repowise vs tokensave
Codebase intelligence for AI and humans: deterministic code-health scores calibrated on real defects, graph-aware refactoring plans agents can execute, auto-docs and git analytics over 9 MCP tools. — versus — Code-intelligence MCP server for coding agents — a pre-indexed semantic graph (libSQL + FTS5) they query instead of grepping: symbols, callers, impact radius in one call. 100% local, 50+ languages.
Both are code-intelligence MCP servers that cut agent context burn; tokensave answers structural questions (symbols, callers, impact) from a semantic graph, repowise layers health scoring and executable refactoring plans on top of its own graph.
| repowise | tokensave | |
|---|---|---|
| Stars | 3.7k | 440 |
| Forks | 438 | 38 |
| Language | Python | Rust |
| License | AGPL-3.0 | MIT |
| Last activity | 2 days ago | yesterday |
| Topics | coding | coding, local |
| Curated connections | 3 | 6 |
repowise — the curator's take
The interesting bet: defect-risk scoring with NO LLM — 25 deterministic markers calibrated against a real defect corpus (published ROC AUC 0.74), indexed in seconds, then the same dependency graph generates concrete refactoring plans (split the god class, break the cycle) your coding agent executes. Health→locate→fix as one loop is what linters and dashboards don't do. NOT a pure-open play: AGPL-3.0 with a hosted-teams funnel, and benchmark claims are self-published — reproduce them on your repo before quoting them; overlaps only partially with symbol-level code-graph MCPs.
tokensave — the curator's take
Use it to stop agents burning tokens on grep/glob/read exploration: one MCP call returns the symbols, relationships and snippets a task needs, and it's local (libSQL, no cloud). Broad reach — 50+ languages, 12+ agent integrations. NOT worth it on small repos where a couple of greps suffice, and it's an index you must keep fresh (re-index on change) — a stale graph misleads the agent. 80+ tools is a lot of surface; most tasks touch a handful.