repowise

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.

3,655 438 Python AGPL-3.0updated yesterday

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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.

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README.md

Five intelligence layers · Nine MCP tools · 15 languages · Multi-repo workspaces · One pip install

Hosted for teams → · Docs · Discord · Contact

_{Layers ·
Learns from you ·
Code Health ·
Refactoring ·
Benchmarks ·
Languages ·
Quickstart ·
MCP tools ·
Comparison ·
Hosted}

measure, locate, and fix what your AI ships
code health that predicts real bugs · ROC AUC 0.74 across 21 repos · 2.3× CodeScene's defects under a fixed review budget
graph-aware refactoring plans your agent can execute · up to −96% context tokens · −70% agent tool calls at answer-quality parity

_{Measured, reproducible, on public codebases. See the benchmarks ↓}

repowise demo: Claude Code querying the codebase through repowise's MCP tools, then a tour of the local dashboard

AI now writes a large and growing share of the code, and the humans accountable for it have to trust what ships. A score that says "this file is risky" isn't enough: you need to know where the risk concentrates and how to fix it.

repowise closes that loop. It indexes your codebase once and scores every file for defect risk, maintainability, and performance from 25 deterministic markers, calibrated against a real defect corpus, no LLM, in under 30 seconds ([the proof ↓](#-

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