kunal12203/GrapeRoot

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

What it solves

GrapeRoot is designed to reduce the time and token cost associated with AI coding assistants. Instead of forcing the AI to spend multiple turns exploring a codebase using tool calls (like searching for symbols or tracing routes), GrapeRoot pre-loads the most relevant code context into the prompt before the AI even receives the question.

How it works

GrapeRoot acts as a launcher that sits between the user and the AI assistant. It operates through a following process:

  1. Graph Scan: It extracts files, functions, classes, and import relationships into a local semantic graph stored in the project directory.
  2. Context Retrieval: When a question is asked, the graph ranks the most relevant files and packs them into the prompt automatically.
  3. Session Memory: The system tracks which files were read, edited, or queried, weighting them higher in future turns to make subsequent interactions more efficient.
  4. MCP Tools: It provides graph-aware tools (like graph_read and graph_retrieve) for the AI to perform deeper exploration if necessary.

Who it’s for

Developers using AI coding assistants (such as Claude Code, Cursor, GitHub Copilot, or Gemini CLI) who work with large codebases and want to reduce latency, token usage, and the number of turns required to complete a task.

Highlights

  • Pre-loaded Context: Eliminates the "exploration phase" where AI assistants typically spend turns searching for code.
  • Broad Tool Support: Compatible with a wide range of AI tools including Claude Code, Cursor, and OpenAI Codex CLI.
  • Local Processing: All semantic graph processing is performed locally on the machine.
  • Capstone Performance: Benchmarks show significant cost reductions (up to 81% for architecture design) and a reduction in average turns per task from 11.7 to 3.5.

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