Overview

Graphify transforms any code project, documentation set, or even multimedia collection into a powerful, queryable knowledge graph. Seamlessly integrated with popular AI coding assistants like Claude Code, Gemini CLI, and GitHub Copilot CLI, Graphify allows you to interact with your entire codebase—including application logic, database schemas, and infrastructure—as a unified, intelligent entity.

Key Features

• Universal Knowledge Graph: Ingests and connects code, SQL schemas, R scripts, shell scripts, documentation (Markdown, HTML, PDFs), research papers, images, and videos into a single, queryable graph.
• AI Assistant Integration: Works out-of-the-box with a wide range of AI coding assistants, enabling instant project-wide context understanding. Simply type /graphify . in your assistant.
• Rich Output: Generates an interactive graph.html for visual exploration, a GRAPH_REPORT.md summarizing key insights and connections, and a graph.json for programmatic access.
• Deep Code Analysis: Leverages 36 Tree-sitter grammars for precise Abstract Syntax Tree (AST) extraction of code, plus specialized parsers for Terraform, Salesforce Apex, and more.
• Beyond Code: Extends to handle office documents (.docx, .xlsx), Google Workspace files, PDFs, and even video/audio through transcription.
• Intelligent Insights: Identifies "god nodes" (central concepts), surprising cross-module connections, and extracts explanatory context from comments and docstrings.
• Suggested Queries: Provides tailored questions that the knowledge graph is uniquely positioned to answer, guiding further exploration.
• Confidence Scoring: Each inferred relationship is tagged with its origin (EXTRACTED, INFERRED, AMBIGUOUS), offering transparency into the graph's construction.
• Command-Line Interface (CLI): Offers flexible commands for building, updating, exporting call flows, and installing platform-specific integrations.

Typical Use Cases

• Accelerated Onboarding: Quickly understand complex codebases by querying project-wide relationships and core concepts.
• Impact Analysis: Identify how changes in one part of the system might affect others by tracing dependencies through the graph.
• Architectural Review: Gain a high-level overview of project structure, identifying key components and their interdependencies.
• Knowledge Discovery: Uncover hidden connections between disparate pieces of information, such as code logic explained by a research paper or documentation.
• Debugging and Troubleshooting: Pinpoint the root cause of issues by querying relationships between error messages, relevant code modules, and configuration files.
• Documentation Enhancement: Automatically generate interactive wikis and reports from your project's knowledge graph.
• AI-Powered Development: Empower your AI coding assistant with comprehensive project context for more accurate and insightful assistance.