kunal12203/GrapeRoot

Compounding Context for AI Coding Assistants — MCP graph engine for Claude Code, Cursor, Copilot, Gemini, OpenCode

What it solves

GrapeRoot is a context engine designed to reduce the amount of time and tokens AI coding assistants spend exploring a codebase. Instead of requiring the AI to manually search for symbols or trace call chains through multiple tool calls, GrapeRoot pre-loads the most relevant code into the prompt before the AI even sees the question, allowing it to start reasoning immediately.

How it works

  1. Graph Scan: The tool scans the project to build a local semantic graph of files, functions, classes, and import relationships.
  2. Context Retrieval: When a user asks a question, the engine ranks the most relevant files based on the graph and packs them into the prompt.
  3. Session Memory: The system tracks which files have been read, edited, or queried, weighting them higher in subsequent turns to compound token savings.
  4. MCP Tools: It provides Model Context Protocol (MCP) tools (like graph_read and graph_neighbors) for the AI to perform deeper exploration if needed.

Who it’s for

Developers using AI coding assistants (such as Claude Code, Cursor, GitHub Copilot, or Gemini CLI) who want to reduce latency, lower API costs, and improve the quality of AI-generated code by providing better initial context.

Highlights

  • Pre-loaded Context: Eliminates the "exploration phase" where AI assistants typically spend multiple turns searching for code.
  • Broad Tool Support: Integrates with a wide range of AI tools including Claude Code, Cursor, and OpenAI Codex CLI.
  • Local Processing: All codebase scanning and graph building happens locally on the user's machine.
  • Detailed Benchmarks: Claims significant cost reductions (up to 81% for architecture design) and reduced average turns per task (from 11.7 to 3.5).
  • Multi-language Support: Supports a wide array of languages including TypeScript, Python, Go, Rust, Java, and C#.

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