Repowise

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

<a href="https://www.repowise.dev/#contact">Hosted for teams →</a> · <a href="https://docs.repowise.dev">Docs</a> · <a href="https://discord.gg/cQVpuDB6rh">Discord</a> · <a href="mailto:hello@repowise.dev">Contact</a>

<a href="#the-five-layers">Layers</a> · <a href="#-code-health--the-layer-nobody-else-nails">Code Health</a> · <a href="#refactoring-intelligence">Refactoring</a> · <a href="#benchmarks">Benchmarks</a> · <a href="#supported-languages">Languages</a> · <a href="#quickstart">Quickstart</a> · <a href="#nine-mcp-tools">MCP tools</a> · <a href="#how-it-compares">Comparison</a> · <a href="#for-teams--enterprises">Hosted</a>

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measure, locate, and fix what your AI ships code health that predicts real bugs  ·  ROC AUC 0.74  ·  2.3× the commercial market leader 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 — <a href="#benchmarks">see the benchmarks ↓</a>

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</div>

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 biomarkers — calibrated against a real defect corpus, no LLM, in under 30 seconds (the proof ↓). The same index then locates the risk through a real dependency graph and git history, and generates the fix: concrete, graph-aware refactoring plans — split this god class, move this method, break this dependency cycle, dedup this clone — that your coding agent can execute.

And because it is all one index, your agent gets the rest for free: five intelligence layers — dependency graph, git history, auto-generated docs, architectural decisions, and code health — exposed to Claude Code, Codex, and any MCP-compatible agent through nine task-shaped tools. 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 ↓). One index: context your agent can use, signals your team can trust, and the fix it can apply.

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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.

| 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) · selectable wiki styles (comprehensive / reference / tutorial / caveman) | 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 per file · three signals: defect risk · maintainability · performance · coverage ingestion · trend alerts · concrete graph-aware refactoring plans (Extract Class / Helper / Move Method / Break Cycle) · zero LLM, <30s | ★ Defect-validated, with the fix attached — our edge ↓ |

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

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★ 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. It runs as a loop: measure every file across three signals, locate where the risk concentrates through the graph and git history, then fix it with a concrete refactoring plan your agent can execute.

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.

Three signals, one index. The headline 1–10 is defect risk — the defect-calibrated, bug-predictive score in the table below. From the same biomarker stream, repowise surfaces two co-equal companion views: maintainability (cohesion, brain methods, primitive obsession, DRY and god-class smells that raise change-cost without predicting bugs) and performance (static I/O-in-loop / N+1 risk, including cross-function cases caught through the call graph). The two companions are separate lenses — never blended into the defect headline, so the bug-predictive number stays clean.

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

And it proves itself on your repo, not just a benchmark: after every index, repowise checks its own flags against your git history and reports the hit rate in the terminal and on the dashboard — "16/20 lowest-health files had a bug fix in the last 6 months, 3.3x the 24% baseline". See Does the score find the bugs?.

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:

| 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

Refactoring intelligence

A health score tells you a file is in trouble. Every other tool stops there, or prints the same static sentence for every god class in every repo. repowise names the specific fix, computed deterministically from the graph, the class model, and git co-change — the same data the score is built on:

Extract Class — split an incohesive class along its real cohesion groups

(LCOM4 components): "these methods + these fields belong together; these don't."

Extract Helper — dedup a clone across its exact occurrences, with the

shared helper placed at the community centroid of the files involved.

Move Method — relocate a feature-envy method to the class it actually talks

to (Jaccard distance over the call graph), not the one it lives in.

Break Cycle — name the minimal set of import edges to invert to break a

dependency cycle (greedy minimum feedback arc set over the real import graph).

Every plan carries its blast radius (the callers and co-changing files that must move with it) and is ranked graph-aware — impact × call-graph centrality × blast radius, so a fix on a central hub outranks the same fix on a leaf. That is the wedge: the leading commercial tool ranks by churn alone, stays within-function, and ignores its own coupling signal when it generates code.

The deterministic plan is the product; an LLM is strictly opt-in and never in the indexing hot path. Turn it on and any plan can be expanded into generated code plus a unified diff, fed the graph and co-change context a bare codegen tool throws away.

repowise health --refactoring-targets        # ranked plans, biggest win for least effort

get_health(include=["refactoring"])           # the same structured plans over MCP

The web Refactoring tab renders each plan as a card — the split groups as a tree, the move arrow, the clone occurrences with line links — with a copy-to-agent button and the opt-in Generate code diff view. Full reference: docs/REFACTORING.md

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Change risk & agent provenance

Two more deterministic signals, built on the same graph and git history, for the people who have to trust what ships:

★ Change risk: score any commit or base..HEAD range 0–10 for defect

risk from the shape of the diff (Kamei-style just-in-time metrics), with PR-mode directives (will_break, missing_cochanges, missing_tests). One command: repowise risk main..HEAD. Reference: docs/CHANGE_RISK.md.

★ Agent provenance: attribute commits to the AI agents that wrote them,

straight from git history, so you can see how much of your codebase an agent produced and which of that code is a low-health hotspot owned by a single person. Risk management for AI-era codebases, not developer surveillance.

Both are zero-LLM and reproducible. Deep dives on the hosted site: change risk → · agent provenance →.

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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):

up to −96% tokens to load context  ·  −89% file reads  ·  −70% fewer tool calls  ·  answer quality at parity

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The win is context: repowise hands the agent a curated answer instead of a pile of files to read. Loading a commit's context via get_context costs 2,391 tokens vs 64,039 raw — ~27× fewer (−96%). Across the two benchmarks, agents read −69% to −89% fewer files and make −49% to −70% fewer tool calls at answer quality on par with raw exploration; on a long, multi-step investigation that compounds to −41% of the context re-read across the whole session. Saved tokens are tokens you don't pay for — dollar cost drops too, though agent-side prompt caching now mutes the cost delta. Reports: flask48 · flask v3 · 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:

| Command | Raw → distilled tokens | Saved | |---|---|---:| | pytest -q (11 failures) | 3,374 → 1,317 | 61% — all 11 failure lines preserved | | git log -50 | 3,064 → 331 | 89% | | git diff (30 commits) | 62,833 → 8,635 | 86% |

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 →

<div align="center"> <img src=".github/assets/savings.png" alt="repowise Costs dashboard — tokens and dollars saved across distill and MCP tools" width="100%" /> The Costs dashboard tallies both savings surfaces — <code>repowise distill</code> (command output) and the MCP tools (each curated answer replacing the raw file reads it stood in for) — priced at your coding agent's own model. Example shown from a week of heavy local use. </div>

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 →

<div align="center"> ⭐ Star the repo if repowise just saved your agent a few greps — it helps the next engineer find it, and tells us to keep building. </div>

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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 (three signals — defect · maintainability · performance — coverage, trends) · Refactoring (ranked plan cards, blast radius, copy-to-agent, opt-in code-gen diff) · Security (local pattern scan) · Costs · Workspace (cross-repo contracts & co-changes). Full view-by-view list in docs/USER_GUIDE.md.

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Supported languages

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

Full tier   <img src="https://img.shields.io/badge/Python-3776AB?style=flat-square&logo=python&logoColor=white" alt="Python" /> <img src="https://img.shields.io/badge/TypeScript-3178C6?style=flat-square&logo=typescript&logoColor=white" alt="TypeScript" /> <img src="https://img.shields.io/badge/JavaScript-F7DF1E?style=flat-square&logo=javascript&logoColor=black" alt="JavaScript" /> <img src="https://img.shields.io/badge/Java-ED8B00?style=flat-square&logo=openjdk&logoColor=white" alt="Java" /> <img src="https://img.shields.io/badge/Kotlin-7F52FF?style=flat-square&logo=kotlin&logoColor=white" alt="Kotlin" /> <img src="https://img.shields.io/badge/Go-00ADD8?style=flat-square&logo=go&logoColor=white" alt="Go" /> <img src="https://img.shields.io/badge/Rust-000000?style=flat-square&logo=rust&logoColor=white" alt="Rust" /> <img src="https://img.shields.io/badge/C++-00599C?style=flat-square&logo=cplusplus&logoColor=white" alt="C++" /> <img src="https://img.shields.io/badge/C%23-512BD4?style=flat-square&logo=csharp&logoColor=white" alt="C#" /> Good tier   <img src="https://img.shields.io/badge/C-A8B9CC?style=flat-square&logo=c&logoColor=black" alt="C" /> <img src="https://img.shields.io/badge/Ruby-CC342D?style=flat-square&logo=ruby&logoColor=white" alt="Ruby" /> <img src="https://img.shields.io/badge/Swift-F05138?style=flat-square&logo=swift&logoColor=white" alt="Swift" /> <img src="https://img.shields.io/badge/Scala-DC322F?style=flat-square&logo=scala&logoColor=white" alt="Scala" /> <img src="https://img.shields.io/badge/PHP-777BB4?style=flat-square&logo=php&logoColor=white" alt="PHP" />  · Partial   <img src="https://img.shields.io/badge/Luau-00A2FF?style=flat-square&logo=lua&logoColor=white" alt="Luau" />

| 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 →

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Who it's for

| | Start here | |---|---| | Individual developers | pip install repowise → repowise init → query from Claude Code, Cursor, or any MCP agent. 100% local, BYO API key, free under AGPL-3.0. For developers → | | Team leads | Know which PRs to worry about before you merge: change-risk scoring plus the free Repowise PR Bot that posts one deterministic comment per PR (hotspots, hidden coupling, declining health), zero LLM. For team leads → | | Engineering leaders | See how much of your code AI wrote and whether it is healthy: agent provenance, code-health trends, and bus factor, from git history. For engineering leaders → | | Security & compliance | Reachability-aware CVE triage, secret detection across full git history, and SBOM, on your real dependency graph. For security → | | Enterprises | On-prem / air-gapped, SSO/SCIM, commercial licensing (no AGPL obligation), and IP indemnification. For enterprise → · docs/COMMERCIAL.md |

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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, Live System Map + per-repo pages

The workspace Live System Map renders your services and their typed relationships (HTTP / gRPC / events / package deps / co-change) as a code-derived, always-current diagram — health-colored, filterable, with drill-down to the underlying contracts. See Workspaces.

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 · Upgrading · Config

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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.

| 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?) | Biomarker scores per file across three signals (defect · maintainability · performance). Dashboard mode → KPIs + lowest-scoring files + module rollup; targeted mode → per-file findings. Self-check before a PR via include: accuracy (does the score find the bugs), signals (per-file churn / owners / prior defects), churn_complexity, a dimension name to filter findings, plus coverage, trend, and refactoring → structured, graph-aware refactoring plans (split groups, move target, cut edges + blast radius), not template strings. |

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 →

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How it compares

| | 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 | ❌ | ❌ | ❌ | ✅ | | Concrete cross-file refactoring plans (Extract Class / Move Method / Break Cycle) | ✅ graph-aware + blast radius | ❌ | ❌ | ❌ | ⚠️ within-function only | | 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 with the graph-aware fix attached + auto-generated docs + agent-native MCP + architectural decisions + multi-repo workspace intelligence — self-hostable and open source. Full side-by-side comparisons (CodeScene, DeepWiki, Sourcegraph, Cursor, GitClear): repowise.dev/compare →.

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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 →

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Privacy

Self-hosted: your code never leaves your infrastructure — no code, file paths, or repo names are ever sent. The CLI does report anonymous, opt-out usage telemetry (command names + coarse environment only) to help us prioritize; turn it off with repowise telemetry disable, DO_NOT_TRACK=1, or by running fully offline. What's collected →
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.

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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.

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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.

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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 hello@repowise.dev.

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Built for engineers who got tired of watching their AI agent <code>cat</code> the same file for the fourth time.

⭐ If repowise earns a place in your workflow, give it a star. It costs you nothing, and it's the signal that keeps a small team building this in the open.

<a href="https://repowise.dev">repowise.dev</a> · <a href="https://www.repowise.dev/explore">Explore →</a> · <a href="https://discord.gg/cQVpuDB6rh">Discord</a> · <a href="https://x.com/repowisedev">X</a> · <a href="mailto:hello@repowise.dev">hello@repowise.dev</a>

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The five layers

★ Code Health — the layer nobody else nails

Refactoring intelligence

Change risk & agent provenance

Benchmarks

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

2 · Distill — index-aware output distillation

3 · Code health predicts real defects

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