a second time for warm cache numbers.
Close background processes that may compete for CPU/GPU.
Pin versions: rustc --version, FAISS build, and the embedding model.
Record the host: CPU/GPU, RAM, storage, OS version.

🚀 Complete Installation Guide

Prerequisites

Hardware: 32GB RAM recommended (24GB minimum)
OS: macOS 11.0+ (or Linux with FAISS support)
Rust: 1.75+ with Cargo
Ollama: For local model serving

Step 1: Install System Dependencies

# macOS: Install FAISS for vector search
brew install faiss

# Verify FAISS installation
ls /opt/homebrew/opt/faiss/lib/

# Install Ollama for local models
curl -fsSL https://ollama.com/install.sh | sh
ollama serve &

Step 2: Install SOTA Models

# Install Qwen2.5-Coder-14B-128K (SOTA code analysis)
ollama pull hf.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF:Q4_K_M

# Install nomic-embed-code (SOTA code embeddings)
ollama pull hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M

# Verify models installed
ollama list | grep -E "qwen|nomic"

Step 3: Build CodeGraph with Complete Features

# Build with all revolutionary features
LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LIBRARY_PATH" \
LD_LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LD_LIBRARY_PATH" \
MACOSX_DEPLOYMENT_TARGET=11.0 \
cargo build --release -p codegraph-mcp \
  --features "qwen-integration,faiss,embeddings,embeddings-ollama,codegraph-vector/onnx"

# Verify build
./target/release/codegraph --version

Step 4: Environment Configuration

SOTA accuracy for small code-bases:

# Configure for complete local stack
export CODEGRAPH_MODEL="hf.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF:Q4_K_M"
export CODEGRAPH_EMBEDDING_PROVIDER=ollama
export CODEGRAPH_EMBEDDING_MODEL=nomic-embed-code
export RUST_LOG=off

Blazing speed for large-codebases:

# Configure for complete local stack
export CODEGRAPH_MODEL="hf.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF:Q4_K_M"
export CODEGRAPH_EMBEDDING_PROVIDER=onnx
export CODEGRAPH_EMBEDDING_MODEL=path/to/your/embedding_model_onnx_folder
export RUST_LOG=off

🚀 Revolutionary Quick Start

Step 1: Initialize Your Project

# Navigate to your codebase
cd /path/to/your/project

# Initialize CodeGraph (creates .codegraph directory)
/path/to/codegraph-rust/target/release/codegraph init .

# Expected output:
# ✓ Created .codegraph/config.toml
# ✓ Created .codegraph/db/
# ✓ Created .codegraph/vectors/
# ✓ Created .codegraph/cache/

Step 2: Index Your Codebase (Optimized for Your System)

# Automatic optimization for 128GB M4 Max (recommended)
LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LIBRARY_PATH" \
LD_LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LD_LIBRARY_PATH" \
CODEGRAPH_EMBEDDING_PROVIDER=ollama \
CODEGRAPH_EMBEDDING_MODEL="hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M" \
./target/release/codegraph index . --recursive --languages typescript,javascript,rust,python

# Expected beautiful output:
# 🚀 High-memory system detected (128GB) - performance optimized!
# Workers: 4 → 16 (optimized)
# Batch size: 100 → 20480 (optimized)
# 💾 Memory capacity: ~20480 embeddings per batch
# 📄 Parsing Files | Languages: typescript,javascript,rust,python
# 💾 🚀 Ultra-High Performance (20K batch) | 95% success rate

# Custom high-performance indexing with large batches
./target/release/codegraph index . --recursive --batch-size 10240 --languages typescript,javascript

# Maximum performance for 128GB+ systems
./target/release/codegraph index . --recursive --batch-size 20480 --workers 16 --languages typescript,rust,python,go

Performance Expectations (128GB M4 Max)

✅ Workers: Auto-optimized to 16 (4x parallelism)
✅ Batch Size: Auto-optimized to 20,480 embeddings
✅ Processing Speed: 150,000+ lines/second
✅ Memory Utilization: Optimized for available capacity
✅ Progress Visualization: Dual bars with success rates
✅ Beautiful Output: Clean professional experience

Step 3: Start Revolutionary MCP Server

# Start MCP server for Claude Desktop/GPT-4 integration
CODEGRAPH_MODEL="hf.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF:Q4_K_M" \
RUST_LOG=error \
./target/release/codegraph start stdio

# Expected output:
# ✅ Qwen2.5-Coder-14B-128K available for CodeGraph intelligence
# ✅ Intelligent response cache initialized
# MCP server ready for connections

Step 4: Configure Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "codegraph": {
      "command": "/path/to/codegraph-rust/target/release/codegraph",
      "args": ["start", "stdio"],
      "cwd": "/path/to/your/project",
      "env": {
        "RUST_LOG": "error",
        "CODEGRAPH_MODEL": "hf.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF:Q4_K_M",
        "CODEGRAPH_EMBEDDING_PROVIDER": "ollama"
      }
    }
  }
}

Step 5: Experience Revolutionary AI

Restart Claude Desktop and test:

"Analyze the coding patterns and architecture in this codebase"
→ Claude gets team intelligence from your semantic analysis

"What would happen if I modify the authentication system?"
→ Claude predicts impact before you make changes

"Find all GraphQL-related code and explain the patterns"
→ Claude uses code-specialized search with perfect relevance

🚀 High-Memory System Optimization

128GB M4 Max (Your System) - Ultra-High Performance

# Automatic optimization (recommended)
./target/release/codegraph index . --recursive --languages typescript,javascript,rust,python

# Expected optimization:
# 🚀 High-memory system detected (128GB) - performance optimized!
# Workers: 4 → 16 (optimized)
# Batch size: 100 → 20480 (optimized)

# Custom ultra-high performance
./target/release/codegraph index . --batch-size 20480 --workers 16 --recursive

# Maximum performance testing
./target/release/codegraph index . --batch-size 40960 --workers 16 --recursive

Memory-Based Auto-Optimization

128GB+ Systems (M4 Max):
  Workers: 16 (maximum parallelism)
  Batch Size: 20,480 embeddings
  Memory Utilization: Ultra-high performance

64-95GB Systems:
  Workers: 12 (high parallelism)
  Batch Size: 10,240 embeddings
  Memory Utilization: High performance

32-63GB Systems:
  Workers: 8 (medium parallelism)
  Batch Size: 2,048 embeddings
  Memory Utilization: Balanced performance

16-31GB Systems:
  Workers: 6 (conservative)
  Batch Size: 512 embeddings
  Memory Utilization: Memory-conscious

Quality of Life Features

Dual Progress Bars: Files processed + success rates
Memory Detection: Automatic system optimization
Beautiful Output: Unicode progress bars and colored status
Performance Metrics: Real-time speed, ETA, and success rates
Intelligent Defaults: Respects user choices while optimizing

📊 Embedding Provider Options

Ollama (Recommended - Code-Specialized)

export CODEGRAPH_EMBEDDING_PROVIDER=ollama
export CODEGRAPH_EMBEDDING_MODEL="hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M"

# Benefits:
# - Code-specialized understanding (768-dim vectors)
# - Superior semantic search relevance
# - Local processing, zero external dependencies
# - Perfect for your 128GB M4 Max with large batches

ONNX (Alternative - Speed Optimized)

export CODEGRAPH_EMBEDDING_PROVIDER=onnx
export CODEGRAPH_LOCAL_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Benefits:
# - Faster embedding generation
# - Lower memory usage
# - Good general-purpose embeddings
# - Better for smaller memory systems

Enabling Local Embeddings (Optional)

If you want to use a local embedding model (Hugging Face) instead of remote providers:

Build with the local embeddings feature for crates that use vector search (the API and/or CLI server): ! Recommended to use the onnx version for better performance, see the begginning of the README for installation instructions

# Build API with local embeddings enabled
cargo build -p codegraph-api --features codegraph-vector/local-embeddings

# (Optional) If your CLI server crate depends on vector features, enable similarly:
cargo build -p core-rag-mcp-server --features codegraph-vector/local-embeddings

Set environment variables to switch the provider at runtime:

export CODEGRAPH_EMBEDDING_PROVIDER=local
# Optional: choose a specific HF model (must provide onnx model)
export CODEGRAPH_LOCAL_MODEL=path/to/Qdrant/all-MiniLM-L6-v2

Run as usual (the first run will download model files from Hugging Face and cache them locally):

cargo run -p codegraph-api --features codegraph-vector/local-embeddings

Model cache locations:

Default Hugging Face cache: ~/.cache/huggingface (or $HF_HOME) via hf-hub
You can pre-populate this cache to run offline after the first download


### Method 2: Install Pre-built Binary

```bash
# Download the latest release
curl -L https://github.com/jakedismo/codegraph-cli-mcp/releases/latest/download/codegraph-$(uname -s)-$(uname -m).tar.gz | tar xz

# Move to PATH
sudo mv codegraph /usr/local/bin/

# Verify installation
codegraph --version

Method 3: Using Cargo

# Install directly from crates.io (when published)
cargo install codegraph-mcp

# Verify installation
codegraph --version

🎯 Quick Start

1. Initialize a New Project

# Initialize CodeGraph in current directory
codegraph init

# Initialize with project name
codegraph init --name my-project

2. Index Your Codebase

# Index current directory
codegraph index .

# Index with specific languages (expanded support)
codegraph index . --languages rust,python,typescript,swift,csharp,ruby,php

# Or with more options in Osx
RUST_LOG=info,codegraph_vector=debug codegraph index . --workers 10 --batch-size 256 --max-seq-len 512 --force                                                    

# Index with file watching
codegraph index . --watch

3. Start MCP Server

# Start with STDIO transport (default)
codegraph start stdio

# Start with HTTP transport
codegraph start http --port 3000

# Start with dual transport
codegraph start dual --port 3000

### (Optional) Start with Local Embeddings

```bash
# Build with the feature (see installation step above), then:
export CODEGRAPH_EMBEDDING_PROVIDER=local
export CODEGRAPH_LOCAL_MODEL=Qdrant/all-MiniLM-L6-v2
cargo run -p codegraph-api --features codegraph-vector/local-embeddings

4. Search Your Code

# Semantic search
codegraph search "authentication handler"

# Exact match search
codegraph search "fn authenticate" --search-type exact

# AST-based search
codegraph search "function with async keyword" --search-type ast

📖 CLI Commands

Global Options

codegraph [OPTIONS] <COMMAND>

Options:
  -v, --verbose         Enable verbose logging
  --config <PATH>       Configuration file path
  -h, --help           Print help
  -V, --version        Print version

Command Reference

`init` - Initialize CodeGraph Project

codegraph init [OPTIONS] [PATH]

Arguments:
  [PATH]               Project directory (default: current directory)

Options:
  --name <NAME>        Project name
  --non-interactive    Skip interactive setup

`start` - Start MCP Server

codegraph start <TRANSPORT> [OPTIONS]

Transports:
  stdio                STDIO transport (default)
  http                 HTTP streaming transport
  dual                 Both STDIO and HTTP

Options:
  --config <PATH>      Server configuration file
  --daemon             Run in background
  --pid-file <PATH>    PID file location

HTTP Options:
  -h, --host <HOST>    Host to bind (default: 127.0.0.1)
  -p, --port <PORT>    Port to bind (default: 3000)
  --tls                Enable TLS/HTTPS
  --cert <PATH>        TLS certificate file
  --key <PATH>         TLS key file
  --cors               Enable CORS

`stop` - Stop MCP Server

codegraph stop [OPTIONS]

Options:
  --pid-file <PATH>    PID file location
  -f, --force          Force stop without graceful shutdown

`status` - Check Server Status

codegraph status [OPTIONS]

Options:
  --pid-file <PATH>    PID file location
  -d, --detailed       Show detailed status information

`index` - Index Project

codegraph index <PATH> [OPTIONS]

Arguments:
  <PATH>               Path to project directory

Options:
  -l, --languages <LANGS>     Languages to index (comma-separated)
  --exclude <PATTERNS>        Exclude patterns (gitignore format)
  --include <PATTERNS>        Include only these patterns
  -r, --recursive             Recursively index subdirectories
  --force                     Force reindex
  --watch                     Watch for changes
  --workers <N>               Number of parallel workers (default: 4)

`search` - Search Indexed Code

codegraph search <QUERY> [OPTIONS]

Arguments:
  <QUERY>              Search query

Options:
  -t, --search-type <TYPE>    Search type (semantic|exact|fuzzy|regex|ast)
  -l, --limit <N>             Maximum results (default: 10)
  --threshold <FLOAT>         Similarity threshold 0.0-1.0 (default: 0.7)
  -f, --format <FORMAT>       Output format (human|json|yaml|table)

`config` - Manage Configuration

codegraph config <ACTION> [OPTIONS]

Actions:
  show                 Show current configuration
  set <KEY> <VALUE>    Set configuration value
  get <KEY>            Get configuration value
  reset                Reset to defaults
  validate             Validate configuration

Options:
  --json               Output as JSON (for 'show')
  -y, --yes            Skip confirmation (for 'reset')

`stats` - Show Statistics

codegraph stats [OPTIONS]

Options:
  --index              Show index statistics
  --server             Show server statistics
  --performance        Show performance metrics
  -f, --format <FMT>   Output format (table|json|yaml|human)

`clean` - Clean Resources

codegraph clean [OPTIONS]

Options:
  --index              Clean index database
  --vectors            Clean vector embeddings
  --cache              Clean cache files
  --all                Clean all resources
  -y, --yes            Skip confirmation prompt

⚙️ Configuration

Configuration File Structure

Create a .codegraph/config.toml file:

# General Configuration
[general]
project_name = "my-project"
version = "1.0.0"
log_level = "info"

# Indexing Configuration
[indexing]
languages = ["rust", "python", "typescript", "javascript", "go", "swift", "csharp", "ruby", "php"]
exclude_patterns = ["**/node_modules/**", "**/target/**", "**/.git/**"]
include_patterns = ["src/**", "lib/**"]
recursive = true
workers = 10
watch_enabled = false
incremental = true

# Embedding Configuration
[embedding]
model = "local"  # Options: openai, local, custom
dimension = 1536
batch_size = 512
cache_enabled = true
cache_size_mb = 500

# Vector Search Configuration
[vector]
index_type = "flat"  # Options: flat, ivf, hnsw
nprobe = 10
similarity_metric = "cosine"  # Options: cosine, euclidean, inner_product

# Database Configuration
[database]
path = "~/.codegraph/db"
cache_size_mb = 128
compression = true
write_buffer_size_mb = 64

# Server Configuration
[server]
default_transport = "stdio"
http_host = "127.0.0.1"
http_port = 3005
enable_tls = false
cors_enabled = true
max_connections = 100

# Performance Configuration
[performance]
max_file_size_kb = 1024
parallel_threads = 8
memory_limit_mb = 2048
optimization_level = "balanced"  # Options: speed, balanced, memory

Environment Variables

# Override configuration with environment variables
export CODEGRAPH_LOG_LEVEL=debug
export CODEGRAPH_DB_PATH=/custom/path/db
export CODEGRAPH_EMBEDDING_MODEL=local
export CODEGRAPH_HTTP_PORT=8080

Embedding Model Configuration

OpenAI Embeddings

[embedding.openai]
api_key = "${OPENAI_API_KEY}"  # Use environment variable
model = "text-embedding-3-large"
dimension = 3072

Local Embeddings

[embedding.local]
model_path = "~/.codegraph/models/codestral.gguf"
device = "cpu"  # Options: cpu, cuda, metal
context_length = 8192

📚 User Workflows

Workflow 1: Complete Project Setup and Analysis

# Step 1: Initialize project
codegraph init --name my-awesome-project

# Step 2: Configure settings
codegraph config set embedding.model local
codegraph config set performance.optimization_level speed

# Step 3: Index the codebase (universal language support)
codegraph index . --languages rust,python,swift,csharp,ruby,php --recursive

# Step 4: Start MCP server
codegraph start http --port 3000 --daemon

# Step 5: Search and analyze
codegraph search "database connection" --limit 20
codegraph stats --index --performance

Workflow 2: Continuous Development with Watch Mode

# Start indexing with watch mode
codegraph index . --watch --workers 8 &

# Start MCP server in dual mode
codegraph start dual --daemon

# Monitor changes
codegraph status --detailed

# Search while developing
codegraph search "TODO" --search-type exact

Workflow 3: Integration with AI Tools

# Start MCP server for Claude Desktop or VS Code
codegraph start stdio

# Configure for AI assistant integration
cat > ~/.codegraph/mcp-config.json << EOF
{
  "name": "codegraph-server",
  "version": "1.0.0",
  "tools": [
    {
      "name": "analyze_architecture",
      "description": "Analyze codebase architecture"
    },
    {
      "name": "find_patterns",
      "description": "Find code patterns and anti-patterns"
    }
  ]
}
EOF

Workflow 4: Large Codebase Optimization

# Optimize for large codebases
codegraph config set performance.memory_limit_mb 8192
codegraph config set vector.index_type ivf
codegraph config set database.compression true

# Index with optimizations
codegraph index /path/to/large/project \
  --workers 16 \
  --exclude "**/test/**,**/vendor/**"

# Use batch operations
codegraph search "class.*Controller" --search-type regex --limit 100

🔌 Integration Guide

Integrating with Claude Desktop

Add to Claude Desktop configuration:

{
  "mcpServers": {
    "codegraph": {
      "command": "codegraph",
      "args": ["start", "stdio"],
      "env": {
        "CODEGRAPH_CONFIG": "~/.codegraph/config.toml"
      }
    }
  }
}

Restart Claude Desktop to load the MCP server

Integrating with VS Code

Install the MCP extension for VS Code
Add to VS Code settings:

{
  "mcp.servers": {
    "codegraph": {
      "command": "codegraph",
      "args": ["start", "stdio"],
      "rootPath": "${workspaceFolder}"
    }
  }
}

API Integration

import requests
import json

# Connect to HTTP MCP server
base_url = "http://localhost:3000"

# Index a project
response = requests.post(f"{base_url}/index", json={
    "path": "/path/to/project",
    "languages": ["python", "javascript"]
})

# Search code
response = requests.post(f"{base_url}/search", json={
    "query": "async function",
    "limit": 10
})

results = response.json()

Using with CI/CD

# GitHub Actions example
name: CodeGraph Analysis

on: [push, pull_request]

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      
      - name: Install CodeGraph
        run: |
          cargo install codegraph-mcp
      
      - name: Index Codebase
        run: |
          codegraph init --non-interactive
          codegraph index . --languages rust,python
      
      - name: Run Analysis
        run: |
          codegraph stats --index --format json > analysis.json
      
      - name: Upload Results
        uses: actions/upload-artifact@v2
        with:
          name: codegraph-analysis
          path: analysis.json

🔧 Troubleshooting

Common Issues and Solutions

Issue: Server fails to start

Solution:

# Check if port is already in use
lsof -i :3000

# Kill existing process
codegraph stop --force

# Start with different port
codegraph start http --port 3001

Issue: Indexing is slow

Solution:

# Increase workers
codegraph index . --workers 16

# Exclude unnecessary files
codegraph index . --exclude "**/node_modules/**,**/dist/**"

# Use incremental indexing
codegraph config set indexing.incremental true

Issue: Out of memory during indexing

Solution:

# Reduce batch size
codegraph config set embedding.batch_size 50

# Limit memory usage
codegraph config set performance.memory_limit_mb 1024

# Use streaming mode
codegraph index . --streaming

Issue: Vector search returns poor results

Solution:

# Adjust similarity threshold
codegraph search "query" --threshold 0.5

# Re-index with better embeddings
codegraph config set embedding.model openai
codegraph index . --force

# Use different search type
codegraph search "query" --search-type fuzzy

#### Issue: Hugging Face model fails to download

**Solution:**
```bash
# Ensure you have internet access and the model name is correct
export CODEGRAPH_LOCAL_MODEL=Qdrant/all-MiniLM-L6-v2

# If the model is private, set a HF token (if required by your environment)
export HF_TOKEN=your_hf_access_token

# Clear/inspect cache (default): ~/.cache/huggingface
ls -lah ~/.cache/huggingface

# Note: models must include safetensors weights; PyTorch .bin-only models are not supported by the local loader here

Issue: Local embeddings are slow

Solution:

# Reduce batch size via config or environment (CPU defaults prioritize stability)
# Consider using a smaller model (e.g., all-MiniLM-L6-v2) or enabling GPU backends.

# For Apple Silicon (Metal) or CUDA, additional wiring can be enabled in config.
# Current default uses CPU; contact maintainers to enable device selectors in your environment.

Issue: FAISS linking error during cargo install

Error: ld: library 'faiss_c' not found

Solution:

# On macOS: Install FAISS via Homebrew
brew install faiss

# Set library paths and retry installation
export LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LIBRARY_PATH"
export LD_LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LD_LIBRARY_PATH"

# Retry the cargo install command
cargo install --path crates/codegraph-mcp --features "embeddings,codegraph-vector/onnx,faiss"

Alternative Solution:

# On Ubuntu/Debian
sudo apt-get update
sudo apt-get install libfaiss-dev

# On Fedora/RHEL
sudo dnf install faiss-devel

# Then retry cargo install
cargo install --path crates/codegraph-mcp --features "embeddings,codegraph-vector/onnx,faiss"


### Debug Mode

Enable debug logging for troubleshooting:

```bash
# Set debug log level
export RUST_LOG=debug
codegraph --verbose index .

# Check logs
tail -f ~/.codegraph/logs/codegraph.log

Health Checks

# Check system health
codegraph status --detailed

# Validate configuration
codegraph config validate

# Test database connection
codegraph test db

# Verify embeddings
codegraph test embeddings

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone repository
git clone https://github.com/jakedismo/codegraph-cli-mcp.git
cd codegraph-cli-mcp

# Install development dependencies
cargo install cargo-watch cargo-nextest

# Run tests
cargo nextest run

# Run with watch mode
cargo watch -x check -x test

📄 License

This project is dual-licensed under MIT and Apache 2.0 licenses. See LICENSE-MIT and LICENSE-APACHE for details.

🙏 Acknowledgments

Built with Rust
Powered by Tree-sitter
Vector search by FAISS
Graph storage with RocksDB
MCP Protocol by Anthropic

codegraph-rust

CodeGraph MCP Intelligence Platform

📋 Table of Contents

🎯 Revolutionary Overview

🧠 Core Innovation: MCP-First Intelligence

🚀 Revolutionary Capabilities

🌍 Universal Programming Language Support

🚀 Tier 1: Advanced Semantic Analysis (8 Languages)

🛠 Tier 2: Basic Semantic Analysis (3 Languages)

🔮 Future Language Roadmap

🎯 Revolutionary MCP Tools (11 Available)

✅ Available Immediately (No Model Required)

🧠 Available Once Qwen2.5-Coder Downloads

⚡ Performance Achievements

Existing Performance (Proven)

Revolutionary Performance (Validated)

Complete Local Stack Performance

📊 Performance Benchmarking (M4 Max 128GB)

Production Codebase Results (1,505 files, 2.5M lines)

Embedding Provider Performance Comparison

CodeGraph Advantages

Recommended Strategy

🎯 Success Indicators

✅ Working Correctly When You See:

🚨 Needs Attention When You See:

📈 Expected Results

First-Time Setup

Daily Usage

✨ Features

Core Features

🏗️ Architecture

🧠 Embeddings with ONNX Runtime (macOS)

📦 Prerequisites

System Requirements

Required Dependencies

Optional Dependencies

🚀 Performance Benchmarks - pure raw speed!

Build with performance features

Configure local embedding backend

Run the benchmark

🚀 Complete Installation Guide

Prerequisites

Step 1: Install System Dependencies

Step 2: Install SOTA Models

Step 3: Build CodeGraph with Complete Features

Step 4: Environment Configuration

🚀 Revolutionary Quick Start

Step 1: Initialize Your Project

Step 2: Index Your Codebase (Optimized for Your System)

Performance Expectations (128GB M4 Max)

Step 3: Start Revolutionary MCP Server

Step 4: Configure Claude Desktop

Step 5: Experience Revolutionary AI

🚀 High-Memory System Optimization

128GB M4 Max (Your System) - Ultra-High Performance

Memory-Based Auto-Optimization

Quality of Life Features

📊 Embedding Provider Options

Ollama (Recommended - Code-Specialized)

ONNX (Alternative - Speed Optimized)

Enabling Local Embeddings (Optional)

Method 3: Using Cargo

🎯 Quick Start

1. Initialize a New Project

2. Index Your Codebase

3. Start MCP Server

4. Search Your Code

📖 CLI Commands

Global Options

Command Reference

init - Initialize CodeGraph Project

start - Start MCP Server

stop - Stop MCP Server

status - Check Server Status

index - Index Project

search - Search Indexed Code

config - Manage Configuration

stats - Show Statistics

clean - Clean Resources

⚙️ Configuration

`init` - Initialize CodeGraph Project

`start` - Start MCP Server

`stop` - Stop MCP Server

`status` - Check Server Status

`index` - Index Project

`search` - Search Indexed Code

`config` - Manage Configuration

`stats` - Show Statistics

`clean` - Clean Resources