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
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 ↓).
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.
| Layer | What it gives you | Edge |
|---|---|---|
| ◈ Graph | tree-sitter dependency graph across 15 languages · two-tier file + symbol nodes · 3-tier call resolution · Leiden communities · PageRank / centrality / execution flows · framework-aware route→handler edges | A real graph most tools never build |
| ◈ Git | hotspots (churn × complexity) · ownership % · co-change pairs (hidden coupling) · bus factor · contributor profiles · module health · reviewer suggestions | Behavioral signals static analysis can't see |
| ◈ Docs | LLM-generated wiki per module/file · incremental on every commit · freshness + confidence scoring · hybrid RAG search (FTS + vector via RRF) | Stays current — rebuilt every commit |
| ◈ Decisions | architectural decisions mined from 8 sources, evidence-backed (verified / fuzzy / unverified), linked to graph nodes, connected by supersedes/refines/conflicts_with edges, tracked for staleness |
★ Captured nowhere else |
| ★ Code Health | 25 deterministic biomarkers, 1–10 score per file · defect-calibrated weights · coverage ingestion · trend alerts · refactoring targets · zero LLM, <30s | ★ Defect-validated — our edge ↓ |
Full deep-dive on every layer (graph, git, docs, decisions, hooks, auto-sync, dead code, CLAUDE.md generation): docs/INTELLIGENCE_LAYERS.md →
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 alertsDoes 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:
| Axis (head-to-head, paired tests) | repowise | Leading commercial tool |
|---|---|---|
| Recall @ 20%-of-lines budget | 0.173 | 0.074 |
| Effort-aware ranking (Popt) | 0.607 | 0.462 |
| Defect density, size-normalized (defects/KLOC, Alert:Healthy) | 2.18× | 0.56× |
| Discrimination (ROC AUC) | 0.731 | 0.705 |
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
Reproducible, on public codebases — repowise-bench →
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):
−70% tool calls · −89% file reads · −36% cost per query · answer quality at parity
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
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 →
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.
15 languages parsed to AST · 9 at the Full tier · framework-aware across all of them.
Full tier
Good tier
| Tier | Languages | What works |
|---|---|---|
| Full | Python · TypeScript · JavaScript · Java · Kotlin · Go · Rust · C++ · C# | AST parsing, import resolution, named bindings, call resolution, heritage extraction, docstrings; multi-project workspace resolvers; framework-aware edges; per-language dynamic-hint extractors; code-health biomarkers |
| Good | C · Ruby · Swift · Scala · PHP | AST parsing, import resolution, named bindings, call resolution, heritage (mixins / derive / extensions / traits), docstrings; dedicated workspace-aware resolvers; Rails / Laravel / TYPO3 framework edges; dynamic-hint extractors |
| Config / data | OpenAPI · Protobuf · GraphQL · Dockerfile · Makefile · YAML · JSON · TOML · SQL · Terraform · Markdown · Shell | Included in the file tree; special handlers extract endpoints / targets where applicable |
| Git-blame only | Objective-C · Elixir · Erlang · Dart · Zig · Julia · Clojure · Haskell · OCaml · F# · … | Tracked in git history (blame, hotspots, co-change); no AST parsing yet |
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 →
| Start here | |
|---|---|
| Individual developers | pip install repowise → repowise init → query from Claude Code in minutes. 100% local, BYO API key, free under AGPL-3.0. |
| Teams | repowise.dev hosted — zero ops, hosted MCP endpoint, auto re-index on every commit, plus the free Repowise PR Bot that comments on hotspots, hidden coupling, and declining health per PR. |
| Enterprises | On-prem topology, SSO/SCIM, RBAC, CVE-aware security layer, workflow integrations, and commercial licensing (no AGPL obligation) — see docs/COMMERCIAL.md. |
pip install repowise # or: uv tool install repowisecd your-project
repowise init # builds all five intelligence layers (one-time)
repowise serve # starts MCP server + local dashboardcd 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 pagesrepowise 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-onlyfor 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 · Workspaces · Auto-Sync · Config
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.
| Tool | What only this tool answers |
|---|---|
get_overview() |
Architecture summary, module map, entry points, git health, community summary. First call on any unfamiliar codebase. |
get_answer(question) |
Hybrid retrieval (FTS + vector via RRF) + PageRank bias + 1-hop graph expansion → a cited answer with calibrated retrieval_quality. Returns structured best_guesses on low confidence. Collapses search → read → reason into one round-trip. |
get_context(targets, include?) |
Triage card for files / modules / symbols: title, summary, signatures, hotspot bit, governing_decisions, and symbol_ids. include opens callers/callees, ownership, metrics, decisions, full_doc. Batch many targets. |
get_symbol("file.py::Name") |
Raw source bytes for one indexed symbol with exact line bounds — cheaper and safer than Read + offset math. |
search_codebase(query, kind?) |
Semantic search over the wiki, filterable by kind (implementation / test / config / doc), tagging each result's search_method. |
get_risk(targets, changed_files?) |
Hotspot scores, dependents, co-change partners, ownership, test gaps, security signals. Pass changed_files for PR mode → a directive block (will_break, missing_cochanges, missing_tests, governance_risk). |
get_why(query?, targets?) |
Architectural decision records, status, evidence spans, and the supersession lineage chain. Falls back to git archaeology when no ADRs exist. |
get_dead_code(...) |
Unreachable code by confidence tier with cleanup-impact estimates; cross-repo consumer detection in workspace mode. |
get_health(targets?, include?) |
25-biomarker scores per file. Dashboard mode → KPIs + lowest-scoring files + module rollup; targeted mode → per-file findings. include: coverage, refactoring, trend. |
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 →
| repowise | Google Code Wiki | DeepWiki | Swimm | CodeScene | |
|---|---|---|---|---|---|
| Self-hostable, open source | ✅ AGPL-3.0 | ❌ cloud only | ❌ cloud only | ❌ Enterprise only | ✅ Docker |
| Private repo — no cloud | ✅ | ❌ in development | ❌ OSS forks only | ✅ Enterprise tier | ✅ |
| Auto-generated documentation | ✅ | ✅ Gemini | ✅ | ✅ PR2Doc | ❌ |
| MCP server for AI agents | ✅ 9 tools | ❌ | ✅ 3 tools | ✅ | ✅ |
| Proactive agent hooks | ✅ Claude + Codex hooks | ❌ | ❌ | ❌ | ❌ |
Auto-generated AI instructions (CLAUDE.md, AGENTS.md) |
✅ | ❌ | ❌ | ❌ | ❌ |
| Code health score (1–10) | ✅ 25 biomarkers | ❌ | ❌ | ❌ | ✅ 25–30 |
| Brain Method / LCOM4 / god class | ✅ | ❌ | ❌ | ❌ | ✅ |
| Test-coverage intelligence | ✅ LCOV/Cobertura/Clover | ❌ | ❌ | ❌ | ❌ |
| Untested-hotspot detection | ✅ coverage × hotspot | ❌ | ❌ | ❌ | ❌ |
| Health trend + declining alerts | ✅ rolling snapshots | ❌ | ❌ | ❌ | ✅ |
| Refactoring recommendations | ✅ deterministic | ❌ | ❌ | ❌ | ✅ |
| Git intelligence (hotspots, ownership, co-change) | ✅ | ❌ | ❌ | ❌ | ✅ |
| Bus factor analysis | ✅ | ❌ | ❌ | ❌ | ✅ |
| Dead code detection | ✅ | ❌ | ❌ | ❌ | ❌ |
| Architectural decision records | ✅ | ❌ | ❌ | ❌ | ❌ |
| Multi-repo workspace intelligence | ✅ co-changes, contracts, federated MCP | ❌ | ❌ | ❌ | ❌ |
| Local dashboard | ✅ | ❌ | ❌ | ❌ IDE only | ✅ |
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.
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 →
- 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.
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 doctor # check setup, API keys, store driftrepowise 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.
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.
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 hello@repowise.dev.
Built for engineers who got tired of watching their AI agent cat the same file for the fourth time.
repowise.dev · Explore → · Discord · X · hello@repowise.dev