CodeGraph

Turn your codebase into a searchable knowledge graph powered by embeddings and LLMs

CodeGraph indexes your source code to a graph database, creates semantic embeddings, and exposes a Model Context Protocol (MCP) server that AI tools (Claude Desktop, LM Studio, etc.) can query for project-aware context.

✨ What you get:

🔍 Semantic code search across your entire codebase
🧠 LLM-powered code intelligence and analysis
📊 Automatic dependency graphs and code relationships
⚡ Fast vector search with FAISS or cloud SurrealDB HNSW (2-5ms query latency)
🔌 MCP server for AI tool integration (stdio and streamable HTTP)
⚙️ Easy-to-use CLI interface
☁️ NEW: Jina AI cloud embeddings with modifiable models and dimensions and reranking
🗄️ NEW: SurrealDB HNSW backend for cloud-native and local vector search
📦 NEW: Node.js NAPI bindings for zero-overhead TypeScript integration
🤖 NEW: Agentic code-agent tools with tier-aware multi-step reasoning
🔬 EXPERIMENTAL: AutoAgents framework integration for improved agent orchestration

⚠️ Important: MCP Server Architecture Change

FAISS+RocksDB support in MCP server is deprecated in favor of SurrealDB-based architecture.

What Changed:

❌ MCP server no longer uses FAISS vector search or RocksDB graph storage
✅ CLI and SDK continue to support FAISS/RocksDB for local operations
✅ NAPI bindings still provide TypeScript access to all features
🆕 MCP code-agent tools now require SurrealDB for graph analysis

Required Setup for Code-Agent Tools:

The new agentic MCP tools (agentic_code_search, agentic_dependency_analysis, etc.) require SurrealDB:

Option 1: Free Cloud Instance (Recommended)

Sign up at Surreal Cloud
Get 1GB FREE instance - perfect for testing and small projects
Configure connection details in environment variables

Option 2: Local Installation

# Install SurrealDB
curl -sSf https://install.surrealdb.com | sh

# Run locally
surreal start --bind 127.0.0.1:3004 --user root --pass root memory

Free Cloud Resources:

🆓 SurrealDB Cloud: 1GB free instance at surrealdb.com/cloud
🆓 Jina AI: 10 million free API tokens at jina.ai for embeddings and reranking

Why This Change:

Native graph capabilities: SurrealDB provides built-in graph database features
Unified storage: Single database for both vectors and graph relationships and extendable to relational and document use-cases!
Cloud-native: Better support for distributed deployments
Reduced complexity: Eliminates custom RocksDB integration layer

See CHANGELOG.md for detailed migration guide.

🔬 Experimental: AutoAgents Framework

CodeGraph now supports the AutoAgents framework for agentic orchestration as an experimental feature.

What is AutoAgents?

Modern Rust-based agent framework with ReAct (Reasoning + Acting) pattern
Replaces ~1,200 lines of custom orchestration code
Maintains compatibility with all 7 existing agentic MCP tools
Same tier-aware prompting system (Small/Medium/Large/Massive)

Enabling AutoAgents

Build with experimental feature:

# Using Makefile
make build-mcp-autoagents

# Or directly with cargo
cargo build --release -p codegraph-mcp --features "ai-enhanced,autoagents-experimental,faiss,ollama"

Without AutoAgents (default):

cargo build --release -p codegraph-mcp --features "ai-enhanced,faiss,ollama"

Status

✅ Core implementation complete
⏳ Testing and validation in progress
📝 Feedback welcome via GitHub issues
🔄 Legacy orchestrator remains as stable fallback

The experimental feature is opt-in via build flag and does not affect existing functionality when disabled.

🎯 Choose Your Setup

Pick the setup that matches your needs:

Option 1: Local Setup (Free, Private) 🏠

Best for: Privacy-conscious users, offline work, no API costs

Providers:

Embeddings: ONNX or Ollama
LLM: Ollama (Qwen2.5-Coder, CodeLlama, etc.)

Pros: ✅ Free, ✅ Private, ✅ No internet required after setup Cons: ❌ Slower, ❌ Requires local GPU/CPU resources

→ Jump to Local Setup Instructions

Option 2: LM Studio (Best Performance on Mac) 🚀

Best for: Mac users (Apple Silicon), best local performance

Providers:

Embeddings: LM Studio (Jina embeddings)
LLM: LM Studio (DeepSeek Coder, etc.)

Pros: ✅ 120 embeddings/sec, ✅ MLX + Flash Attention 2, ✅ Free Cons: ❌ Mac only, ❌ Requires LM Studio app

→ Jump to LM Studio Setup Instructions

Option 3: Cloud Providers (Best Quality) ☁️

Best for: Production use, best quality, don't want to manage local models

Providers:

Embeddings: Jina (You get 10 million tokens for free when you just create an account!)
LLM: Anthropic Claude or OpenAI GPT-5-*
Backend: SurrealDB graph database (You get a free cloud instance up-to 1gb! Or run it completely locally!)

Pros: ✅ Best quality, ✅ Fast, ✅ 1M context (sonnet[1m]) Cons: ❌ API costs, ❌ Requires internet, ❌ Data sent to cloud

→ Jump to Cloud Setup Instructions

Option 4: Hybrid (Mix & Match) 🔀

Best for: Balancing cost and quality

Example combinations:

Local embeddings (ONNX) + Cloud LLM (OpenAI, Claude, x.ai)
LMStudio embeddings + Cloud LLM (OpenAI, Claude, x.ai)
Jina AI embeddings + Local LLM (Ollama, LMStudio)

→ Jump to Hybrid Setup Instructions

🛠️ Installation

Prerequisites (All Setups)

# 1. Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# 2. Install FAISS (vector search library)
# macOS:
brew install faiss

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

# Arch Linux:
sudo pacman -S faiss

Local Setup (ONNX + Ollama)

Step 1: Install Ollama

# macOS/Linux:
curl -fsSL https://ollama.com/install.sh | sh

# Or download from: https://ollama.com/download

brew install onnx-runtime

Step 2: Pull models

# Pull embedding model
hf (cli) download qdrant/all-minillm-onnx

# Pull LLM for code intelligence (optional)
ollama pull qwen2.5-coder:14b

Step 3: Build CodeGraph

cd codegraph-rust

# Build with ONNX embeddings and Ollama support
cargo build --release --features "onnx,ollama,faiss"

Step 4: Configure

Create ~/.codegraph/config.toml:

[embedding]
provider = "onnx"  # or "ollama" if you prefer
model = "qdrant/all-minillm-onnx"
dimension = 384

[llm]
enabled = true
provider = "ollama"
model = "qwen2.5-coder:14b"
ollama_url = "http://localhost:11434"

Step 5: Index and run

# Index your project
./target/release/codegraph index /path/to/your/project

# Start MCP server
./target/release/codegraph start stdio

✅ Done! Your local setup is ready.

LM Studio Setup

Step 1: Install LM Studio

Download from lmstudio.ai
Install and launch the app

Step 2: Download models in LM Studio

Embedding model: jinaai/jina-embeddings-v4
LLM model (optional): lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF

Step 3: Start LM Studio server

In LM Studio, go to "Local Server" tab
Click "Start Server" (runs on http://localhost:1234)

Step 4: Build CodeGraph

cd codegraph-rust

# Build with OpenAI-compatible support (for LM Studio)
cargo build --release --features "openai-compatible,faiss"

Step 5: Configure

Create ~/.codegraph/config.toml:

[embedding]
provider = "lmstudio"
model = "jinaai/jina-embeddings-v4"
lmstudio_url = "http://localhost:1234"
dimension = 2048

[llm]
enabled = true
provider = "lmstudio"
model = "lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF"
lmstudio_url = "http://localhost:1234"

Step 6: Index and run

# Index your project
./target/release/codegraph index /path/to/your/project

# Start MCP server
./target/release/codegraph start stdio

✅ Done! LM Studio setup complete.

Cloud Setup (Anthropic, OpenAI, xAI & Jina AI)

Step 1: Get API keys

Anthropic: console.anthropic.com(Claude 4.5 models 1M/200k ctx)
OpenAI: platform.openai.com(GPT-5 models 400k/200k ctx)
xAI: x.ai (Grok-4-fast with 2M ctx, $0.50-$1.50/M tokens)
Jina AI: jina.ai (for SOTA embeddings & reranking)
SurrealDB [https://www.surrealdb.com] (for graph dabase backend local or cloud based setup)

Step 2: Build CodeGraph with cloud features

cd codegraph-rust

# Build with all cloud providers
cargo build --release --features "anthropic,openai-llm,openai,faiss"

# Or with Jina AI cloud embeddings (Matryoska dimensions + reranking)
cargo build --release --features "cloud-jina,anthropic,faiss"

# Or with SurrealDB HNSW cloud/local vector backend
cargo build --release --features "cloud-surrealdb,openai,faiss"

Step 3: Run setup wizard (easiest)

./target/release/codegraph-setup

The wizard will guide you through configuration.

Or manually configure ~/.codegraph/config.toml:

For Anthropic Claude:

[embedding]
provider = "jina" # or openai
model = "jina-embeddings-v4"
openai_api_key = "sk-..."  # or set OPENAI_API_KEY env var
dimension = 2048

[llm]
enabled = true
provider = "anthropic"
model = "claude-haiku"
anthropic_api_key = "sk-ant-..."  # or set ANTHROPIC_API_KEY env var
context_window = 200000

For OpenAI (with reasoning models):

[embedding]
provider = "jina" # or openai
model = "jina-embeddings-v4"
openai_api_key = "sk-..."
dimension = 2048

[llm]
enabled = true
provider = "openai"
model = "gpt-5-codex-mini"
openai_api_key = "sk-..."
max_completion_token = 128000
reasoning_effort = "medium"  # reasoning models: "minimal", "medium", "high"

For Jina AI (cloud embeddings with reranking):

[embedding]
provider = "jina"
model = "jina-embeddings-v4"
jina_api_key = "jina_..."  # or set JINA_API_KEY env var
dimension = 2048 # or matryoshka 1024,512,256 adjust the schemas/*.surql file HNSW vector index to match your embedding model dimensions
jina_enable_reranking = true  # Optional two-stage retrieval
jina_reranking_model = "jina-reranker-v3"

[llm]
enabled = true
provider = "anthropic"
model = "claude-haiku"
anthropic_api_key = "sk-ant-..."

For xAI Grok (2M context window, $0.50-$1.50/M tokens):

[embedding]
provider = "openai"  # or "jina"
model = "text-embedding-3-small"
openai_api_key = "sk-..."
dimension = 2048

[llm]
enabled = true
provider = "xai"
model = "grok-4-fast"  # or "grok-4-turbo"
xai_api_key = "xai-..."  # or set XAI_API_KEY env var
xai_base_url = "https://api.x.ai/v1"  # default, can be omitted
reasoning_effort = "medium"  # Options: "minimal", "medium", "high"
context_window = 2000000  # 2M tokens!

For SurrealDB HNSW (graph database backend with advanced features):

[embedding]
provider = "jina"  # or "openai"
model = "jina-embeddings-v4"
openai_api_key = "sk-..."
dimension = 2048

[vector_store]
backend = "surrealdb"  # Instead of "faiss"
surrealdb_url = "ws://localhost:8000"  # or cloud instance
surrealdb_namespace = "codegraph"
surrealdb_database = "production"

[llm]
enabled = true
provider = "anthropic"
model = "claude-haiku"

Step 4: Index and run

# Index your project
./target/release/codegraph index /path/to/your/project

# Start MCP server
./target/release/codegraph start stdio

✅ Done! Cloud setup complete.

Hybrid Setup

Mix local and cloud providers to balance cost and quality:

Example: Local embeddings + Cloud LLM

[embedding]
provider = "onnx"  # Free, local
model = "sentence-transformers/all-MiniLM-L6-v2"
dimension = 384

[llm]
enabled = true
provider = "anthropic"  # Best quality for analysis
model = "sonnet[1m]"
anthropic_api_key = "sk-ant-..."

Build with required features:

cargo build --release --features "onnx,anthropic,faiss"

⚙️ Configuration

Quick Configuration

Use the interactive wizard:

cargo build --release --bin codegraph-setup --features all-cloud-providers
./target/release/codegraph-setup

Manual Configuration

Configuration directory: ~/.codegraph/

All configuration files are stored in ~/.codegraph/ in TOML format.

Configuration is loaded from (in order):

~/.codegraph/default.toml (base configuration)
~/.codegraph/{environment}.toml (e.g., development.toml, production.toml)
~/.codegraph/local.toml (local overrides, machine-specific)
./config/ (fallback for backward compatibility)
Environment variables (CODEGRAPH__* prefix)

See Configuration Guide for complete documentation.

Full configuration example:

[embedding]
provider = "lmstudio"  # or "onnx", "ollama", "openai"
model = "jinaai/jina-embeddings-v4"
dimension = 2048
batch_size = 64

[llm]
enabled = true
provider = "anthropic"  # or "openai", "ollama", "lmstudio" or "xai"
model = "haiku"
anthropic_api_key = "sk-ant-..."
context_window = 200000
temperature = 0.1
max_completion_token = 4096

[performance]
num_threads = 0  # 0 = auto-detect
cache_size_mb = 512
max_concurrent_requests = 4

[logging]
level = "warn"  # trace, debug, info, warn, error
format = "pretty"  # pretty, json, compact

See .codegraph.toml.example for all options.

🚀 Usage

Basic Commands

# Index a project
codegraph index -r /path/to/project

# Start MCP server (for Claude Desktop, LM Studio, etc.)
codegraph start stdio

# List available MCP tools
codegraph tools list

Note: HTTP transport is not yet implemented with the official rmcp SDK. Use STDIO transport for all MCP integrations.

Using with Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on Mac):

{
  "mcpServers": {
    "codegraph": {
      "command": "/path/to/codegraph",
      "args": ["start", "stdio"],
      "env": {
        "RUST_LOG": "warn"
      }
    }
  }
}

Using with LM Studio

Start CodeGraph MCP server: codegraph start stdio
In LM Studio, enable MCP support in settings
CodeGraph tools will appear in LM Studio's tool palette

📚 For AI Agents: Using CodeGraph MCP

CodeGraph provides powerful code intelligence tools via the Model Context Protocol (MCP).

Available MCP Tools

After indexing your codebase, AI agents can use these tools:

🔍 enhanced_search - Semantic code search with AI insights (2-5s)
🔬 pattern_detection - Analyze coding patterns and conventions (1-3s)
⚡ vector_search - Fast similarity-based code search (0.5s)
🗺️ graph_neighbors - Find code dependencies for an element (0.3s)
🌐 graph_traverse - Follow dependency chains through code (0.5-2s)
💬 codebase_qa - Ask questions with RAG [feature-gated] (5-30s)
📖 code_documentation - Generate AI docs [feature-gated] (10-45s)

Quick Start

# 1. Index your codebase
codegraph index /path/to/your/project

# 2. Start MCP server
codegraph start stdio

# 3. Use tools from your AI agent
enhanced_search("how does authentication work?")
graph_neighbors("node-uuid-from-search-results")

📊 Feature Flags Reference

When building, include features for the providers you want to use:

Feature	Providers Enabled	Use Case
`onnx`	ONNX embeddings	Local CPU/GPU embeddings
`ollama`	Ollama embeddings + LLM	Local models via Ollama
`openai`	OpenAI embeddings	Cloud embeddings (text-embedding-3-large/small)
`openai-llm`	OpenAI	Cloud LLM (gpt-5, gpt-5-codex, gpt-5-codex-mini)
`anthropic`	Anthropic Claude	Cloud LLM (Claude 4.5, Haiku 4.5)
`openai-compatible`	LMStudio, custom providers	OpenAI Responses API compatible
`cloud-jina`	Jina AI embeddings + reranking	Cloud embeddings & Free usage (SOTA and variable dims)
`cloud-surrealdb`	SurrealDB HNSW	Local & Free Cloud-native graph database backend (up-to 1gb)
`cloud`	Jina AI + SurrealDB	All cloud vector & graph features
`faiss`	FAISS vector search	Local vector search graph backend (rocksdb persisted)
`all-cloud-providers`	All cloud LLM providers	Shortcut for Jina + Surreal + Anthropic + OpenAI

Common Build Commands

# Local only (ONNX + Ollama)
cargo build --release --features "onnx,ollama,faiss"

# LM Studio
cargo build --release --features "openai-compatible,faiss"

# Cloud only (Anthropic + OpenAI)
cargo build --release --features "anthropic,openai-llm,openai,faiss"

# Jina AI cloud embeddings + local FAISS
cargo build --release --features "cloud-jina,faiss"

# SurrealDB cloud vector backend
cargo build --release --features "cloud-surrealdb,openai,faiss"

# Full cloud (Jina + SurrealDB + Anthropic)
cargo build --release --features "cloud,anthropic,faiss"

# Everything (local + cloud)
cargo build --release --features "all-cloud-providers,onnx,ollama,cloud,faiss"

⚡ Performance

Speed Metrics (Apple Silicon + LM Studio)

Operation	Performance	Notes
Embedding generation	120 embeddings/sec	LM Studio with MLX
Vector search (local)	2-5ms latency	FAISS with index caching
Vector search (cloud)	2-5ms latency	SurrealDB HNSW
Jina AI embeddings	50-150ms per query	Cloud API call overhead
Jina reranking	80-200ms for top-K	Two-stage retrieval
Ollama embeddings	~60 embeddings/sec	About half LM Studio speed

Optimizations (Enabled by Default)

Optimization	Speedup	Memory Cost
FAISS index cache	10-50×	300-600 MB
Embedding cache	10-100×	~90 MB
Query cache	100×	~10 MB
Parallel search	2-3×	Minimal

🔧 Troubleshooting

Build Issues

"Could not find library faiss"

# Install FAISS first
brew install faiss  # macOS
sudo apt-get install libfaiss-dev  # Ubuntu

"Feature X is not enabled"

Make sure you included the feature flag when building
Example: cargo build --release --features "anthropic,faiss"

Runtime Issues

"API key not found"

Set environment variable: export ANTHROPIC_API_KEY="sk-ant-..."
Or add to config file: anthropic_api_key = "sk-ant-..."

"Model not found"

For Ollama: Run ollama pull <model-name> first
For LM Studio: Download the model in LM Studio app
For cloud: Check your model name matches available models

"Connection refused"

LM Studio: Make sure the local server is running
Ollama: Check Ollama is running with ollama list
Cloud: Check your internet connection

Getting Help

Check docs/CLOUD_PROVIDERS.md for detailed provider setup
See LMSTUDIO_SETUP.md for LM Studio specifics
Open an issue on GitHub with your error message

📦 Node.js Integration (NAPI Bindings)

Zero-Overhead TypeScript Integration

CodeGraph provides native Node.js bindings through NAPI-RS for seamless TypeScript/JavaScript integration:

Key Features:

🚀 Native Performance: Direct Rust-to-Node.js bindings with zero serialization overhead
📘 Auto-Generated Types: TypeScript definitions generated directly from Rust code
⚡ Async Runtime: Full tokio async support integrated with Node.js event loop
🔄 Hot-Reload Config: Update configuration without restarting your Node.js process
🌐 Dual-Mode Search: Automatic routing between local FAISS and cloud SurrealDB

Installation

Option 1: Direct Install (Recommended)

# Build the addon
cd crates/codegraph-napi
npm install
npm run build

# Install in your project
cd /path/to/your-project
npm install /path/to/codegraph-rust/crates/codegraph-napi

Option 2: Pack and Install

# Build and pack
cd crates/codegraph-napi
npm install
npm run build
npm pack  # Creates codegraph-napi-1.0.0.tgz

# Install in your project
cd /path/to/your-project
npm install /path/to/codegraph-rust/crates/codegraph-napi/codegraph-napi-1.0.0.tgz

API Examples

Semantic Search:

import { semanticSearch } from 'codegraph-napi';

const results = await semanticSearch('find authentication code', {
  limit: 10,
  useCloud: true,      // Use cloud search with automatic fallback
  reranking: true      // Enable Jina reranking (if configured)
});

console.log(`Found ${results.totalCount} results in ${results.searchTimeMs}ms`);
console.log(`Search mode: ${results.modeUsed}`);  // "local" or "cloud"

Configuration Management:

import { getCloudConfig, reloadConfig } from 'codegraph-napi';

// Check cloud feature availability
const config = await getCloudConfig();
console.log('Jina AI enabled:', config.jina_enabled);
console.log('SurrealDB enabled:', config.surrealdb_enabled);

// Hot-reload configuration without restart
await reloadConfig();

Embedding Operations:

import { getEmbeddingStats, countTokens } from 'codegraph-napi';

// Get embedding provider stats
const stats = await getEmbeddingStats();
console.log(`Provider: ${stats.provider}, Dimension: ${stats.dimension}`);

// Count tokens for cost estimation (Jina AI)
const tokens = await countTokens("query text");
console.log(`Token count: ${tokens}`);

Graph Navigation:

import { getNeighbors, getGraphStats } from 'codegraph-napi';

// Get connected nodes
const neighbors = await getNeighbors(nodeId);

// Get graph statistics
const stats = await getGraphStats();
console.log(`Nodes: ${stats.node_count}, Edges: ${stats.edge_count}`);

Build Options

Feature flags for selective compilation:

# Local-only (FAISS, no cloud)
npm run build  # Uses default = ["local"]

# Cloud-only (no FAISS)
npm run build -- --features cloud

# Full build (local + cloud)
npm run build -- --features full

See NAPI README for complete documentation.

🤝 Contributing

We welcome contributions!

# Format code
cargo fmt --all

# Run linter
cargo clippy --workspace --all-targets

# Run tests
cargo test --workspace

Open an issue to discuss large changes before starting.

📄 License

Dual-licensed under MIT and Apache 2.0. See LICENSE-MIT and LICENSE-APACHE for details.

📚 Learn More

NAPI Bindings Guide - Complete TypeScript integration documentation
Cloud Providers Guide - Detailed cloud provider setup
Configuration Reference - All configuration options
Changelog - Version history and release notes
Legacy Docs - Historical experiments and architecture notes

Name		Name	Last commit message	Last commit date
Latest commit History 313 Commits
.github		.github
config		config
crates		crates
docs		docs
examples		examples
schema		schema
sdk		sdk
tests		tests
vendor		vendor
.dockerignore		.dockerignore
.dockerignore.security		.dockerignore.security
.env.example		.env.example
.gitignore		.gitignore
.gitignore.codegraph		.gitignore.codegraph
AGENT_FRAMEWORKS_REFERENCE.md		AGENT_FRAMEWORKS_REFERENCE.md
AGENT_FRAMEWORK_DECISION.md		AGENT_FRAMEWORK_DECISION.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
Makefile		Makefile
README.md		README.md
RUST_AGENT_FRAMEWORKS_ANALYSIS.md		RUST_AGENT_FRAMEWORKS_ANALYSIS.md
TESTING.md		TESTING.md
claude-desktop-config.json		claude-desktop-config.json
clippy.toml		clippy.toml
install-codegraph-cloud.sh		install-codegraph-cloud.sh
install-codegraph-local-speed-osx.sh		install-codegraph-local-speed-osx.sh
install-codegraph.sh		install-codegraph.sh
interactive.js		interactive.js
jest.config.js		jest.config.js
package-mcp.json		package-mcp.json
pyproject.toml		pyproject.toml
requirements-test.txt		requirements-test.txt
rustfmt.toml		rustfmt.toml
style.css		style.css
test_agentic_tools.py		test_agentic_tools.py
tsconfig.json		tsconfig.json
uv.lock		uv.lock
verify-setup.sh		verify-setup.sh

Jakedismo/codegraph-rust

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CodeGraph

⚠️ Important: MCP Server Architecture Change

What Changed:

Required Setup for Code-Agent Tools:

Why This Change:

🔬 Experimental: AutoAgents Framework

What is AutoAgents?

Enabling AutoAgents

Status

📋 Table of Contents

🎯 Choose Your Setup

Option 1: Local Setup (Free, Private) 🏠

Option 2: LM Studio (Best Performance on Mac) 🚀

Option 3: Cloud Providers (Best Quality) ☁️

Option 4: Hybrid (Mix & Match) 🔀

🛠️ Installation

Prerequisites (All Setups)

Local Setup (ONNX + Ollama)

LM Studio Setup

Cloud Setup (Anthropic, OpenAI, xAI & Jina AI)

Hybrid Setup

⚙️ Configuration

Quick Configuration

Manual Configuration

🚀 Usage

Basic Commands

Using with Claude Desktop

Using with LM Studio

📚 For AI Agents: Using CodeGraph MCP

Available MCP Tools

Quick Start

📊 Feature Flags Reference

Common Build Commands

⚡ Performance

Speed Metrics (Apple Silicon + LM Studio)

Optimizations (Enabled by Default)

🔧 Troubleshooting

Build Issues

Runtime Issues

Getting Help

📦 Node.js Integration (NAPI Bindings)

Zero-Overhead TypeScript Integration

Installation

API Examples

Build Options

🤝 Contributing

📄 License

📚 Learn More

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