IGraph: Code Knowledge Graph with LLM and Multimodal Search

IGraph: Code Knowledge Graph with LLM and Multimodal Search

IGraph is a powerful tool designed to enhance code understanding and interaction for developers. It converts your codebase into a structured knowledge graph, making it searchable and navigable in novel ways. By integrating Large Language Models (LLMs) and multimodal search capabilities, IGraph allows for more intuitive and efficient exploration of your code's architecture, dependencies, and functionality. This tool is particularly valuable for complex projects where understanding the intricate relationships between different code components can be challenging.

What IGraph Does

IGraph's core function is to ingest your source code and represent it as a knowledge graph. This graph captures entities like functions, classes, variables, and their relationships. Beyond static analysis, IGraph leverages LLMs to enrich this graph with semantic understanding. This means you can query your code not just by its structure, but by its intent and purpose. Furthermore, multimodal search capabilities allow for searching using various input types, bridging the gap between natural language queries and code structure.

Key Features

• Code to Knowledge Graph Conversion: Transforms source code into a structured, queryable knowledge graph.
• LLM-Powered Enrichment: Utilizes LLMs to add semantic meaning and context to code entities.
• Multimodal Search: Enables searching code using natural language, code snippets, and potentially other modalities.
• Integrated MCP Server: Includes an MCP server, facilitating seamless integration with AI assistants and agents for developers.
• Source Code Accessibility: Available on GitHub for direct access and contribution.

Who IGraph is For

IGraph is an essential tool for AI builders , software engineers working on large or complex codebases, and developers looking to integrate advanced AI capabilities into their workflow. It's ideal for those who need to quickly understand unfamiliar code, debug intricate issues, refactor existing systems, or build intelligent code assistants. If you're working with AI agents and require a robust way to represent and interact with code knowledge, IGraph provides a foundational solution.