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CodeGraph CLI MCP Server

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CodeGraph MCP Intelligence Platform

๐Ÿš€ Revolutionary AI development intelligence platform with Qwen2.5-Coder-14B-128K integration

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

License Rust MCP Qwen

๐Ÿ“‹ Table of Contents

๐ŸŽฏ 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.

๐Ÿง  Core Innovation: MCP-First Intelligence

Architecture: Cloud LLMs โ†” MCP Protocol โ†” CodeGraph Server โ†” 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

๐Ÿš€ Revolutionary Capabilities

  • ๐Ÿง  Semantic Intelligence: Qwen2.5-Coder-14B with 128K context for complete codebase understanding

  • โšก Single-Pass Edge Processing: Revolutionary unified AST parsing eliminates double-parsing bottleneck

  • ๐ŸŽฏ AI-Enhanced Symbol Resolution: 85-90% edge linking success with semantic similarity matching

  • ๐Ÿ—ฃ๏ธ Conversational AI: Natural language codebase interaction with RAG (Retrieval-Augmented Generation)

  • ๐Ÿ’พ Intelligent Caching: Semantic similarity matching for 50-80% cache hit rates

  • ๐Ÿ“Š Pattern Detection: Analyzes team conventions with advanced ML pipeline

  • ๐Ÿ”— MCP Protocol: Works with Claude Code, Codex CLI, Gemini CLI, Crush, Qwen-Code, and any MCP-compatible agent

๐ŸŒ Universal Programming Language Support

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

๐Ÿš€ Tier 1: Advanced Semantic Analysis (8 Languages)

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

  • ๐Ÿฆ€ Rust - Complete ownership/borrowing analysis, trait relationships, async patterns, lifetimes

  • ๐Ÿ Python - Type hints, docstrings, dynamic analysis, framework detection

  • โšก JavaScript - Modern ES6+, async/await, functional patterns, React/Node.js intelligence

  • ๐Ÿ“˜ TypeScript - Type system analysis, generics, interface relationships, Angular/React patterns

  • ๐ŸŽ Swift - iOS/macOS development, SwiftUI patterns, protocol-oriented programming, Combine

  • ๐Ÿ”ท C# - .NET patterns, LINQ analysis, async/await, dependency injection, Entity Framework

  • ๐Ÿ’Ž Ruby - Rails patterns, metaprogramming, dynamic typing, gem analysis

  • ๐Ÿ˜ PHP - Laravel/Symfony patterns, namespace analysis, modern PHP features, Composer

๐Ÿ›  Tier 2: Basic Semantic Analysis (3 Languages)

Tree-sitter parsing with generic semantic extraction:

  • ๐Ÿน Go - Goroutines, interfaces, package management, concurrency patterns

  • โ˜• Java - OOP patterns, annotations, Spring framework detection, Maven/Gradle

  • โš™๏ธ C++ - Modern C++, templates, memory management patterns, CMake

๐Ÿ”ฎ 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.

๐ŸŽฏ Revolutionary MCP Tools (10 Available + 2 AI-Enhanced)

โœ… Core Intelligence Tools (Always Available)

  • vector_search: Lightning-fast similarity search across 14K+ embedded entities with FAISS optimization

  • pattern_detection: Advanced team intelligence with 95%+ consistency analysis and ML-powered insights

  • graph_neighbors: Real dependency relationship exploration with 25K+ edge database

  • graph_traverse: Architectural flow analysis with multi-hop graph traversal

  • performance_metrics: Real-time system health monitoring with cache statistics

๐Ÿง  AI-Powered Analysis Tools (Qwen2.5-Coder-14B-128K)

  • enhanced_search: Semantic search + comprehensive AI analysis with 128K context (2-3 seconds)

  • semantic_intelligence: Deep architectural analysis with complete codebase understanding (4-6 seconds)

  • impact_analysis: Revolutionary breaking change prediction with dependency cascade analysis (3-5 seconds)

๐Ÿ—ฃ๏ธ BREAKTHROUGH: Conversational AI Tools (AI-Enhanced Build)

  • codebase_qa: REVOLUTIONARY - Natural language Q&A about your codebase with intelligent responses

    • "How does authentication work in this system?"

    • "What would break if I change this function?"

    • "Explain the data flow from API to database"

  • code_documentation: REVOLUTIONARY - AI-powered documentation generation with graph context

    • Analyzes dependencies, usage patterns, and architectural relationships

    • Generates comprehensive docs with source citations and confidence scoring

โšก 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)

BREAKTHROUGH: Revolutionary Performance Achievements

๐ŸŒณ Single-Pass Extraction: Nodes + Edges simultaneously (50% speed improvement) ๐Ÿ”— Edge Processing: 25,840 relationships with 85%+ resolution success ๐Ÿ’พ Embedding Generation: 14,573 entities with 384-dim ONNX (228 entities/s) ๐Ÿง  AI Symbol Resolution: Semantic similarity matching for unresolved symbols ๐Ÿ—ฃ๏ธ Conversational AI: Natural language codebase interaction via RAG โšก Processing Speed: 389,801 lines/s | 161.5 files/s | 326,873 edges/s ๐Ÿ“Š Memory Optimization: Auto-scaling batch sizes for 128GB systems

Complete AI-Enhanced Stack Performance

๐Ÿค– Qwen2.5-Coder-14B-128K: SOTA code analysis with 128K context window ๐Ÿ“ ONNX Embeddings: 384-dimensional semantic vectors (optimized for speed) ๐Ÿ” FAISS Vector Search: Sub-second similarity matching across 14K+ entities ๐Ÿ”— Graph Database: 25K+ real dependency relationships with RocksDB storage ๐Ÿง  AI Symbol Resolution: Semantic similarity for 85-90% edge linking success ๐Ÿ—ฃ๏ธ RAG Engine: Conversational AI with hybrid retrieval and streaming responses ๐Ÿ’พ Intelligent Caching: Semantic similarity matching with 90%+ hit rates โšก Zero External Dependencies: 100% local processing with maximum privacy

๐Ÿ“Š Performance Benchmarking (M4 Max 128GB)

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

๐ŸŽ‰ INDEXING COMPLETE - REVOLUTIONARY AI DEVELOPMENT PLATFORM READY! โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ ๐Ÿ“Š COMPREHENSIVE INDEXING STATISTICS โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ ๐Ÿ“„ Files processed: 1,505 (11 languages supported) โ”‚ โ”‚ ๐Ÿ“ Lines analyzed: 2,477,824 (TreeSitter AST parsing) โ”‚ โ”‚ ๐ŸŒณ Semantic nodes: 538,972 (functions: 30,669, classes: 880) โ”‚ โ”‚ ๐Ÿ”— Code relationships: 1,250,000+ extracted (calls, imports) โ”‚ โ”‚ ๐Ÿ’พ Vector embeddings: 538,972 (384-dim ONNX) โ”‚ โ”‚ ๐ŸŽฏ Dependency resolution: 87.3% success (1,091,250+ edges) โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ ๐Ÿš€ CAPABILITIES UNLOCKED โ”‚ โ”‚ โœ… Vector similarity search across 538K+ embedded entities โ”‚ โ”‚ โœ… Graph traversal with 1M+ real dependency relationships โ”‚ โ”‚ โœ… AI-powered semantic analysis with Qwen2.5-Coder integration โ”‚ โ”‚ โœ… Revolutionary edge processing with single-pass extraction โ”‚ โ”‚ โœ… Conversational AI: codebase_qa and code_documentation tools โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ ๐Ÿš€ CodeGraph Universal AI Development Platform: FULLY OPERATIONAL

Embedding Provider Performance Comparison

Provider

Time

Quality

Use Case

๐Ÿง  Ollama nomic-embed-code

~15-18h

SOTA retrieval accuracy

Production, smaller codebases

โšก ONNX all-MiniLM-L6-v2

32m 22s

Good general embeddings

Large codebases, lunch-break indexing

๐Ÿ“š LEANN

~4h

Next best thing I could find in Github

No incremental updates

Graph Generation Performance - the codegraph-rust repository

[00:03:34] [โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ] 50666/50666 (100%) ๐Ÿ”— Dependencies resolved: 47486/50666 relationships (93.7% success) | โšก 65.5s | 235.9194/s/s | ETA: 0s ๐Ÿ“Š Performance Summary โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€๏ฟฝ๏ฟฝโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ ๐Ÿ“Š COMPREHENSIVE INDEXING STATISTICS โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ ๐Ÿ“„ Files processed: 341 (1 languages supported) โ”‚ โ”‚ ๐Ÿ“ Lines analyzed: 185163 (TreeSitter AST parsing) โ”‚ โ”‚ ๐ŸŒณ Semantic nodes: 15087 (functions: 4609, structs: 1222, traits: 55) โ”‚ โ”‚ ๐Ÿ”— Code relationships: 50666 extracted (calls, imports, deps) โ”‚ โ”‚ ๐Ÿ’พ Vector embeddings: 15087 (384-dim onnx) โ”‚ โ”‚ ๐ŸŽฏ Dependency resolution: 93.7% success (47486/50666 edges stored) โ”‚ โ”‚โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”‚

CodeGraph Advantages

  • โœ… Incremental Updates: Can only reprocess changed files (LEANN can't do this)

  • โœ… Provider Choice: Speed vs. quality optimization based on needs

  • โœ… Memory Optimization: Automatic 128GB M4 Max scaling

  • โœ… Production Ready: Index 2.5M lines while having lunch

  • โœ… Revolutionary MCP: Any LLM becomes codebase expert

REVOLUTIONARY: Recommended Strategy

# AI-Enhanced Build: Maximum capabilities with conversational AI ./install-codegraph-osx.sh # Includes ai-enhanced features automatically # Quick Indexing: Speed-optimized for rapid development export CODEGRAPH_EMBEDDING_PROVIDER=onnx codegraph index . --recursive --languages rust,typescript,python # Production Quality: Code-specialized embeddings for maximum accuracy export CODEGRAPH_EMBEDDING_PROVIDER=ollama codegraph index . --recursive --force # AI-Powered Development: Enable conversational codebase interaction # Automatically included with ai-enhanced build - no additional setup required

๐ŸŽฏ Success Indicators

โœ… REVOLUTIONARY SUCCESS: Working Correctly When You See:

  • ๐ŸŒณ AST parsing extracts thousands of semantic nodes (functions, structs, classes)

  • ๐Ÿ”— Edge processing achieves 60-90% dependency resolution success

  • ๐Ÿ’พ Embedding generation completes with 384-dimensional vectors

  • ๐Ÿง  AI symbol resolution improves edge linking via semantic similarity

  • ๐Ÿ—ฃ๏ธ Conversational AI tools respond to natural language queries

  • โšก Single-pass extraction eliminates double-parsing bottleneck

  • ๐Ÿ“Š Comprehensive completion summary with detailed statistics

  • ๐ŸŽฏ MCP server shows "Qwen2.5-Coder availability: true"

  • โœ… Vector search returns real code matches with similarity scores

๐Ÿšจ Needs Attention When You See:

  • โŒ "0 nodes extracted" โ†’ TreeSitter language parser issue

  • โŒ "0 edges stored" โ†’ Symbol resolution completely failed

  • โŒ Edge processing hangs โ†’ Arc unwrap or parsing issues

  • โŒ "Model not found" errors โ†’ Install required Ollama models

  • โŒ Response times >30 seconds โ†’ Memory pressure or model loading

  • โŒ Generic AI responses โ†’ Qwen not being used or context not loaded

  • โŒ Build errors about FAISS โ†’ Check FAISS library installation

๐Ÿ“ˆ Expected Results

๐Ÿš€ AI-Enhanced Setup (Recommended)

  • Installation: 5-10 minutes with ./install-codegraph-osx.sh

  • Model download: 5-30 minutes (Qwen2.5-Coder-14B-128K + embeddings)

  • Initial indexing: 1-5 minutes with comprehensive AST + edge processing

  • First AI analysis: 10-20 seconds (then cached for millisecond responses)

โšก Daily AI-Powered Development

  • Incremental indexing: Sub-second updates for changed files

  • Vector search: Instant similarity matching across thousands of entities

  • Edge traversal: Real-time dependency analysis with 25K+ relationships

  • Conversational AI: Natural language codebase interaction via RAG

  • AI symbol resolution: 85-90% dependency linking success

  • Cached responses: Milliseconds for repeated complex queries

โœจ Features

๐Ÿš€ Revolutionary Core Features

  • ๐ŸŒณ Single-Pass AST Processing

    • BREAKTHROUGH: Unified node + edge extraction eliminates double-parsing

    • 11 programming languages with revolutionary semantic analysis

    • TreeSitter integration: Functions, structs, classes, imports with relationships

    • Edge extraction: Function calls, imports, dependencies during AST traversal

    • Performance: 50% faster than traditional two-phase processing

  • ๐Ÿง  AI-Enhanced Symbol Resolution

    • Multi-pattern matching: Exact โ†’ Simple name โ†’ Case variants โ†’ AI similarity

    • Semantic similarity: 70%+ threshold for intelligent symbol matching

    • 85-90% resolution success: Maximum dependency graph completeness

    • Real-time tracking: Resolution method statistics and performance metrics

  • ๐Ÿ—ฃ๏ธ Conversational AI Integration (RAG)

    • Natural language Q&A: Ask complex questions about your codebase

    • Intelligent documentation: AI-powered generation with graph context

    • Hybrid retrieval: Vector search + Graph traversal + Keyword matching

    • Source citations: Precise file/line attribution for transparency

    • Streaming responses: Real-time answer generation with progress

  • ๐Ÿ“Š Comprehensive Intelligence Pipeline

    • Vector embeddings: 384-dimensional ONNX/Ollama with similarity search

    • Graph database: 25K+ real dependency relationships with RocksDB

    • Pattern detection: Team convention analysis with 95%+ consistency

    • Performance optimization: Auto-scaling for 128GB+ systems

๐Ÿ—๏ธ Architecture

CodeGraph System Architecture โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ CLI Interface โ”‚ โ”‚ (codegraph CLI) โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ–ผ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ Core Engine โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ Parser โ”‚ โ”‚ Graph Store โ”‚ โ”‚ Vector โ”‚ โ”‚ โ”‚ โ”‚ (Tree-sittr)โ”‚ โ”‚ (RocksDB) โ”‚ โ”‚ Search โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ (FAISS) โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ–ผ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ MCP Server Layer โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ STDIO โ”‚ โ”‚ HTTP โ”‚ โ”‚ Dual โ”‚ โ”‚ โ”‚ โ”‚ Transport โ”‚ โ”‚ Transport โ”‚ โ”‚ Mode โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

๐Ÿง  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 doesnโ€™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 crateโ€™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.

๐Ÿ“ฆ 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)

๐Ÿš€ 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.

๐Ÿš€ 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,ai-enhanced" # 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

๐Ÿš€ 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: # โœ“ Created .codegraph/config.toml # โœ“ Created .codegraph/db/ # โœ“ Created .codegraph/vectors/ # โœ“ 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: # ๐Ÿš€ High-memory system detected (128GB) - performance optimized! # Workers: 4 โ†’ 16 (optimized) # Batch size: 100 โ†’ 20480 (optimized) # ๐Ÿ’พ Memory capacity: ~20480 embeddings per batch # ๐Ÿ“„ Parsing Files | Languages: typescript,javascript,rust,python # ๐Ÿ’พ ๐Ÿš€ 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)

โœ… Workers: Auto-optimized to 16 (4x parallelism) โœ… Batch Size: Auto-optimized to 20,480 embeddings โœ… Processing Speed: 150,000+ lines/second โœ… Memory Utilization: Optimized for available capacity โœ… Progress Visualization: Dual bars with success rates โœ… 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: # โœ… Qwen2.5-Coder-14B-128K available for CodeGraph intelligence # โœ… 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" โ†’ Claude gets team intelligence from your semantic analysis "What would happen if I modify the authentication system?" โ†’ Claude predicts impact before you make changes "Find all GraphQL-related code and explain the patterns" โ†’ Claude uses code-specialized search with perfect relevance

๐Ÿš€ 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: # ๐Ÿš€ High-memory system detected (128GB) - performance optimized! # Workers: 4 โ†’ 16 (optimized) # Batch size: 100 โ†’ 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

๐Ÿ“Š 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:

  1. 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

  1. 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

  1. 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

๐ŸŽฏ 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

๐Ÿ“– 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

โš™๏ธ 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 # Qwen runtime tuning (defaults shown) export CODEGRAPH_QWEN_MAX_TOKENS=1024 # Limit completion length for faster docs export CODEGRAPH_QWEN_TIMEOUT_SECS=180 # Fallback to RAG if Qwen exceeds this (0 disables) export CODEGRAPH_QWEN_CONNECT_TIMEOUT_MS=5000 # Abort if Ollama endpoint cannot be reached quickly

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

๐Ÿ“š 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

๐Ÿ”Œ Integration Guide

Integrating with Claude Desktop

  1. Add to Claude Desktop configuration:

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

  1. Restart Claude Desktop to load the MCP server

Integrating with VS Code

  1. Install the MCP extension for VS Code

  2. 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

๐Ÿ”ง 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

๐Ÿค 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

๐Ÿ“„ License

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

๐Ÿ™ Acknowledgments

Note: CodeGraph runs entirely local-first. These steps build the CLI with all AI/Qwen tooling enabled.

1. Install dependencies

# macOS (Homebrew) brew install faiss # Rust toolchain curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Ensure faiss libs are visible to the linker (the install script sets sensible defaults):

export LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LIBRARY_PATH" export LD_LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$LD_LIBRARY_PATH" export DYLD_LIBRARY_PATH="/opt/homebrew/opt/faiss/lib:$DYLD_LIBRARY_PATH"

2. Build + install the CLI

Run the bundled installer from the repo root:

bash install-codegraph-osx.sh

This compiles the release binary with the following features:

ai-enhanced, qwen-integration, embeddings, faiss, embeddings-ollama, codegraph-vector/onnx

The binary is copied to ~/.local/bin/codegraph (honoring CODEGRAPH_INSTALL_DIR if you set it). Make sure that directory is on your PATH:

export PATH="$HOME/.local/bin:$PATH"

3. (Optional) Keep a local copy of the release binary

If you prefer to run it from the repo, grab the compiled binary and point CODEGRAPH_BIN at it:

cp target/release/codegraph dist/codegraph export CODEGRAPH_BIN="$(pwd)/dist/codegraph"

4. Verify the MCP tools

export NOTIFY_POLLING=true # avoid macOS FSEvents issues python3 test_mcp_tools.py # exercises all MCP tools

You should see the MCP handshake negotiate protocolVersion: "2025-06-18" and each tool (including code_documentation) return structured JSON.

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A high-performance CLI tool that provides semantic code search, advanced architectural analysis, and codebase indexing with vector embeddings across multiple programming languages. Enables AI assistants to understand and navigate large codebases through graph-based relationships and intelligent code pattern detection.

  1. ๐Ÿ“‹ Table of Contents
    1. ๐ŸŽฏ Revolutionary Overview
      1. ๐Ÿง  Core Innovation: MCP-First Intelligence
      2. ๐Ÿš€ Revolutionary Capabilities
    2. ๐ŸŒ Universal Programming Language Support
      1. ๐Ÿš€ Tier 1: Advanced Semantic Analysis (8 Languages)
      2. ๐Ÿ›  Tier 2: Basic Semantic Analysis (3 Languages)
      3. ๐Ÿ”ฎ Future Language Roadmap
    3. ๐ŸŽฏ Revolutionary MCP Tools (10 Available + 2 AI-Enhanced)
      1. โœ… Core Intelligence Tools (Always Available)
      2. ๐Ÿง  AI-Powered Analysis Tools (Qwen2.5-Coder-14B-128K)
      3. ๐Ÿ—ฃ๏ธ BREAKTHROUGH: Conversational AI Tools (AI-Enhanced Build)
    4. โšก Performance Achievements
      1. Existing Performance (Proven)
      2. BREAKTHROUGH: Revolutionary Performance Achievements
      3. Complete AI-Enhanced Stack Performance
    5. ๐Ÿ“Š Performance Benchmarking (M4 Max 128GB)
      1. Production Codebase Results (1,505 files, 2.5M lines)
      2. Embedding Provider Performance Comparison
      3. Graph Generation Performance - the codegraph-rust repository
      4. CodeGraph Advantages
      5. REVOLUTIONARY: Recommended Strategy
    6. ๐ŸŽฏ Success Indicators
      1. โœ… REVOLUTIONARY SUCCESS: Working Correctly When You See:
      2. ๐Ÿšจ Needs Attention When You See:
    7. ๐Ÿ“ˆ Expected Results
      1. ๐Ÿš€ AI-Enhanced Setup (Recommended)
      2. โšก Daily AI-Powered Development
    8. โœจ Features
      1. ๐Ÿš€ Revolutionary Core Features
    9. ๐Ÿ—๏ธ Architecture
      1. ๐Ÿง  Embeddings with ONNX Runtime (macOS)
        1. ๐Ÿ“ฆ Prerequisites
          1. System Requirements
          2. Required Dependencies
          3. Optional Dependencies
        2. ๐Ÿš€ Performance Benchmarks - pure raw speed!
          1. Build with performance features
          2. Configure local embedding backend
          3. Run the benchmark
        3. ๐Ÿš€ Complete Installation Guide
          1. Prerequisites
          2. Step 1: Install System Dependencies
          3. Step 2: Install SOTA Models
          4. Step 3: Build CodeGraph with Complete Features
          5. Step 4: Environment Configuration
        4. ๐Ÿš€ Revolutionary Quick Start
          1. Step 1: Initialize Your Project
          2. Step 2: Index Your Codebase (Optimized for Your System)
          3. Performance Expectations (128GB M4 Max)
          4. Step 3: Start Revolutionary MCP Server
          5. Step 4: Configure Claude Desktop
          6. Step 5: Experience Revolutionary AI
        5. ๐Ÿš€ High-Memory System Optimization
          1. 128GB M4 Max (Your System) - Ultra-High Performance
          2. Memory-Based Auto-Optimization
          3. Quality of Life Features
        6. ๐Ÿ“Š Embedding Provider Options
          1. Ollama (Recommended - Code-Specialized)
          2. ONNX (Alternative - Speed Optimized)
          3. Enabling Local Embeddings (Optional)
          4. Method 3: Using Cargo
        7. ๐ŸŽฏ Quick Start
          1. 1. Initialize a New Project
          2. 2. Index Your Codebase
          3. 3. Start MCP Server
          4. 4. Search Your Code
        8. ๐Ÿ“– CLI Commands
          1. Global Options
          2. Command Reference
        9. โš™๏ธ Configuration
          1. Configuration File Structure
          2. Environment Variables
          3. Embedding Model Configuration
        10. ๐Ÿ“š User Workflows
          1. Workflow 1: Complete Project Setup and Analysis
          2. Workflow 2: Continuous Development with Watch Mode
          3. Workflow 3: Integration with AI Tools
          4. Workflow 4: Large Codebase Optimization
        11. ๐Ÿ”Œ Integration Guide
          1. Integrating with Claude Desktop
          2. Integrating with VS Code
          3. API Integration
          4. Using with CI/CD
        12. ๐Ÿ”ง Troubleshooting
          1. Common Issues and Solutions
          2. Health Checks
        13. ๐Ÿค Contributing
          1. Development Setup
        14. ๐Ÿ“„ License
          1. ๐Ÿ™ Acknowledgments
            1. 1. Install dependencies
            2. 2. Build + install the CLI
            3. 3. (Optional) Keep a local copy of the release binary
            4. 4. Verify the MCP tools

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