An accurate Retrieval-Augmented Generation (RAG) system that analyzes multi-language codebases using Tree-sitter, builds comprehensive knowledge graphs, and enables natural language querying of codebase structure and relationships as well as editing capabilities.

Use the Makefile for:

• make install: Install project dependencies with full language support.
• make python: Install dependencies for Python only.
• make dev: Setup dev environment (install deps + pre-commit hooks).
• make test: Run all tests.
• make clean: Clean up build artifacts and cache.
• make help: Show available commands.

• 🌍 Multi-Language Support: Supports Python, JavaScript, TypeScript, Rust, Go, Scala, Java, C++, and C codebases
• 🌳 Tree-sitter Parsing: Uses Tree-sitter for robust, language-agnostic AST parsing
• 📊 Knowledge Graph Storage: Uses Memgraph to store codebase structure as an interconnected graph
• 🗣️ Natural Language Querying: Ask questions about your codebase in plain English
• 🤖 AI-Powered Cypher Generation: Supports multiple AI providers - Google Gemini, OpenAI, Anthropic Claude, and local models (Ollama) for natural language to Cypher translation
• 🤖 Multi-Provider Flexibility: Seamlessly switch between providers based on your needs and preferences
• 📝 Code Snippet Retrieval: Retrieves actual source code snippets for found functions/methods
• ✍️ Advanced File Editing: Surgical code replacement with AST-based function targeting, visual diff previews, and exact code block modifications
• ⚡️ Shell Command Execution: Can execute terminal commands for tasks like running tests or using CLI tools.
• 🚀 Interactive Code Optimization: AI-powered codebase optimization with language-specific best practices and interactive approval workflow
• 📚 Reference-Guided Optimization: Use your own coding standards and architectural documents to guide optimization suggestions
• 🔗 Dependency Analysis: Parses pyproject.toml to understand external dependencies
• 🎯 Nested Function Support: Handles complex nested functions and class hierarchies
• 🔄 Language-Agnostic Design: Unified graph schema across all supported languages
• 🤖 MCP Server: Model Context Protocol server for AI agent integration

• ⚡ Parallel Processing: Multi-core parallel file parsing with configurable worker pools
• 💾 Memory Optimization: Streaming parsers and memory-mapped file handling for large codebases
• 📈 Graph Indexing: Automatic index creation for 20+ common query patterns
• 🚀 Query Caching: LRU cache with configurable TTL for faster repeated queries
• 📊 Progress Reporting: Real-time progress tracking with ETA for large operations

• 🔧 C Language Support: Full support for C including:
  • Function pointers and callbacks
  • Macros and preprocessor directives
  • Structs, unions, and enums
  • Linux kernel patterns (syscalls, exports, locks)
• 🧪 Test Framework Integration: Automatic detection and parsing of tests:
  • Python: pytest, unittest
  • JavaScript/TypeScript: Jest, Mocha, Jasmine
  • C: Unity, Check, CMocka
  • Rust: cargo test
  • Go: testing package, Ginkgo
  • Java: JUnit, TestNG
• 🥒 BDD Support: Parse and link Gherkin feature files to implementations

• 🔒 Security Vulnerability Detection:
  • SQL/Command injection detection
  • XSS vulnerability scanning
  • Hardcoded secrets detection
  • Buffer overflow analysis (C/C++)
  • Taint flow tracking
• 📊 Data Flow Analysis: Track variable usage and data movement through code
• 🧬 Inheritance Analysis: Full OOP relationship tracking (inheritance, interfaces, overrides)
• 📈 Test Coverage Analysis: Find untested code and calculate coverage metrics
• 🔄 Circular Dependency Detection: Identify and visualize circular imports

• 📝 Git Repository Analysis: Full Git history integration with commit and author tracking
• 👥 Contributor Analysis: Track top contributors, commit patterns, and expertise areas
• 📅 Change History: Query files by modification date and track change frequency
• 🔍 Commit Metadata: Access commit messages, author information, and parent relationships
• 📊 Blame Information: Line-by-line authorship tracking for accountability
• 🔄 File History: Complete modification history for each file

• ⚙️ Configuration File Parsing: Support for JSON, YAML, TOML, INI, .env files
• 📦 Dependency Extraction: Extract dependencies from config files
• 🏗️ Build Script Analysis: Parse npm scripts, Makefiles, Dockerfiles, Kconfig
• 🌍 Environment Detection: Identify environment-specific configurations
• 🔗 Config References: Track relationships between configuration files
• 🔑 Secret Detection: Find API keys, tokens, and sensitive configuration

The system consists of three main components:

1. Multi-language Parser: Tree-sitter based parsing system that analyzes codebases and ingests data into Memgraph
2. RAG System (codebase_rag/): Interactive CLI for querying the stored knowledge graph
3. MCP Server (mcp_server/): Model Context Protocol server enabling AI agents to interact with the system

• Python 3.12+
• Docker & Docker Compose (for Memgraph)
• For AI providers: At least one API key from:
  • Google Gemini
  • OpenAI
  • Anthropic Claude
  • Or Ollama for local models
• uv package manager

For a complete step-by-step setup guide, see SETUP.md.

git clone https://github.com/vitali87/code-graph-rag.git

cd code-graph-rag

2. Install dependencies:

For basic Python support:

uv sync

For full multi-language support:

uv sync --extra treesitter-full

For development (including tests and pre-commit hooks):

make dev

This installs all dependencies and sets up pre-commit hooks automatically.

This installs Tree-sitter grammars for all supported languages (see Multi-Language Support section).

3. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration (see options below)

Configure one or more AI providers in your .env file:

# .env file
GEMINI_API_KEY=your_gemini_api_key_here

Get your free API key from Google AI Studio.

# .env file
OPENAI_API_KEY=your_openai_api_key_here

Get your API key from OpenAI Platform.

# .env file
ANTHROPIC_API_KEY=your_anthropic_api_key_here

Get your API key from Anthropic Console.

# .env file
LOCAL_MODEL_ENDPOINT=http://localhost:11434/v1
LOCAL_ORCHESTRATOR_MODEL_ID=llama3
LOCAL_CYPHER_MODEL_ID=llama3
LOCAL_MODEL_API_KEY=ollama

Install and run Ollama:

# Install Ollama (macOS/Linux)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull required models
ollama pull llama3
# Or try other models like:
# ollama pull llama3.1
# ollama pull mistral
# ollama pull codellama

# Ollama will automatically start serving on localhost:11434

Note: Local models provide privacy and no API costs, but may have lower accuracy compared to cloud models like Gemini.

4. Start Memgraph database:

docker-compose up -d

The Graph-Code system offers four main modes of operation:

1. Parse & Ingest: Build knowledge graph from your codebase
2. Interactive Query: Ask questions about your code in natural language
3. Export & Analyze: Export graph data for programmatic analysis
4. AI Optimization: Get AI-powered optimization suggestions for your code.
5. Editing: Perform surgical code replacements and modifications with precise targeting.

Parse and ingest a multi-language repository into the knowledge graph:

For the first repository (clean start):

python -m codebase_rag.main start --repo-path /path/to/repo1 --update-graph --clean

For additional repositories (preserve existing data):

python -m codebase_rag.main start --repo-path /path/to/repo2 --update-graph
python -m codebase_rag.main start --repo-path /path/to/repo3 --update-graph

Performance Options (New!):

# Enable parallel processing with automatic worker detection
python -m codebase_rag.main start --repo-path /path/to/repo --update-graph --parallel

# Specify number of worker processes
python -m codebase_rag.main start --repo-path /path/to/repo --update-graph --parallel --workers 8

# Process only specific folders
python -m codebase_rag.main start --repo-path /path/to/repo --update-graph --folder-filter "src,lib,tests"

# Filter files by pattern
python -m codebase_rag.main start --repo-path /path/to/repo --update-graph --file-pattern "*.py,*.js"

# Skip test files for faster processing
python -m codebase_rag.main start --repo-path /path/to/repo --update-graph --skip-tests

# Combine options for large codebases
python -m codebase_rag.main start --repo-path /path/to/linux-kernel \
  --update-graph --clean \
  --parallel --workers 16 \
  --folder-filter "drivers,kernel,fs" \
  --skip-tests

The system automatically detects and processes files for all supported languages (see Multi-Language Support section).

Start the interactive RAG CLI:

python -m codebase_rag.main start --repo-path /path/to/your/repo

You can switch between providers and models at runtime using CLI arguments:

Use specific models directly (auto-detects provider):

# Gemini models
python -m codebase_rag.main start --repo-path /path/to/your/repo \
  --orchestrator-model gemini-2.5-pro \
  --cypher-model gemini-2.5-flash-lite-preview-06-17

# OpenAI models
python -m codebase_rag.main start --repo-path /path/to/your/repo \
  --orchestrator-model gpt-4o \
  --cypher-model gpt-4o-mini

# Anthropic models
python -m codebase_rag.main start --repo-path /path/to/your/repo \
  --orchestrator-model claude-3-5-sonnet-20241022 \
  --cypher-model claude-3-5-haiku-20241022

# Local models (Ollama)
python -m codebase_rag.main start --repo-path /path/to/your/repo \
  --orchestrator-model llama3.1 \
  --cypher-model codellama

Mix providers for different tasks:

# Use Claude for orchestration, Gemini for Cypher generation
python -m codebase_rag.main start --repo-path /path/to/your/repo \
  --orchestrator-model claude-3-5-sonnet-20241022 \
  --cypher-model gemini-2.5-flash-lite-preview-06-17

Example queries (works across all supported languages):

Basic Structure Queries:

• "Show me all classes that contain 'user' in their name"
• "Find functions related to database operations"
• "What methods does the User class have?"
• "Show me all TypeScript interfaces"
• "Find Rust structs and their methods"

Security & Quality Queries:

• "Show me all SQL injection vulnerabilities"
• "Find hardcoded passwords or API keys"
• "What functions haven't been tested?"
• "Show me high-severity security issues"
• "Find buffer overflow vulnerabilities in C code"

Architecture & Dependencies:

• "Show me circular dependencies"
• "What external packages do we depend on?"
• "Find classes that inherit from BaseModel"
• "Which modules import the database layer?"
• "Show me all abstract classes"

Version Control & History:

• "Who are the top contributors to this project?"
• "What files changed in the last week?"
• "Show me the most frequently modified files"
• "Who last modified the authentication module?"

Configuration & Testing:

• "Show me all configuration files"
• "Find database connection settings"
• "List all test suites and their coverage"
• "Show me BDD scenarios for user authentication"
• "Find all npm scripts in package.json files"

Code Modification Examples:

• "Add logging to all database connection functions"
• "Refactor the User class to use dependency injection"
• "Convert these Python functions to async/await pattern"
• "Add error handling to authentication methods"
• "Optimize this function for better performance"

For programmatic access and integration with other tools, you can export the entire knowledge graph to JSON:

Export during graph update:

python -m codebase_rag.main start --repo-path /path/to/repo --update-graph --clean -o my_graph.json

Export existing graph without updating:

python -m codebase_rag.main export -o my_graph.json

Working with exported data:

from codebase_rag.graph_loader import load_graph

# Load the exported graph
graph = load_graph("my_graph.json")

# Get summary statistics
summary = graph.summary()
print(f"Total nodes: {summary['total_nodes']}")
print(f"Total relationships: {summary['total_relationships']}")

# Find specific node types
functions = graph.find_nodes_by_label("Function")
classes = graph.find_nodes_by_label("Class")

# Analyze relationships
for func in functions[:5]:
    relationships = graph.get_relationships_for_node(func.node_id)
    print(f"Function {func.properties['name']} has {len(relationships)} relationships")

Example analysis scripts:

# Basic graph analysis
python examples/graph_export_example.py my_graph.json

# Process large codebases with parallel processing
python examples/large_codebase_example.py /path/to/linux-kernel --workers 16 --folder-filter "drivers,kernel"

# Analyze C code and test coverage
python examples/c_and_test_analysis_example.py --report

This provides a reliable, programmatic way to access your codebase structure without LLM restrictions, perfect for:

• Integration with other tools
• Custom analysis scripts
• Building documentation generators
• Creating code metrics dashboards
• Processing million-line codebases efficiently
• Analyzing test coverage and quality

For AI-powered codebase optimization with best practices guidance:

Basic optimization for a specific language:

python -m codebase_rag.main optimize python --repo-path /path/to/your/repo

Optimization with reference documentation:

python -m codebase_rag.main optimize python \
  --repo-path /path/to/your/repo \
  --reference-document /path/to/best_practices.md

Using specific models for optimization:

python -m codebase_rag.main optimize javascript \
  --repo-path /path/to/frontend \
  --llm-provider gemini \
  --orchestrator-model gemini-2.0-flash-thinking-exp-01-21

Supported Languages for Optimization: All supported languages: python, javascript, typescript, rust, go, java, scala, cpp

How It Works:

1. Analysis Phase: The agent analyzes your codebase structure using the knowledge graph
2. Pattern Recognition: Identifies common anti-patterns, performance issues, and improvement opportunities
3. Best Practices Application: Applies language-specific best practices and patterns
4. Interactive Approval: Presents each optimization suggestion for your approval before implementation
5. Guided Implementation: Implements approved changes with detailed explanations

Example Optimization Session:

Starting python optimization session...
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ The agent will analyze your python codebase and propose specific          ┃
┃ optimizations. You'll be asked to approve each suggestion before          ┃
┃ implementation. Type 'exit' or 'quit' to end the session.                 ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

🔍 Analyzing codebase structure...
📊 Found 23 Python modules with potential optimizations

💡 Optimization Suggestion #1:
   File: src/data_processor.py
   Issue: Using list comprehension in a loop can be optimized
   Suggestion: Replace with generator expression for memory efficiency

   [y/n] Do you approve this optimization?

Reference Document Support: You can provide reference documentation (like coding standards, architectural guidelines, or best practices documents) to guide the optimization process:

# Use company coding standards
python -m codebase_rag.main optimize python \
  --reference-document ./docs/coding_standards.md

# Use architectural guidelines
python -m codebase_rag.main optimize java \
  --reference-document ./ARCHITECTURE.md

# Use performance best practices
python -m codebase_rag.main optimize rust \
  --reference-document ./docs/performance_guide.md

The agent will incorporate the guidance from your reference documents when suggesting optimizations, ensuring they align with your project's standards and architectural decisions.

Common CLI Arguments:

• --llm-provider: Choose gemini or local models
• --orchestrator-model: Specify model for main operations
• --cypher-model: Specify model for graph queries
• --repo-path: Path to repository (defaults to current directory)
• --reference-document: Path to reference documentation (optimization only)

The knowledge graph uses the following node types and relationships:

• Project: Root node representing the entire repository
• Package: Language packages (Python: __init__.py, etc.)
• Module: Individual source code files (.py, .js, .jsx, .ts, .tsx, .rs, .go, .scala, .sc, .java, .c, .h)
• Class: Class/Struct/Enum definitions across all languages
• Function: Module-level functions and standalone functions
• Method: Class methods and associated functions
• Folder: Regular directories
• File: All files (source code and others)
• ExternalPackage: External dependencies
• TestSuite: Test suite containers (pytest classes, Jest describe blocks, etc.)
• TestCase: Individual test cases across frameworks
• Assertion: Test assertions with expected/actual values
• BDDFeature: Gherkin feature definitions
• BDDScenario: BDD scenarios within features
• BDDStep: Given/When/Then steps
• Macro: C preprocessor macros
• Struct: C struct definitions
• GlobalVariable: C global variables
• Pointer: C pointer variables
• Typedef: C type definitions
• Syscall: Linux kernel syscall definitions
• KernelExport: Kernel exported symbols
• Vulnerability: Security vulnerabilities with severity and CWE IDs
• Author: Git commit authors with contribution statistics
• Commit: Git commits with metadata and relationships
• ConfigFile: Configuration files (YAML, JSON, INI, etc.)
• ConfigValue: Individual configuration settings

• Python: function_definition, class_definition
• JavaScript/TypeScript: function_declaration, arrow_function, class_declaration
• Rust: function_item, struct_item, enum_item, impl_item
• Go: function_declaration, method_declaration, type_declaration
• Scala: function_definition, class_definition, object_definition, trait_definition
• Java: method_declaration, class_declaration, interface_declaration, enum_declaration
• C++: function_definition, constructor_definition, destructor_definition, class_specifier, struct_specifier, union_specifier, enum_specifier
• C: function_definition, struct_specifier, union_specifier, enum_specifier, declaration (for typedefs and variables)

• CONTAINS_PACKAGE: Project or Package contains Package nodes
• CONTAINS_FOLDER: Project, Package, or Folder contains Folder nodes
• CONTAINS_FILE: Project, Package, or Folder contains File nodes
• CONTAINS_MODULE: Project, Package, or Folder contains Module nodes
• DEFINES: Module defines classes/functions
• DEFINES_METHOD: Class defines methods
• DEPENDS_ON_EXTERNAL: Project depends on external packages
• CALLS: Function or Method calls other functions/methods
• POINTS_TO: Pointer points to a variable or function
• ASSIGNS_FP: Function pointer assignment
• INVOKES_FP: Function pointer invocation
• LOCKS/UNLOCKS: Concurrency primitive usage
• EXPANDS_TO: Macro expansion relationships
• TESTS: Test case tests a function/method
• ASSERTS: Assertion validates behavior
• IN_SUITE: Test case belongs to test suite
• IN_TEST: Assertion belongs to test case
• IN_FEATURE: Scenario belongs to BDD feature
• IN_SCENARIO: Step belongs to BDD scenario
• IMPLEMENTS_STEP: Function implements BDD step
• GIVEN_LINKS_TO/WHEN_LINKS_TO/THEN_LINKS_TO: BDD step linkages
• FLOWS_TO: Data flow between variables
• INHERITS_FROM/IMPLEMENTS: OOP inheritance relationships
• OVERRIDES: Method override relationships
• HAS_VULNERABILITY: Code element has security vulnerability
• TAINT_FLOW: Tainted data flow paths
• AUTHORED_BY: Commit authored by contributor
• PARENT_OF: Commit parent relationships
• MODIFIED_IN/ADDED_IN/REMOVED_IN: File modification in commits
• CONFIGURES: Configuration file configures module
• INCLUDES_CONFIG: Configuration file includes another
• REFERENCES_CONFIG: Code references configuration

Configuration is managed through environment variables in .env file:

• GEMINI_API_KEY: Required when using Google Gemini models
• GEMINI_MODEL_ID: Main model for orchestration (default: gemini-2.5-pro)
• MODEL_CYPHER_ID: Model for Cypher generation (default: gemini-2.5-flash-lite-preview-06-17)

• OPENAI_API_KEY: Required when using OpenAI models
• OPENAI_ORCHESTRATOR_MODEL_ID: Model for orchestration (default: gpt-4o-mini)
• OPENAI_CYPHER_MODEL_ID: Model for Cypher generation (default: gpt-4o-mini)

• ANTHROPIC_API_KEY: Required when using Anthropic models
• ANTHROPIC_ORCHESTRATOR_MODEL_ID: Model for orchestration (default: claude-3-5-sonnet-20241022)
• ANTHROPIC_CYPHER_MODEL_ID: Model for Cypher generation (default: claude-3-5-haiku-20241022)

• LOCAL_MODEL_ENDPOINT: Ollama endpoint (default: http://localhost:11434/v1)
• LOCAL_ORCHESTRATOR_MODEL_ID: Model for main RAG orchestration (default: llama3)
• LOCAL_CYPHER_MODEL_ID: Model for Cypher query generation (default: llama3)
• LOCAL_MODEL_API_KEY: API key for local models (default: ollama)

• MEMGRAPH_HOST: Memgraph hostname (default: localhost)
• MEMGRAPH_PORT: Memgraph port (default: 7687)
• TARGET_REPO_PATH: Default repository path (default: .)

• tree-sitter: Core Tree-sitter library for language-agnostic parsing
• tree-sitter-{language}: Language-specific grammars (Python, JS, TS, Rust, Go, Scala, Java, C++, C)
• pydantic-ai: AI agent framework for RAG orchestration
• pymgclient: Memgraph Python client for graph database operations
• loguru: Advanced logging with structured output
• python-dotenv: Environment variable management
• tqdm: Progress bars for large operations
• psutil: Memory and system monitoring
• multiprocessing: Parallel processing support

The agent is designed with a deliberate workflow to ensure it acts with context and precision, especially when modifying the file system.

The agent has access to a suite of tools to understand and interact with the codebase:

• query_codebase_knowledge_graph: The primary tool for understanding the repository. It queries the graph database to find files, functions, classes, and their relationships based on natural language.
• get_code_snippet: Retrieves the exact source code for a specific function or class.
• read_file_content: Reads the entire content of a specified file.
• create_new_file: Creates a new file with specified content.
• replace_code_surgically: Surgically replaces specific code blocks in files. Requires exact target code and replacement. Only modifies the specified block, leaving rest of file unchanged. True surgical patching.
• execute_shell_command: Executes a shell command in the project's environment.

The agent uses AST-based function targeting with Tree-sitter for precise code modifications. Features include:

• Visual diff preview before changes
• Surgical patching that only modifies target code blocks
• Multi-language support across all supported languages
• Security sandbox preventing edits outside project directory
• Smart function matching with qualified names and line numbers

• Python: Full support including nested functions, methods, classes, and package structure
• JavaScript/TypeScript: Functions, arrow functions, classes, and method definitions
• Rust: Functions, structs, enums, impl blocks, and associated functions
• Go: Functions, methods, type declarations, and struct definitions
• Scala: Functions, methods, classes, objects, traits, case classes, and Scala 3 syntax
• Java: Methods, constructors, classes, interfaces, enums, and annotation types
• C++: Functions, classes, structs, and methods
• C: Functions, structs, unions, enums, typedefs, macros, function pointers, and kernel-specific constructs

The system uses a configuration-driven approach for language support. Each language is defined in codebase_rag/language_config.py.

The Graph-Code RAG system includes a Model Context Protocol (MCP) server that enables AI agents and LLMs to interact with codebases programmatically.

• 🔌 Standardized Protocol: Compatible with Claude Desktop, OpenAI assistants, and custom AI agents
• 🛠️ Rich Tool Set: Load repositories, query graphs, analyze security, check coverage, and more
• 📊 Real-time Analysis: Stream analysis results directly to AI conversations
• 🔒 Secure Access: Built-in security features and rate limiting
• 🚀 High Performance: Caching, parallel processing, and optimized queries

1. Install MCP dependencies:

pip install mcp>=0.9.0

2. Start the MCP server:

python -m mcp_server.server
# or use the launcher script
./mcp_server/launch.sh

3. Configure Claude Desktop (add to config):

{
  "mcpServers": {
    "code-graph-rag": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "cwd": "/path/to/code-graph-rag"
    }
  }
}

• load_repository - Load and analyze a codebase
• query_graph - Query using natural language or Cypher
• analyze_security - Find security vulnerabilities
• analyze_test_coverage - Check test coverage
• get_code_metrics - Get complexity and quality metrics
• analyze_git_history - Analyze contributors and commits
• find_code_patterns - Find patterns or anti-patterns
• export_graph - Export graph data

# AI agent analyzing code quality
async def analyze_codebase():
    # Load repository
    await mcp.load_repository("/path/to/repo")

    # Check security
    vulns = await mcp.analyze_security()

    # Get test coverage
    coverage = await mcp.analyze_test_coverage()

    # Generate recommendations
    if vulns['critical'] > 0:
        print("Fix critical security issues first!")

For detailed MCP documentation, see mcp_server/README.md.

You can build a binary of the application using the build_binary.py script. This script uses PyInstaller to package the application and its dependencies into a single executable.

python build_binary.py

The resulting binary will be located in the dist directory.

1. Check Memgraph connection:

   • Ensure Docker containers are running: docker-compose ps
   • Verify Memgraph is accessible on port 7687

2. View database in Memgraph Lab:

   • Open http://localhost:3000
   • Connect to memgraph:7687

3. For local models:

   • Verify Ollama is running: ollama list
   • Check if models are downloaded: ollama pull llama3
   • Test Ollama API: curl http://localhost:11434/v1/models
   • Check Ollama logs: ollama logs

Please see CONTRIBUTING.md for detailed contribution guidelines.

Good first PRs are from TODO issues.

For issues or questions:

1. Check the logs for error details
2. Verify Memgraph connection
3. Ensure all environment variables are set
4. Review the graph schema matches your expectations

• SETUP.md - Complete step-by-step setup guide
• ADVANCED_FEATURES.md - Comprehensive guide to all advanced features
• ADVANCED_USAGE.md - Advanced usage scenarios for large-scale analysis
• QUERY_COOKBOOK.md - Practical query examples and patterns
• MIGRATION.md - Guide for upgrading to latest features
• CHANGELOG.md - Detailed list of changes and new features
• CONTRIBUTING.md - Guidelines for contributors
• Examples - Sample scripts demonstrating features:
  • graph_export_example.py - Basic graph analysis
  • large_codebase_example.py - Parallel processing demo
  • c_and_test_analysis_example.py - C language and test analysis
  • vcs_and_config_example.py - Git history and configuration analysis
  • security_and_test_example.py - Security scanning and test coverage
  • comprehensive_analysis_example.py - All features integrated
  • kernel_analysis_example.py - Linux kernel code analysis
  • large_scale_management.py - Managing million-line codebases
  • multi_repo_analysis.py - Ecosystem and cross-repository analysis
  • performance_benchmark.py - Performance testing and optimization

• Security Analysis - Vulnerability detection and security scanning
• Test Coverage - Test analysis and coverage metrics
• Data Flow - Variable tracking and taint analysis
• Git Integration - Version control history and blame
• Configuration Parsing - Config file analysis
• Query Templates - Pre-built Cypher query templates
• Query Cookbook - Extensive collection of query examples