Ross ROSS = Recommend OSS · open-source software intelligence for agents

kunal12203/GrapeRoot

Compounding Context for AI Coding Assistants — MCP graph engine for Claude Code, Cursor, Copilot, Gemini, OpenCode observed · 2026-09-03

github.com/kunal12203/GrapeRoot · homepage · PowerShell · Apache-2.0 (permissive) observed · 2026-09-03

Health v2 · maintenance only

73/100

  • Activity 100
  • Release rhythm 72
  • Longevity 13
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0
  • age_days: 193
  • days_rel: 186
  • days_push: 0
  • n_releases_24m: 4

Full methodology

Adoption not part of the score

1033 stars · 123 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

GrapeRoot is an open-source context engine and MCP server that builds a semantic graph of a codebase (files, symbols, imports, call chains) and pre-loads relevant code into AI coding assistant prompts. It runs 100% locally and integrates with Claude Code, Cursor, Copilot, Codex, Gemini, and OpenCode to reduce token waste and exploration turns.

Use cases

  • reduce token usage when using Claude Code on a large codebase
  • give my AI coding assistant better codebase context
  • stop my AI assistant from re-exploring files every turn
  • index my project so Cursor or Copilot finds the right files
  • run a local MCP server that maps my codebase
  • track which files my AI assistant read and edited in a session
  • speed up multi-turn AI coding sessions on a monorepo

When to choose

  • you use AI coding assistants (Claude Code, Cursor, Copilot, Codex, Gemini) on medium-to-large codebases
  • you want to cut token costs and exploration overhead in AI coding sessions
  • you need a fully local, privacy-preserving context layer with no cloud dependency
  • you want zero-config setup that auto-connects to your AI tool

When to avoid

  • you don't use AI coding assistants or MCP-compatible tools
  • you need cross-repo, branch-aware, or org-scale features available only in the paid tiers
  • your project uses languages outside its supported parser set
  • you prefer your AI assistant to explore the codebase itself rather than receive pre-loaded context

Facets

cli-tool · maturity active

mcp search-engine rag developer-tools parser developer-tools large-language-models programming-languages windows cli python context-engine mcp-server code-graph token-optimization ai-coding-assistants semantic-graph local-first claude-code cursor copilot ai-agents macos linux nodejs

5 sources

Member repositories

RepositoryRoleHealth v2
kunal12203/GrapeRootmain73

For agents

markdown · JSON · MCP: product_card(name="kunal12203/GrapeRoot")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem