The GitLab Knowledge Graph (GKG) is a system designed to create structured, queryable representations of code repositories, aiming to enhance developer productivity and power AI features.

The core methodology of GKG involves several key technical components:

1. Code Parsing and Graph Generation: It constructs a Universal Call Graph by natively parsing various programming languages, including Rust, TypeScript, Python, and Ruby. This is achieved through an extensible architecture powered by the gitlab-code-parser, allowing for the easy addition of support for new languages. The output of this parsing process is a detailed graph representation of the codebase, capturing relationships such as function calls, dependencies, and architectural structures.
2. Graph Database for Querying: The generated knowledge graph is stored and queried using the Kuzu Graph Database. Kuzu is purpose-built for efficient and lightning-fast execution of complex relationship queries. This enables users to perform sophisticated analyses like tracing function calls, identifying dependencies, and exploring the overall software architecture at scale.
3. AI-Ready Integration: GKG supports the MCP Protocol, which facilitates integration with AI tools. This protocol enables AI systems to understand the underlying structure of the codebase, providing context-aware assistance, code suggestions, and enhanced analysis capabilities.
4. Developer-First Accessibility: GKG offers multiple interfaces for interaction: a powerful Command Line Interface (CLI) for automation (e.g., gkg index for indexing, gkg server start for launching the web UI), seamless IDE integration, and a web-based user interface for visual exploration. It is designed to be compatible with all major operating systems.

Key use cases for GKG include:

• Codebase RAG (Retrieval Augmented Generation): Facilitating context-aware code suggestions and analysis for AI tools via MCP Protocol integration.
• Code Navigation: Enabling comprehensive exploration of code, including finding all references, dependencies, and relationships across the entire codebase.
• Impact Analysis: Helping developers understand the potential effects of changes on other parts of the system before implementation.
• Architecture Visualization: Generating interactive graphical representations of system structure and inter-dependencies.

The project is currently in public beta, with core functionality deemed stable for evaluation, while certain features are still undergoing refinement.