repowise-dev/repowise

Source: https://github.com/repowise-dev/repowise

The intelligence layer that gives your AI agent context, ownership, decisions — and a code-health score proven to predict real bugs.

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

Hosted for teams → · Docs · Discord · Contact

_{Layers · Code Health · Benchmarks · Languages · Quickstart · MCP tools · Comparison · Hosted}

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Your AI coding agent reads files. It doesn’t know which ones change together, which ones are dead, or why they were built the way they were. It has the source code and no memory of how the codebase got there.

repowise fixes that. It indexes your codebase into five intelligence layers — dependency graph, git history, auto-generated docs, architectural decisions, and code health — and exposes them to Claude Code, Codex, and any MCP-compatible agent through nine task-shaped tools. The result: your agent answers “why does auth work this way?” instead of “here is what auth.ts contains” — with fewer tool calls, fewer file reads, and lower cost per query, at comparable answer quality (benchmarks ↓).

The five layers

repowise runs once, builds everything, then keeps it in sync on every commit. Each layer is queryable from the CLI, the MCP tools, and the local dashboard.

Full deep-dive on every layer (graph, git, docs, decisions, hooks, auto-sync, dead code, CLAUDE.md generation): docs/INTELLIGENCE_LAYERS.md →

★ Code Health — the layer nobody else nails

Code health is repowise’s deepest differentiator — the one layer with no real equivalent, and the only one we can prove predicts real bugs.

repowise scores every file 1–10 from 25 deterministic biomarkers — McCabe complexity, deep nesting, brain methods, class cohesion (LCOM4), god classes, native Rabin–Karp clone detection, untested hotspots, function-level churn, code-age volatility, ownership dispersion, change entropy, co-change scatter, prior-defect history, test-quality smells, and more.

Zero LLM calls. Zero cloud requirement. Zero new runtime dependencies. Pure Python over tree-sitter + git data — finishes in under 30 seconds on a 3,000-file repo. The biomarker weights are calibrated against a real defect corpus, not hand-tuned; only the learned constants ship and the runtime stays fully deterministic.

repowise health                       # KPIs + lowest-scoring files
repowise health --coverage cov.lcov   # ingest LCOV/Cobertura/Clover → untested-hotspot
repowise health --refactoring-targets # ranked by impact / effort
repowise health --trend               # snapshots + declining / predicted-decline alerts

Does the score actually find bugs? Yes — and it out-ranks the leading commercial code-health tool. On the same 2,770 files across 9 languages, scored at the same leakage-free commit against the same defect labels:

Ranking by repowise health surfaces 2.3× the defects under a fixed review budget (Popt Δ +0.144, recall Δ +0.098, density Δ p = 0.003 — all paired, significant). Full methodology & CIs →

User guide & per-biomarker reference: docs/CODE_HEALTH.md

Benchmarks

Reproducible, on public codebases — repowise-bench →

1 · Agent efficiency — repowise does the exploration once, offline

Most of a coding agent’s spend goes to exploration — greping for symbols, reading candidate files, re-reading them as context grows. repowise does that work once so the agent skips it on every query. Paired SWE-QA runs on real repositories (same model, same harness, with vs without repowise’s MCP tools):

Best case shown; across the two benchmarks the range is −49% to −70% tool calls, −69% to −89% file reads, and −29% to −36% cost. Bonus: feeding an agent a commit via get_context costs 2,391 tokens vs 64,039 for the raw changed files — ~27× fewer. Reports: flask48 · sklearn48

2 · Distill — index-aware output distillation

Most of what an agent reads from a shell command is noise: 300 lines of passing tests around 4 failures, full commit bodies for “what changed recently”. repowise distill <cmd> compresses command output before the agent reads it — errors-first, exit code preserved, and every omission reversible via an inline [repowise#<ref>] marker (repowise expand <ref>). Paired runs on a public OSS repo, per command:

Small outputs pass through untouched (net-positive guard), and in an end-to-end spot-check the agent reached the identical root-cause diagnosis from distilled output as from raw. Opt-in Claude Code hook rewrites noisy commands automatically (shown for approval); repowise saved tracks tokens and dollars saved. Full guide: docs/DISTILL.md →

3 · Code health predicts real defects

Health scores are collected at a historical commit (T0); bug-fixing commits are counted over the following 6 months; the two are correlated — strictly no leakage. Across 21 open-source repositories spanning all 9 Full-tier languages:

• Cross-project mean ROC AUC 0.74 [95% CI 0.68–0.79] at identifying the files that go on to receive bug-fixes — up to 0.90 on individual repos.
• Survives controlling for file size (partial Spearman ρ = −0.16) — it is not just “flag the big files.”
• Significantly out-discriminates recent churn (+0.10 AUC) and prior-defect history (+0.12 AUC), DeLong p < 1e-9.
• Holds up on an external published dataset it has never seen (PROMISE/jEdit CK-metrics: AUC 0.76–0.78, within ~0.03 of the dataset’s own tuned model).

Full report: health-defect/BENCHMARK_REPORT.md →

Local dashboard

repowise serve starts a full web UI alongside the MCP server — no separate setup.

Highlights: Chat (natural-language Q&A) · Docs (wiki with Mermaid + graph sidebar) · Graph (interactive, 2,000+ nodes, community coloring, path finder) · C4 Architecture (Context → Containers → Components) · Risk (hotspots, ownership heatmap, module health, dead code, blast radius) · Contributors (per-author profiles) · Decisions (evidence drawer, evolution timeline, decision-graph) · Health (biomarker scores, coverage, trends) · Security (local pattern scan) · Costs · Workspace (cross-repo contracts & co-changes). Full view-by-view list in docs/USER_GUIDE.md.

Supported languages

15 languages parsed to AST · 9 at the Full tier · framework-aware across all of them.

Full tier  

Good tier    · Partial  

Adding a language needs one .scm query file and one config entry — no changes to the parser core. Full per-language matrix, code-health checklist, and the contributor recipe: docs/LANGUAGE_SUPPORT.md →

Who it’s for

Quickstart

pip install repowise          # or: uv tool install repowise

Single repo

cd your-project
repowise init        # builds all five intelligence layers (one-time)
repowise serve       # starts MCP server + local dashboard

Multi-repo workspace

cd my-workspace/     # parent dir containing backend/, frontend/, shared-libs/
repowise init .      # scans for git repos, indexes each, runs cross-repo analysis
repowise serve       # workspace dashboard + per-repo pages

repowise init automatically registers the MCP server, installs a PostToolUse hook in ~/.claude/settings.json, generates .mcp.json at the project root, and offers a post-commit hook that keeps everything in sync. If the Codex CLI is installed and logged in, interactive runs also offer to write project-local .codex/config.toml, .codex/hooks.json, and a managed AGENTS.md; non-interactive runs require --codex. Skip Codex setup with --no-codex; force or skip AGENTS.md with --agents / --no-agents.

Claude Code plugin. Prefer a one-command setup? Install the plugin from the marketplace — it registers the MCP server and hook and adds /repowise:* slash commands (init, health, risk, dead-code, decision, …):

/plugin marketplace add repowise-dev/repowise
/plugin install repowise@repowise

To add the MCP server to another editor manually:

{
  "mcpServers": {
    "repowise": { "command": "repowise", "args": ["mcp", "/path/to/your/project"] }
  }
}

Init time: the graph, git, dead-code, and code-health layers build in minutes with zero LLM calls — run repowise init --index-only for a queryable index almost immediately. The one-time cost is the documentation layer (LLM-generated wiki pages, can run in the background). After that, every commit-triggered update takes under 30 seconds and only regenerates the pages your change touched.

Docs: Quickstart · User Guide · CLI Reference · Codex · MCP Tools · Distill · Workspaces · Auto-Sync · Config

Nine MCP tools

Most tools are designed around data entities — one module, one file, one symbol — forcing agents into long chains of sequential calls. repowise tools are designed around tasks: pass multiple targets in one call, get complete context back. Every response carries an _meta envelope with index_age_days, indexed_commit, and a stale_warning that fires only when the indexed HEAD diverges from live .git/HEAD.

Worked example (“Add rate limiting to all API endpoints” in 5 calls instead of ~30 greps+reads) and the full reference: docs/MCP_TOOLS.md →

How it compares

repowise is the intersection: behavioral git intelligence + a defect-validated code-health score + auto-generated docs + agent-native MCP + architectural decisions + multi-repo workspace intelligence — self-hostable and open source. Detailed breakdown: docs/COMPETITIVE_ANALYSIS.md.

For teams & enterprises

repowise.dev is the same engine, fully managed — at feature parity with self-hosted: every CLI command, every MCP tool, the full dashboard. We dogfood it on our own codebase: live snapshot → · explore public repos →.

On top of self-hosting:

• Zero ops — managed deploys & webhooks, auto re-index on every commit.
• Hosted MCP endpoint — point any MCP client at one URL, no local server.
• Repowise PR Bot — free GitHub App, one deterministic comment per PR (hotspot touches, hidden coupling, declining health, dead code), zero LLM calls. Install → · Learn more →
• CVE-aware security layer, cross-repo intelligence at scale, and integrations (Slack, Jira/Linear, Confluence/Notion, PagerDuty) (rolling out).

What’s GA / in development / planned, on-prem topology, SSO/SCIM/RBAC, and pricing: docs/COMMERCIAL.md · Get in touch →

Privacy

• Self-hosted: your code never leaves your infrastructure. No telemetry. No analytics.
• BYOK: bring your own Anthropic / OpenAI key. We never see your LLM calls. Zero data retention via Anthropic’s API policy.
• What’s stored: the NetworkX graph, LanceDB embeddings (non-reversible vectors), generated wiki pages, git metadata. Raw source is processed transiently and never persisted.
• Fully offline: Ollama + a local embedding model = zero external API calls.

CLI & configuration

repowise init [PATH]      # index codebase (one-time; --index-only skips LLM)
repowise serve [PATH]     # MCP server + local dashboard
repowise update [PATH]    # incremental update (<30s; --workspace for all repos)
repowise query "<q>"      # ask anything from the terminal
repowise health           # code-health KPIs + lowest-scoring files
repowise risk main..HEAD  # score a branch / PR range for defect risk
repowise dead-code        # unreachable-code report
repowise distill pytest   # compact errors-first output (reversible) — saves 60–90% tokens
repowise saved            # tokens & dollars saved by distillation
repowise doctor           # check setup, API keys, store drift

repowise init generates .repowise/config.yaml (provider, model, embedder, reasoning mode, exclude patterns, git commit depth). Full command set: docs/CLI_REFERENCE.md · config reference: docs/CONFIG.md.

Contributing

git clone https://github.com/repowise-dev/repowise
cd repowise
uv sync --all-packages
uv run repowise --version
uv run pytest tests/unit/

Full guide, including how to add languages and LLM providers: CONTRIBUTING.md.

License

AGPL-3.0. Free for individuals, teams, and companies using repowise internally.

For commercial licensing — the enterprise security & compliance layer, SSO/SCIM, RBAC, workflow integrations, priority support and SLA, or embedding repowise in a product without AGPL obligations — see docs/COMMERCIAL.md or contact [email protected].