Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI coding-agent developers, agent harness builders, codebase understanding teams, and power users who want a cheap syntactic layer beneath where an LSP's semantics begin — `outline` a 1700-line file as a skeleton, `source` one symbol's body instead of the whole file, `map` a directory's definitions + references in one shot, and pass `symbol-id`s between turns instead of file paths. Desi
Who should use it
Who should skip it
Move on from Entelligentsia/grove if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
Entelligentsia/grove is tracked by RepoRadar as a structural code access for agent in the AI Coding section. It was first seen on 2026-07-03 and last updated on 2026-07-03. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. Across RepoRadar's eight signals, Entelligentsia/grove is strongest on workflow potential (9.3) and novelty (9.0) and weakest on momentum (6.0) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned Entelligentsia/grove a composite score of 8.2 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
grove is syntactic, not semantic — it parses with tree-sitter but does not do type inference, completion, rename, or type-resolved go-to-def. The README and docs/faq.md are explicit about this. For type-resolved cases (find a function's callers across overloads, resolve a trait method to its impl), keep an LSP-aware tool alongside grove; do not expect grove to replace an LSP; The 27 official tree-sitter grammars load at runtime from a hosted WASM registry (docs/languages.md) — this means grove depends on the registry being reachable for the first run on a new language. For air-gapped or fully-offline environments, vendor the WASM grammars locally and point grove at the local copy; for production agent harnesses, pin the grammar versions in the install; The Rust binary install path (`cargo install --git https://github.com/Entelligentsia/grove grove`) requires a Rust toolchain on the dev machine and pulls a tree-sitter build toolchain transitively; for non-Rust environments, use the release binary from the GitHub Releases page. The symbol-id format is stable across turns but assumes the file's symbol set has not changed between turns — if the agent edits the file, the symbol-id resolves to the new version (which is usually what you want, but worth knowing).