Graph-Code: A Graph-Based RAG System for Any Codebases

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.

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Latest News 🔥

• [NEW] MCP Server Integration: Graph-Code now works as an MCP server with Claude Code! Query and edit your codebase using natural language directly from Claude Code. Setup Guide
• [2025/10/21] Semantic Code Search: Added intent-based code search using UniXcoder embeddings. Find functions by describing what they do (e.g., "error handling functions", "authentication code") rather than by exact names.

🚀 Features

• Multi-Language Support:

Language	Status	Extensions	Functions	Classes/Structs	Modules	Package Detection	Additional Features
C++	Fully Supported	.cpp, .h, .hpp, .cc, .cxx, .hxx, .hh, .ixx, .cppm, .ccm	✓	✓	✓	✓	Constructors, destructors, operator overloading, templates, lambdas, C++20 modules, namespaces
Java	Fully Supported	.java	✓	✓	✓	-	Generics, annotations, modern features (records/sealed classes), concurrency, reflection
JavaScript	Fully Supported	.js, .jsx	✓	✓	✓	-	ES6 modules, CommonJS, prototype methods, object methods, arrow functions
Lua	Fully Supported	.lua	✓	-	✓	-	Local/global functions, metatables, closures, coroutines
Python	Fully Supported	.py	✓	✓	✓	✓	Type inference, decorators, nested functions
Rust	Fully Supported	.rs	✓	✓	✓	✓	impl blocks, associated functions
TypeScript	Fully Supported	.ts, .tsx	✓	✓	✓	-	Interfaces, type aliases, enums, namespaces, ES6/CommonJS modules
C#	In Development	.cs	✓	✓	✓	-	Classes, interfaces, generics (planned)
Go	In Development	.go	✓	✓	✓	-	Methods, type declarations
PHP	In Development	.php	✓	✓	✓	-	Classes, functions, namespaces
Scala	In Development	.scala, .sc	✓	✓	✓	-	Case classes, objects

• 🌳 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 both cloud models (Google Gemini), local models (Ollama), and OpenAI models for natural language to Cypher translation
• 🤖 OpenAI Integration: Leverage OpenAI models to enhance AI functionalities.
• 📝 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

🏗️ Architecture

The system consists of two 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

📋 Prerequisites

• Python 3.12+
• Docker & Docker Compose (for Memgraph)
• cmake (required for building pymgclient dependency)
• ripgrep (rg) (required for shell command text searching)
• For cloud models: Google Gemini API key
• For local models: Ollama installed and running
• uv package manager

Installing cmake and ripgrep

On macOS:

brew install cmake ripgrep

On Linux (Ubuntu/Debian):

sudo apt-get update
sudo apt-get install cmake ripgrep

On Linux (CentOS/RHEL):

sudo yum install cmake
sudo dnf install ripgrep
# Note: ripgrep may need to be installed from EPEL or via cargo

🛠️ Installation

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)

Configuration Options

The new provider-explicit configuration supports mixing different providers for orchestrator and cypher models.

Option 1: All Ollama (Local Models)

# .env file
ORCHESTRATOR_PROVIDER=ollama
ORCHESTRATOR_MODEL=llama3.2
ORCHESTRATOR_ENDPOINT=http://localhost:11434/v1

CYPHER_PROVIDER=ollama
CYPHER_MODEL=codellama
CYPHER_ENDPOINT=http://localhost:11434/v1

Option 2: All OpenAI Models

# .env file
ORCHESTRATOR_PROVIDER=openai
ORCHESTRATOR_MODEL=gpt-4o
ORCHESTRATOR_API_KEY=sk-your-openai-key

CYPHER_PROVIDER=openai
CYPHER_MODEL=gpt-4o-mini
CYPHER_API_KEY=sk-your-openai-key

Option 3: All Google Models

# .env file
ORCHESTRATOR_PROVIDER=google
ORCHESTRATOR_MODEL=gemini-2.5-pro
ORCHESTRATOR_API_KEY=your-google-api-key

CYPHER_PROVIDER=google
CYPHER_MODEL=gemini-2.5-flash
CYPHER_API_KEY=your-google-api-key

Option 4: Mixed Providers

# .env file - Google orchestrator + Ollama cypher
ORCHESTRATOR_PROVIDER=google
ORCHESTRATOR_MODEL=gemini-2.5-pro
ORCHESTRATOR_API_KEY=your-google-api-key

CYPHER_PROVIDER=ollama
CYPHER_MODEL=codellama
CYPHER_ENDPOINT=http://localhost:11434/v1

Get your Google API key from Google AI Studio.

Install and run Ollama:

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

# Pull required models
ollama pull llama3.2
# Or try other models like:
# ollama pull llama3
# 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

🛠️ Makefile Commands

Use the Makefile for common development tasks:

Command	Description
make help	Show this help message
make all	Install everything for full development environment (deps, grammars, hooks, tests)
make install	Install project dependencies with full language support
make python	Install project dependencies for Python only
make dev	Setup development environment (install deps + pre-commit hooks)
make test	Run unit tests only (fast, no Docker)
make test-parallel	Run unit tests in parallel (fast, no Docker)
make test-integration	Run integration tests (requires Docker)
make test-all	Run all tests including integration and e2e (requires Docker)
make test-parallel-all	Run all tests in parallel including integration and e2e (requires Docker)
make clean	Clean up build artifacts and cache
make build-grammars	Build grammar submodules
make watch	Watch repository for changes and update graph in real-time
make readme	Regenerate README.md from codebase

🎯 Usage

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.

Step 1: Parse a Repository

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

For the first repository (clean start):

cgr start --repo-path /path/to/repo1 --update-graph --clean

For additional repositories (preserve existing data):

cgr start --repo-path /path/to/repo2 --update-graph
cgr start --repo-path /path/to/repo3 --update-graph

Control Memgraph batch flushing:

# Flush every 5,000 records instead of the default from settings
cgr start --repo-path /path/to/repo --update-graph \
  --batch-size 5000

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

Step 2: Query the Codebase

Start the interactive RAG CLI:

cgr start --repo-path /path/to/your/repo

Step 2.5: Real-Time Graph Updates (Optional)

For active development, you can keep your knowledge graph automatically synchronized with code changes using the realtime updater. This is particularly useful when you're actively modifying code and want the AI assistant to always work with the latest codebase structure.

What it does:

• Watches your repository for file changes (create, modify, delete)
• Automatically updates the knowledge graph in real-time
• Maintains consistency by recalculating all function call relationships
• Filters out irrelevant files (.git, node_modules, etc.)

How to use:

Run the realtime updater in a separate terminal:

# Using Python directly
python realtime_updater.py /path/to/your/repo

# Or using the Makefile
make watch REPO_PATH=/path/to/your/repo

With custom Memgraph settings:

# Python
python realtime_updater.py /path/to/your/repo --host localhost --port 7687 --batch-size 1000

# Makefile
make watch REPO_PATH=/path/to/your/repo HOST=localhost PORT=7687 BATCH_SIZE=1000

Multi-terminal workflow:

# Terminal 1: Start the realtime updater
python realtime_updater.py ~/my-project

# Terminal 2: Run the AI assistant
cgr start --repo-path ~/my-project

Performance note: The updater currently recalculates all CALLS relationships on every file change to ensure consistency. This prevents "island" problems where changes in one file aren't reflected in relationships from other files, but may impact performance on very large codebases with frequent changes. Note: Optimization of this behavior is a work in progress.

CLI Arguments:

• repo_path (required): Path to repository to watch
• --host: Memgraph host (default: localhost)
• --port: Memgraph port (default: 7687)
• --batch-size: Number of buffered nodes/relationships before flushing to Memgraph

Specify Custom Models:

# Use specific local models
cgr start --repo-path /path/to/your/repo \
  --orchestrator ollama:llama3.2 \
  --cypher ollama:codellama

# Use specific Gemini models
cgr start --repo-path /path/to/your/repo \
  --orchestrator google:gemini-2.0-flash-thinking-exp-01-21 \
  --cypher google:gemini-2.5-flash-lite-preview-06-17

# Use mixed providers
cgr start --repo-path /path/to/your/repo \
  --orchestrator google:gemini-2.0-flash-thinking-exp-01-21 \
  --cypher ollama:codellama

Example queries (works across all supported languages):

• "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 functions that handle authentication"
• "List all TypeScript components"
• "Find Rust structs and their methods"
• "Show me Go interfaces and implementations"
• "Find all C++ operator overloads in the Matrix class"
• "Show me C++ template functions with their specializations"
• "List all C++ namespaces and their contained classes"
• "Find C++ lambda expressions used in algorithms"
• "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"

Step 3: Export Graph Data

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

Export during graph update:

cgr start --repo-path /path/to/repo --update-graph --clean -o my_graph.json

Export existing graph without updating:

cgr export -o my_graph.json

Optional: adjust Memgraph batching during export:

cgr export -o my_graph.json --batch-size 5000

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 script:

python examples/graph_export_example.py my_graph.json

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

Step 4: Code Optimization

For AI-powered codebase optimization with best practices guidance:

Basic optimization for a specific language:

cgr optimize python --repo-path /path/to/your/repo

Optimization with reference documentation:

cgr optimize python \
  --repo-path /path/to/your/repo \
  --reference-document /path/to/best_practices.md

Using specific models for optimization:

cgr optimize javascript \
  --repo-path /path/to/frontend \
  --orchestrator google:gemini-2.0-flash-thinking-exp-01-21

# Optional: override Memgraph batch flushing during optimization
cgr optimize javascript --repo-path /path/to/frontend \
  --batch-size 5000

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
cgr optimize python \
  --reference-document ./docs/coding_standards.md

# Use architectural guidelines
cgr optimize java \
  --reference-document ./ARCHITECTURE.md

# Use performance best practices
cgr 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:

• --orchestrator: Specify provider:model for main operations (e.g., google:gemini-2.0-flash-thinking-exp-01-21, ollama:llama3.2)
• --cypher: Specify provider:model for graph queries (e.g., google:gemini-2.5-flash-lite-preview-06-17, ollama:codellama)
• --repo-path: Path to repository (defaults to current directory)
• --batch-size: Override Memgraph flush batch size (defaults to MEMGRAPH_BATCH_SIZE in settings)
• --reference-document: Path to reference documentation (optimization only)

🔌 MCP Server (Claude Code Integration)

Graph-Code can run as an MCP (Model Context Protocol) server, enabling seamless integration with Claude Code and other MCP clients.

Quick Setup

claude mcp add --transport stdio graph-code \
  --env TARGET_REPO_PATH=/absolute/path/to/your/project \
  --env CYPHER_PROVIDER=openai \
  --env CYPHER_MODEL=gpt-4 \
  --env CYPHER_API_KEY=your-api-key \
  -- uv run --directory /path/to/code-graph-rag graph-code mcp-server

Available Tools

Tool	Description
index_repository	Parse and ingest the repository into the Memgraph knowledge graph. This builds a comprehensive graph of functions, classes, dependencies, and relationships.
query_code_graph	Query the codebase knowledge graph using natural language. Ask questions like 'What functions call UserService.create_user?' or 'Show me all classes that implement the Repository interface'.
get_code_snippet	Retrieve source code for a function, class, or method by its qualified name. Returns the source code, file path, line numbers, and docstring.
surgical_replace_code	Surgically replace an exact code block in a file using diff-match-patch. Only modifies the exact target block, leaving the rest unchanged.
read_file	Read the contents of a file from the project. Supports pagination for large files.
write_file	Write content to a file, creating it if it doesn't exist.
list_directory	List contents of a directory in the project.

Example Usage

> Index this repository
> What functions call UserService.create_user?
> Update the login function to add rate limiting

For detailed setup, see Claude Code Setup Guide.

📊 Graph Schema

The knowledge graph uses the following node types and relationships:

Node Types

Label	Properties
Project	{name: string}
Package	{qualified_name: string, name: string, path: string}
Folder	{path: string, name: string}
File	{path: string, name: string, extension: string}
Module	{qualified_name: string, name: string, path: string}
Class	{qualified_name: string, name: string, decorators: list[string]}
Function	{qualified_name: string, name: string, decorators: list[string]}
Method	{qualified_name: string, name: string, decorators: list[string]}
Interface	{qualified_name: string, name: string}
Enum	{qualified_name: string, name: string}
Type	{qualified_name: string, name: string}
Union	{qualified_name: string, name: string}
ModuleInterface	{qualified_name: string, name: string, path: string}
ModuleImplementation	{qualified_name: string, name: string, path: string, implements_module: string}
ExternalPackage	{name: string, version_spec: string}

Language-Specific Mappings

• C++: class_specifier, declaration, enum_specifier, field_declaration, function_definition, lambda_expression, struct_specifier, template_declaration, union_specifier
• Java: annotation_type_declaration, class_declaration, constructor_declaration, enum_declaration, interface_declaration, method_declaration, record_declaration
• JavaScript: arrow_function, class, class_declaration, function_declaration, function_expression, generator_function_declaration, method_definition
• Lua: function_declaration, function_definition
• Python: class_definition, function_definition
• Rust: closure_expression, enum_item, function_item, function_signature_item, impl_item, struct_item, trait_item, type_item, union_item
• TypeScript: abstract_class_declaration, arrow_function, class, class_declaration, enum_declaration, function_declaration, function_expression, function_signature, generator_function_declaration, interface_declaration, internal_module, method_definition, type_alias_declaration
• C#: anonymous_method_expression, class_declaration, constructor_declaration, destructor_declaration, enum_declaration, function_pointer_type, interface_declaration, lambda_expression, local_function_statement, method_declaration, struct_declaration
• Go: function_declaration, method_declaration, type_declaration
• PHP: anonymous_function, arrow_function, class_declaration, enum_declaration, function_definition, function_static_declaration, interface_declaration, trait_declaration
• Scala: class_definition, function_declaration, function_definition, object_definition, trait_definition

Relationships

Source	Relationship	Target
Project, Package, Folder	CONTAINS_PACKAGE	Package
Project, Package, Folder	CONTAINS_FOLDER	Folder
Project, Package, Folder	CONTAINS_FILE	File
Project, Package, Folder	CONTAINS_MODULE	Module
Module	DEFINES	Class, Function
Class	DEFINES_METHOD	Method
Module	IMPORTS	Module
Module	EXPORTS	Class, Function
Module	EXPORTS_MODULE	ModuleInterface
Module	IMPLEMENTS_MODULE	ModuleImplementation
Class	INHERITS	Class
Class	IMPLEMENTS	Interface
Method	OVERRIDES	Method
ModuleImplementation	IMPLEMENTS	ModuleInterface
Project	DEPENDS_ON_EXTERNAL	ExternalPackage
Function, Method	CALLS	Function, Method

🔧 Configuration

Configuration is managed through environment variables in .env file:

Provider-Specific Settings

Orchestrator Model Configuration

• ORCHESTRATOR_PROVIDER: Provider name (google, openai, ollama)
• ORCHESTRATOR_MODEL: Model ID (e.g., gemini-2.5-pro, gpt-4o, llama3.2)
• ORCHESTRATOR_API_KEY: API key for the provider (if required)
• ORCHESTRATOR_ENDPOINT: Custom endpoint URL (if required)
• ORCHESTRATOR_PROJECT_ID: Google Cloud project ID (for Vertex AI)
• ORCHESTRATOR_REGION: Google Cloud region (default: us-central1)
• ORCHESTRATOR_PROVIDER_TYPE: Google provider type (gla or vertex)
• ORCHESTRATOR_THINKING_BUDGET: Thinking budget for reasoning models
• ORCHESTRATOR_SERVICE_ACCOUNT_FILE: Path to service account file (for Vertex AI)

Cypher Model Configuration

• CYPHER_PROVIDER: Provider name (google, openai, ollama)
• CYPHER_MODEL: Model ID (e.g., gemini-2.5-flash, gpt-4o-mini, codellama)
• CYPHER_API_KEY: API key for the provider (if required)
• CYPHER_ENDPOINT: Custom endpoint URL (if required)
• CYPHER_PROJECT_ID: Google Cloud project ID (for Vertex AI)
• CYPHER_REGION: Google Cloud region (default: us-central1)
• CYPHER_PROVIDER_TYPE: Google provider type (gla or vertex)
• CYPHER_THINKING_BUDGET: Thinking budget for reasoning models
• CYPHER_SERVICE_ACCOUNT_FILE: Path to service account file (for Vertex AI)

System Settings

• MEMGRAPH_HOST: Memgraph hostname (default: localhost)
• MEMGRAPH_PORT: Memgraph port (default: 7687)
• MEMGRAPH_HTTP_PORT: Memgraph HTTP port (default: 7444)
• LAB_PORT: Memgraph Lab port (default: 3000)
• MEMGRAPH_BATCH_SIZE: Batch size for Memgraph operations (default: 1000)
• TARGET_REPO_PATH: Default repository path (default: .)
• LOCAL_MODEL_ENDPOINT: Fallback endpoint for Ollama (default: http://localhost:11434/v1)

Key Dependencies

• loguru: Python logging made (stupidly) simple
• mcp: Model Context Protocol SDK
• pydantic-ai: Agent Framework / shim to use Pydantic with LLMs
• pydantic-settings: Settings management using Pydantic
• pymgclient: Memgraph database adapter for Python language
• python-dotenv: Read key-value pairs from a .env file and set them as environment variables
• toml: Python Library for Tom's Obvious, Minimal Language
• tree-sitter-python: Python grammar for tree-sitter
• tree-sitter: Python bindings to the Tree-sitter parsing library
• watchdog: Filesystem events monitoring
• typer: Typer, build great CLIs. Easy to code. Based on Python type hints.
• rich: Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal
• prompt-toolkit: Library for building powerful interactive command lines in Python
• diff-match-patch: Repackaging of Google's Diff Match and Patch libraries.
• click: Composable command line interface toolkit
• protobuf
• defusedxml: XML bomb protection for Python stdlib modules

🤖 Agentic Workflow & Tools

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

Core Tools

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

Tool	Description
query_graph	Query the codebase knowledge graph using natural language questions. Ask in plain English about classes, functions, methods, dependencies, or code structure. Examples: 'Find all functions that call each other', 'What classes are in the user module', 'Show me functions with the longest call chains'.
read_file	Reads the content of text-based files. For documents like PDFs or images, use the 'analyze_document' tool instead.
create_file	Creates a new file with content. IMPORTANT: Check file existence first! Overwrites completely WITHOUT showing diff. Use only for new files, not existing file modifications.
replace_code	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.
list_directory	Lists the contents of a directory to explore the codebase.
analyze_document	Analyzes documents (PDFs, images) to answer questions about their content.
execute_shell	Executes shell commands from allowlist. Read-only commands run without approval; write operations require user confirmation.
semantic_search	Performs a semantic search for functions based on a natural language query describing their purpose, returning a list of potential matches with similarity scores.
get_function_source	Retrieves the source code for a specific function or method using its internal node ID, typically obtained from a semantic search result.
get_code_snippet	Retrieves the source code for a specific function, class, or method using its full qualified name.

Intelligent and Safe File Editing

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

🌍 Multi-Language Support

Adding New Languages

Graph-Code makes it easy to add support for any language that has a Tree-sitter grammar. The system automatically handles grammar compilation and integration.

⚠️ Recommendation: While you can add languages yourself, we recommend waiting for official full support to ensure optimal parsing quality, comprehensive feature coverage, and robust integration. The languages marked as "In Development" above will receive dedicated optimization and testing.

💡 Request Support: If you want a specific language to be officially supported, please submit an issue with your language request.

Quick Start: Add a Language

Use the built-in language management tool to add any Tree-sitter supported language:

# Add a language using the standard tree-sitter repository
cgr language add-grammar <language-name>

# Examples:
cgr language add-grammar c-sharp
cgr language add-grammar php
cgr language add-grammar ruby
cgr language add-grammar kotlin

Custom Grammar Repositories

For languages hosted outside the standard tree-sitter organization:

# Add a language from a custom repository
cgr language add-grammar --grammar-url https://github.com/custom/tree-sitter-mylang

What Happens Automatically

When you add a language, the tool automatically:

1. Downloads the Grammar: Clones the tree-sitter grammar repository as a git submodule
2. Detects Configuration: Auto-extracts language metadata from tree-sitter.json
3. Analyzes Node Types: Automatically identifies AST node types for:
   • Functions/methods (method_declaration, function_definition, etc.)
   • Classes/structs (class_declaration, struct_declaration, etc.)
   • Modules/files (compilation_unit, source_file, etc.)
   • Function calls (call_expression, method_invocation, etc.)
4. Compiles Bindings: Builds Python bindings from the grammar source
5. Updates Configuration: Adds the language to codebase_rag/language_config.py
6. Enables Parsing: Makes the language immediately available for codebase analysis

Example: Adding C# Support

$ cgr language add-grammar c-sharp
🔍 Using default tree-sitter URL: https://github.com/tree-sitter/tree-sitter-c-sharp
🔄 Adding submodule from https://github.com/tree-sitter/tree-sitter-c-sharp...
✅ Successfully added submodule at grammars/tree-sitter-c-sharp
Auto-detected language: c-sharp
Auto-detected file extensions: ['cs']
Auto-detected node types:
Functions: ['destructor_declaration', 'method_declaration', 'constructor_declaration']
Classes: ['struct_declaration', 'enum_declaration', 'interface_declaration', 'class_declaration']
Modules: ['compilation_unit', 'file_scoped_namespace_declaration', 'namespace_declaration']
Calls: ['invocation_expression']

✅ Language 'c-sharp' has been added to the configuration!
📝 Updated codebase_rag/language_config.py

Managing Languages

# List all configured languages
cgr language list-languages

# Remove a language (this also removes the git submodule unless --keep-submodule is specified)
cgr language remove-language <language-name>

Language Configuration

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

"language-name": LanguageConfig(
    name="language-name",
    file_extensions=[".ext1", ".ext2"],
    function_node_types=["function_declaration", "method_declaration"],
    class_node_types=["class_declaration", "struct_declaration"],
    module_node_types=["compilation_unit", "source_file"],
    call_node_types=["call_expression", "method_invocation"],
),

Troubleshooting

Grammar not found: If the automatic URL doesn't work, use a custom URL:

cgr language add-grammar --grammar-url https://github.com/custom/tree-sitter-mylang

Version incompatibility: If you get "Incompatible Language version" errors, update your tree-sitter package:

uv add tree-sitter@latest

Missing node types: The tool automatically detects common node patterns, but you can manually adjust the configuration in language_config.py if needed.

📦 Building a binary

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.

🐛 Debugging

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

🤝 Contributing

Please see CONTRIBUTING.md for detailed contribution guidelines.

Good first PRs are from TODO issues.

🙋‍♂️ Support

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

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