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code-review-graph vs codebase-memory-mcp

Local-first code intelligence graph for AI coding tools: Tree-sitter AST graph + blast-radius analysis served over MCP, so agents read ~82x fewer tokens per review question. — versus — Code intelligence MCP in pure C: tree-sitter knowledge graph over 158 languages, average repo indexed in milliseconds, sub-ms queries, 10x fewer tokens. Single static binary, zero deps.

The curated verdict

Both build tree-sitter AST graphs served over MCP with blast-radius answers. code-review-graph is tuned for the review workflow (82x fewer tokens per review question); codebase-memory is the general-purpose engine with LSP-grade types.

code-review-graphcodebase-memory-mcp
Stars26k34k
Forks2.4k2.6k
LanguagePythonC
LicenseMITMIT
Last activity2 days agoyesterday
Topicscodingcoding, local
Curated connections53

code-review-graph — the curator's take

The rare benchmark-honest repo: it tells you the 528x number is the best case and its recall metric is circular. Install once, it configures 14 platforms (Codex, Claude Code, Cursor...). Reach for it on monorepos where review context is the token sink; skip on small repos where a grep costs less than the graph's own metadata.

codebase-memory-mcp — the curator's take

The performance ceiling of the code-context-server category: pure C, single static binary, the Linux kernel indexed in 3 minutes, structural queries under a millisecond — with a peer-reviewed preprint (83% answer quality, 10x fewer tokens across 31 repos) instead of vibes. Hybrid LSP adds real type resolution for the 12 languages that matter most, and 43 client surfaces means it plugs into whatever agent you run. NOT semantic search — it answers structural questions (call chains, routes, blast radius), not 'where's the code that does X'; pair it with an embedding tool for that. And note its own disclosure: it writes to your agent config files by design — audit posture is unusually good (SLSA 3, OpenSSF, per-release VirusTotal), use it.