Repowise README

Stop paying your agent to re-read your codebase.

Repowise indexes your code, git history, tests and decisions once, on your machine.
Your coding agent asks the index instead of grepping and opening files, so it does about half the work.
You get the same answers in a dashboard: what calls this, what breaks, what to fix first, and why.

Half the tool calls
_{your agent opens 3 files where it opened 7 · 31.6% less output}

Up to 99% less noise
_{from test runs, installs and git output · every error line kept}

88% of the right files
_{found in one call · next best tool: 61%}

_{Graph, risk, health, tests and dead code make zero LLM calls · no API key to index ·
28 languages · Claude Code, Codex, Cursor and any MCP host · free and open source}

Quickstart · For your agent · What it does · Hosted · Benchmarks · Teams · Docs

Set it up in one line

Open Claude Code, Codex, Cursor or any MCP-capable agent in your repository and paste:

text

Read https://docs.repowise.dev/setup.md and set up Repowise in this repository.

The agent installs Repowise, indexes the repo with no API key, connects itself to the index, and asks you before anything costs money. To do it by hand:

bash

uv tool install repowise          # or: pipx install repowise / pip install repowise
cd /path/to/your/repo
repowise init --yes --no-prose    # graph, git, health, dead code, docs. No key, no spend.
                                  # (add model-written docs any time with: repowise generate)
repowise serve                    # dashboard + MCP server (needs Node 20+; first run downloads the UI)

Restart Claude Code or run /mcp, then ask "What breaks if I change src/auth.py?" or "Use Repowise get_overview to summarize this repository." init wires Claude Code for this repo and points the global repowise MCP entry here. Full setup for every agent →

Your agent stops guessing

Without an index, an agent rediscovers your codebase on every task: grep, open a file, open three more, grep again. Repowise answers those questions ahead of time, so the agent asks once and gets on with the work.

In a measured run on django/django, an agent with Repowise made 3.8 tool calls instead of 7.2, opened 3.0 files instead of 7.2 and wrote 31.6% less, with answers just as good. That is the largest saving of the five context tools we tested. On a small local model the output cut reaches 47.9%. How it was measured →

Your agent gets ten task-shaped MCP tools. Ask about several files at once and get callers, risk, tests, owners and the reasons behind the code in one answer. The tool list ↓
Up to 99% less command noise. repowise distill trims test, install and git output before the agent reads it and keeps every error line. Distill →
Context before the agent asks. Optional hooks warn it off fragile files and stale paths, and Repowise keeps your CLAUDE.md and AGENTS.md current from the index.
See your own savings. repowise saved and the dashboard's Usage and savings page count the tokens your agent no longer has to read.

What it does

One index of your code, its history and its decisions, used three ways: to understand the code, to change it safely, and to keep improving it.

See how it fits together A call graph across 28 languages, an architecture map you can explore, C4 views, and a generated wiki for every module and file. The graph · Docs	Know what breaks Every caller of what you changed, the files that usually change with it but are missing from your diff, and the tests that reach it. Change risk · Tests
Fix what matters A 1 to 10 health score for every file, validated against real bug history, a ranked fix-first queue, refactoring plans and dead code. Code health · Refactoring	Follow it across repos Services and the HTTP, gRPC and event contracts between them, with a guard for breaking changes and the consumer files they affect. Workspaces
Ask it anything Cited answers and charts from the dashboard, or the same answers through MCP for your agent. Dashboard · MCP tools	Remember why Decisions mined from ADRs, comments, commits, PRs and agent sessions, each tied to the code it governs, plus docs checked against the code. Decisions · Doc drift

Every question it answers

Understand the code

You ask	Repowise gives you
How does checkout work in this repo?	A cited answer built from the call graph and the generated docs, in one call. Search and answers
What calls this function, and what does it call?	A call graph across 28 parsed languages, every edge stamped with how it was resolved and how far to trust it, plus traced execution flows from each entry point. The graph
Can I get docs for this codebase?	A wiki for every module and file, rendered from the code's structure with no key, or written by a model when you choose. It updates incrementally after each commit. Docs
Which of our docs are wrong?	Markdown checked against the tree: every reference to a file or symbol the code no longer has, with the line to edit. Doc drift
Why is it built this way?	Decisions mined from ADRs, `# WHY:` comments, commit and PR history and your agent sessions, each tied to the code it governs and flagged when it goes stale. Decisions
Who knows this code?	Owners, bus factor, fading ownership when the main author goes quiet, and suggested reviewers. Ownership
Can I see the architecture?	An explorable dependency map, C4 views, and a Structurizr export, no model involved. Dashboard

Change it safely

You ask	Repowise gives you
What breaks if I change this?	Symbol-level blast radius: the callers of what you changed, the files that historically change with it but are missing from your diff, and the tests that reach it. Change risk
How risky is this PR?	Where the change ranks against your repository's own recent commits, with the reasons, as a directive your agent can act on. Change risk
Which tests should run?	The tests a diff actually exercises, from a coverage report if you have one and from the call graph if you do not. Test intelligence
Did this PR add untested lines?	Patch coverage, branch coverage on changed lines and path-scoped gates in CI, on GitHub, GitLab or any runner. CI gates
Will this API change break another repo?	HTTP, gRPC, topic and OpenAPI contracts matched across repositories, with a breaking-change guard and the consumer files it affects. Workspaces
Is anyone else editing these files?	Other open branches touching the same files or their co-change partners, each with the reason it is listed. Branch overlap
Did we just commit a secret?	Keys, tokens and risky calls found in the working tree and in full git history, with a pre-commit check and a CI gate. Security signals

Improve it continuously

You ask	Repowise gives you
What should we fix first?	A ranked queue weighing impact against effort, using churn, fan-in, coverage and bug history. Fix first
Where is the debt?	A 1 to 10 score for every file from 53 deterministic detectors, split into defect risk, maintainability and performance, validated against real bug history. Code health
Why is this slow?	N+1 queries, I/O in loops, blocking calls inside async code and quadratic loops, traced across function and file boundaries. Performance
How do I break this up safely?	Concrete refactoring plans: Extract Method, Extract Class, Move Method, Split File, Break Cycle, with the exact symbols that move and what moves with them. Ready to hand to an agent. Refactoring
What can we delete?	Unreachable files, unused exports and unused packages, each with a confidence tier and the evidence behind it. Dead code
Where do bugs keep landing?	Bug-fix commits traced to files and symbols, and a warning when your agent edits a bug magnet. Bug history
Are our tests testing anything?	Tests with no assertions, tests that only check their own mocks, and untested hotspots. Test-quality smells

Things people do not expect it to do

Test coverage without running coverage. Most repositories never produce a coverage report. Repowise answers "is this tested, and by what" from the import and call graph, and labels every answer measured or inferred.
It checks its own health score on your repository. After each index it reports how many of the lowest-scoring files actually had bug fixes in your recent history, so a bad score on your codebase is visible to you.
It learns from your agent sessions. With transcript capture on, the corrections you keep repeating ("use the shared HTTP client") become tracked decisions it feeds back to the agent later. Transcripts never leave your machine.
It writes your CLAUDE.md and AGENTS.md from the real index and keeps them current, so even an agent with no MCP support starts informed.
It shrinks command output before your agent reads it. repowise distill pytest keeps every failure and drops the noise, and nothing is lost: an expand command restores any cut.
New git worktrees start indexed. A linked worktree seeds its index from the base checkout instead of starting cold.
It knows which external systems you depend on. Package manifests across PyPI, npm, Cargo, Go, NuGet, Maven and CMake feed a map of the services and libraries the code reaches.

Hosted, and Foresight

Everything above runs free on your own machine. repowise.dev runs it for you, private repos included, and is where Foresight arrives first: models trained on years of real commits and fixes that rank the files a fix will touch, name what a change forgot, and explain every answer, with no LLM. What Foresight does →

Already indexed locally? repowise publish puts the repo on repowise.dev in one command. It indexes your GitHub remote, so nothing is uploaded from your machine. Public repos are free.

Know what breaks before you merge

All of this comes from the graph and git history, with no LLM, so the same diff always gets the same answer.

Change risk. Score any commit or branch 0 to 10 against your repository's own recent commits, with what it may break, the companion files it forgot, the tests it is missing and the tests to run. repowise risk main..HEAD (reference)
Tests without a coverage run. Which tests reach a file and which ones a diff exercises, from a coverage report when you have one and from the call graph when you do not: 97.5% correct on which tests to run. repowise impacted-tests main..HEAD (reference)
Bug magnets. Which files keep getting bug fixes, with a warning when your agent edits one. (reference)
Branch overlap. Other open branches editing the same files. repowise overlap (reference)
In CI. Patch coverage, doc drift, security and change risk as gates through the GitHub Action, a GitLab template or plain CLI, with SARIF output. No API key. Repowise in CI →

On every pull request. The free Repowise PR Bot posts one comment, edited in place on every push, with the callers outside the PR, the tests and companion files it is missing, and its change risk. A green PR gets no comment at all.

The dark Repowise per-PR analysis page showing change risk, repository health, changed contracts, outside callers, newly added findings, and a blast-radius treemap of the repository

_{A real comment on a real PR: repowise-dev/repowise#1204 ·
its analysis page → ·
install the PR bot →}

Every change, with its evidence

Each merged PR or commit becomes a Change Record: what it touched, who owns it, the tests that reach it, the contracts and decisions it crosses, and what happened after it landed. No forge API is called.

You ask	Repowise gives you
What landed in this PR, and what backs it up?	The change's evidence checklist, each item `ok`, `attention` or `unknown`, with an in-toto export. Change Records
Can CI hold changes to our payment code to a higher bar?	Rules in `.repowise/policies.yaml`, matched to components by name, tag or criticality and evaluated by `repowise check`, with SARIF or JUnit output. Change policy
Something broke. Which change do we read first?	Recent changes ranked against an error, a stack trace or an issue. On 43 real regressions, the change a later revert or fix named was in the top 5 for 67% of them, against 28% for ranking by shared files. Suspects
Who owns this, and are they still working on it?	Backstage-shaped components with owners from your catalog and CODEOWNERS, compared with the last 90 days of commits. Components
Can we show an auditor or an acquirer where we stand?	Evidence packs: a repository assessment, a change report for a period and a component inventory, as JSON, Markdown, HTML or CSV. Reports

Know exactly what to fix

Every file gets a 1 to 10 score from 53 deterministic detectors, read three ways: defect risk, maintainability and performance. Performance findings such as N+1 queries and I/O in loops are traced across functions and files.

The score is checked against real bugs. On 21 open-source projects, ranking by Repowise found 2.3x as many later-buggy files as CodeScene in the same review budget. It also checks itself on your repository after every index: "16 of the 20 lowest-health files had a bug fix in the last 6 months, 3.3x the 24% baseline."

Then it names the fix: Extract Method, Extract Class, Move Method, Split File or Break Cycle, with the exact symbols that move, ranked so a fix on a central file comes before the same fix on a leaf. Each plan has a copy-to-agent button.

bash

repowise next                          # what to fix first, ranked by impact and effort
repowise health --refactoring-targets  # concrete plans
repowise dead-code                     # what can go, by confidence tier

Code health · Refactoring · Dead code · How it was validated →

Across your repositories

Real systems are more than one repository, and the expensive failures live between them. Change a backend contract and Repowise names the frontend calls that consume it, the services downstream, and the architecture rule the new dependency breaks, before it ships.

Workspace intelligence	What it answers
Contract map	Which services provide and consume each HTTP, gRPC, event, socket and data contract? Links keep their exact or candidate confidence and the source evidence.
Cross-repo blast radius	If this provider changes, which downstream services are in structural reach, and which may drift through historical co-change?
Breaking-change guard	Was an endpoint removed or an OpenAPI, proto or signature shape changed incompatibly, and which consumer files are linked to that contract?
Test impact	Which tests in the consumer repos should run for this provider change, measured from coverage or inferred from the call graph?
Architecture as code	Does the live system graph violate declared dependency rules or contain cycles? `repowise workspace check` gates CI.
Architecture health	How coupled is the estate? Propagation cost, the cyclic core, service roles and a deterministic 1-10 architecture score.
Federated context	One dashboard and one MCP server answer across every repository while keeping repo-level evidence.

Workspace guide and support matrix → · Keeping the index fresh →

Measured against the field

The same repositories, commits and questions for every tool, each given its full advertised surface, against answer keys we did not write.

Output tokens an agent writes to reach an answer across 43 django questions on Codex: repowise 1,250, CodeGraph 1,383, Serena 1,550, Graphify 1,658, code-review-graph 1,710, bare agent 1,828

File coverage on 42 sealed ContextBench instances: repowise get_answer 0.876, repowise search_codebase 0.742, CodeGraph 0.610, Graphify 0.546, code-review-graph 0.445, cocoindex 0.361

	Result
Work saved in an agent loop	3.8 tool calls instead of 7.2, 3.0 files opened instead of 7.2, 31.6% less output. The best of five context tools. n=43, p<0.0001
Finding the right files	0.876 of the files a fix needed, against 0.610 for the next tool, on a sealed set. n=42, p=0.00004
Command output	Up to 99.5% smaller (`npm ci`), 89% for `git log`, 86% for `git diff`, no error line lost
Call graph the compiler agrees with	95% to 99.6% precision on Go, TypeScript, Dart, C and Python, graded by each language's own toolchain. Against four other tools, none that finds as much of the graph gets more of it right, in 7 of 7 cells
Health that predicts bugs	2.3x CodeScene's recall of later-buggy files at the same review budget. 2,770 files, p=0.003
Scale	15 million lines (dotnet/runtime) on one laptop. Building the call graph takes 75 MB, 10x less than the next tool

Every result, in plain English, with the method → · The research it is built on →

How it compares on capability

No single product competes with all of this, so there is no single table. Rows marked measured are head-to-head numbers that link to docs/BENCHMARKS.md, where the sample sizes, the tests and the rows we lose live. Unmarked rows are capability presence, not measurements.

As an agent context layer

	repowise	CodeGraph	Serena	DeepWiki
Self-hostable, open source	✅ AGPL-3.0	✅	✅	❌ cloud only
Private repo, no cloud	✅	✅	✅	❌ OSS forks only
MCP tools served	10 default + 7 opt-in	1	29	3
Finds the gold files (measured, n=42 sealed)	✅ 0.876	0.610	not in this run	not measured
Output tokens vs a bare agent (measured, n=43)	✅ -31.6%	-24.4%	-14.8%	not measured
Memory to build the graph (measured, 5 tools, 35 repos)	✅ 75 MB, lowest on 35 of 35	757 MB	not measured	n/a, cloud
Time to build the graph (measured, same run)	2.77s, fastest on 14 of 35	3.65s, fastest on 16	not measured	n/a, cloud
Time to build the full index, django (measured)	⚠️ 366.8s, slowest here	✅ 16.4s	not measured	n/a, cloud
Call-edge precision, hand-graded (measured, 560 rows, 9 languages)	✅ 85.7%	58.6%	not measured	not measured
Call-edge precision, judged by a compiler (measured, 5 tools, 7 cells)	✅ nothing that finds as much gets more of it right, 7 of 7	lower precision in 7	not measured	not measured
Generated documentation	✅	❌	❌	✅
Proactive agent hooks	✅ Claude + Codex	❌	❌	❌
Generated `CLAUDE.md` / `AGENTS.md`	✅	❌	❌	❌
Command-output distillation	✅ reversible	❌	❌	❌
Architectural decision records	✅	❌	❌	❌
Multi-repo workspace intelligence	✅ contracts, co-change, federated MCP	❌	❌	❌

The two cost rows answer different questions. Building the call graph, we are the lightest tool measured, about ten times lighter than the next, and roughly as fast as the fastest. Building the whole index, CodeGraph is 22x faster than we are, because by then we have also built the git-history layer, the wiki, the decisions and the health pass. If a call graph is all you need, that is the right trade and you should take it. With model-written prose on, it is 135x.

The hand-graded precision row cuts both ways. About fourteen percent of the call edges we draw are wrong, and on seastar CodeGraph grades better than we do. The compiler row exists because we graded the hand-read one ourselves: on Go and TypeScript the answer key is the Go team's own call graph and the tsc checker's own resolution, which we neither wrote nor can tune. Precision alone is easy to win by drawing almost nothing, and recall alone by drawing everything, so the claim is the pair.

_{Competitors measured at CodeGraph 1.5.0, Graphify 0.9.31, Serena 1.6.2.dev0 and
code-review-graph 2.3.7 in August 2026. Repowise compiler-graded figures are from main
on 2026-10-03; the other Repowise rows are from 081a59fa, August 2026.}

As a code health tool

	repowise	CodeScene
Self-hostable, open source	✅ AGPL-3.0	⚠️ on-prem Docker, proprietary
Code health score (1-10)	✅ 53 detectors, 25 scoring	✅ 25-30
Brain Method / LCOM4 / god class	✅	✅
Defects found at a 20% review budget (measured, 2,770 files)	✅ 0.173	0.074
Effort-aware ranking, Popt (measured, p=0.003)	✅ 0.607	0.462
Precision at that budget (measured)	0.580	✅ 0.636, a shorter list
Discrimination, ROC AUC (measured, paired)	0.731	0.705, p=0.054, not significant
Business impact (resolution time)	❌ we could not replicate this on open data	✅ Code Red study
Git intelligence (hotspots, ownership, co-change)	✅	✅
Pre-merge change-risk scoring	✅ 0-10 + directives	✅
Concrete cross-file refactoring plans	✅ graph-aware + blast radius	⚠️ within-function only
Test-coverage intelligence	✅ LCOV/Cobertura/Clover/JaCoCo/Go	❌
Dead code detection	✅	❌
Serves it to an AI agent over MCP	✅	✅

CodeScene is the only other vendor in this category with a published empirical defect study, which is why it is the one we ran head to head. It flags about 27 files where we flag 132, so if you want a short list to act on, its threshold is the better fit.

Documentation generators

DeepWiki, Google Code Wiki and Swimm generate documentation from a repository, which overlaps one of our layers. We have not measured against them, so there is no table here.

The PR bot, against LLM review bots

	Repowise PR Bot	CodeRabbit	Greptile
LLM calls per PR	✅ zero	❌ every review	❌ every review
Same diff, same review	✅ deterministic	❌ sampled output	❌ sampled output
Your code sent to a model provider	✅ never	❌ yes	❌ yes
Symbol-level blast radius	✅ call graph	❌	⚠️ prose, from context
Co-change partners missing from the PR	✅ git history	❌	❌
Change risk vs the repo's own history	✅ 0-10 + percentile	❌	❌
Silent on a clean PR	✅ by default	⚠️ configurable	⚠️ configurable

An LLM reviewer is a different product: it can read intent, and it can be wrong in a new way on every run. This one does set arithmetic over a call graph and a git history, so pushing the same diff twice gives the same review twice. Full side-by-side comparisons: repowise.dev/compare →

Supported languages

28 languages parsed to an AST, 42 on a five-rung ladder, framework-aware where an ecosystem handler exists. Every language ships in the open-source distribution.

Full

Good · Partial

The full ladder, and what each rung gives you

Rung	Languages	What you get
Full (15)	Python · TypeScript · JavaScript · Svelte · Vue · Astro · Java · Kotlin · Go · Rust · C++ · C# · Scala · Ruby · Apex (Salesforce)	The whole pipeline: AST symbols, import resolution, a resolved call graph, heritage, docstrings, framework edges and code-health markers
Good (11)	C · Swift · PHP · Dart · Object Pascal · COBOL · GDScript · VB.NET · Elixir · F# · Objective-C	All of the above except the full health suite, within the language-specific ceilings in the full matrix
Partial (2)	Luau / Roblox · Razor / Blazor	Luau: AST symbols and `require()` resolution, Rojo and `.luaurc` aware. Razor: component symbols, `@code` and component-tag call edges, C# health markers; no import resolution yet
Lightweight (6)	Clojure · Haskell · Lean 4 · Erlang · HTML · QML	A real file-to-file import graph, and no symbol-level claims
Structural (8)	R · Zig · Julia · Elm · OCaml · Crystal · Nim · D	Git history: blame, hotspots, co-change, ownership, bug history

SQL and dbt projects get ref() / source() lineage, shell scripts get function-level symbols, HTML pages contribute their <script src> and <link href> dependencies, and OpenAPI, Protobuf, GraphQL, Dockerfile, Terraform and similar formats get dedicated handlers. Anything else is still tracked through git history.

Every call edge is stamped with how it was resolved and how much to trust it, from same_file at 0.95 down to a repo-wide name match at 0.50, labelled as the guess it is. Accuracy per language, graded against each language's own compiler: docs/BENCHMARKS.md#accuracy-by-language.

Full matrix: docs/layers/LANGUAGE_SUPPORT.md → · adding a language takes five small steps and no changes to the parser core: docs/architecture/language-support.md → · languages moving up the ladder: roadmap →

Supported agents and editors

Eight agents wired end to end · two at the Full tier · every other MCP host one paste away.

Full tier

Good tier

Full is every surface Repowise has: MCP tools, skills, slash commands, a managed instructions file, hook-level interception of tool calls, and transcript mining after the session. Good is MCP tools plus an instructions file or skills, without transcript mining (hooks only where the matrix says). Basic is the MCP config alone. Anything else that speaks MCP is one snippet away: repowise agents print-config claude-code prints a server entry for Cline, Windsurf, Zed, Gemini CLI or any host that reads mcpServers. Integration matrix →

In VS Code, the Repowise extension shows what your change breaks before you push (riskiest files, what is downstream, forgotten companion files, missing tests, suggested reviewers), health in the gutter and status bar, callers and ownership on hover, and refactoring plans as CodeLens. One install also registers the MCP server, so the same index serves you and your agent. Install from the Marketplace or Open VSX and run Repowise: Set Up This Repository. VS Code guide →

Lens in Claude Code on Django: Claude edits query.py, which turns amber on the map while the 12 files that import it light up in plum; a second edit to django/conf/__init__.py lights its 248 importers, and Flow lists what to check before accepting

In Claude Code, Lens ships inside the Repowise plugin and shows you what the index knows while Claude works: the file it is on and how many files depend on it, a change review after a turn that edits files (code health, tests to run, other branches on the same files), and /lens, a pane with Flow (what to check before accepting each turn), a map of the repo lit by what Claude searched, opened and edited and what its edits reach, Ask, and a session recap. Nothing reaches Claude unless you press a button, and Lens makes no model calls of its own. It needs Claude Code 2.1.287 or later, and the map needs repowise serve --no-ui running. Lens guide →

The ten MCP tools

Every response carries a _meta envelope with the indexed commit, the index age and a stale warning when the index has fallen behind your checkout, so your agent always knows how much to trust what it just read.

See the MCP tool surface

Tool	What only this tool answers
`get_overview()`	Architecture summary, module map, entry points, git health. The first call on an unfamiliar codebase.
`get_answer(question)`	Hybrid retrieval (full-text plus vector), graph expansion and one cited answer with a single calibrated `confidence`. Search, read and reason in a single round-trip.
`get_context(targets, include?)`	Triage card for files, modules or symbols: summary, signatures, hotspot flag, governing decisions, symbol ids. `include` opens callers, callees, ownership and metrics. Batch many targets in one call.
`get_symbol("file.py::Name")`	One indexed symbol's source with exact line bounds.
`search_codebase(query)`	Hybrid search over code and docs, by symbol, path or concept.
`get_risk(targets?, changed_files?)`	Hotspots, dependents, co-change partners, ownership, test gaps, bug history. Pass `changed_files` for PR mode and get a `directive` back.
`get_change_risk(revspec)`	What a commit, range or uncommitted change made worse across defect risk, maintainability and performance, the tests that touch it, and how the diff ranks against recent commits.
`get_why(query?, targets?)`	Architectural decisions with their verbatim evidence. Falls back to git archaeology when no decision exists.
`get_dead_code(...)`	Unreachable code by confidence tier, with cross-repo consumers in workspace mode.
`get_health(targets?, include?)`	Health scores and findings across all three lenses, Fix first, coverage, trends, doc drift and refactoring plans.

Ten is a deliberate ceiling: a small, task-shaped surface is easier for an agent to choose from than a large one. Seven more tools (dependency paths, execution flows, refactoring code generation, finding triage, and three workspace architecture tools) are opt-in. Parameters, examples and when to use which: docs/agent/MCP_TOOLS.md →

For teams and enterprises

AI makes producing a change cheaper. It does not make understanding its consequences cheaper. Repowise gives developers, agents, reviewers and platform teams the same evidence about what exists, what depends on it, what is risky and what will break, from one index, in place of a separate health tool, dead-code tool, code-search layer for agents, docs generator and test-impact service.

Deployment	Where your code is read	What leaves your network
Self-hosted, open source (`pip install repowise`)	your machine	anonymous CLI telemetry you can switch off, and your own LLM provider only if you turn prose on
Self-hosted, commercial	your VPC or an air-gapped network	nothing at all in air-gapped mode
Hosted (repowise.dev)	our managed indexer	your code goes to the platform

Graph, git, health, change risk, tests, dead code and PR review make zero LLM calls, and raw source is never stored. Prose is optional and runs on your own provider or fully offline through Ollama.

Evidence you can hand to someone else.

Who is asking	What Repowise produces
An auditor reviewing change management (SOC 2 CC8.1, ISO 27001 A.8.32, PCI DSS 6.5)	Every change in the period with its review record, reaching tests, policy verdict and later outcome: `repowise report changes`
A software inventory review (PCI DSS 6.3.2)	Components with owner teams, languages, entry points and the packages they import: `repowise report inventory`
A security team asking which of your code uses a package	Import sites with component, owner team and reachability, plus a CycloneDX 1.6 usage export: `repowise packages where`
An acquirer, a board or a migration committee	Component posture, hotspots, bus factor, dead code, test reach and contracts: `repowise report assessment`
An incident review	The recent changes to read first, with the signals behind each: `repowise suspects`

Any git host works, because indexing reads a local checkout: GitHub (PR bot, Action, webhooks), GitLab (CI template, webhooks), Bitbucket and others through the CLI, and plain directories or Perforce and SVN exports for every layer except history.

Commercial licences come per seat, per indexed repository or enterprise-wide, with a named contact, an SLA and a quarterly architecture review. Using Repowise inside your company under the AGPL creates no obligation to publish your own code.

Commercial detail and capability status · Security review pack · Evaluating it for your company · hello@repowise.dev

Privacy

Deterministic or offline mode: with --no-prose, code-derived content stays on your infrastructure. The CLI reports anonymous, opt-out usage telemetry (command names and coarse environment only); turn it off with repowise telemetry disable, DO_NOT_TRACK=1, or by running fully offline. What's collected →
Optional LLM features: generated prose, decision extraction and refactoring code generation send code-derived prompts directly to the provider configured with your own key. Repowise does not proxy those calls.
What's stored: the graph, embeddings, generated wiki pages and git metadata. Raw source is processed transiently and never persisted.
Fully offline: Ollama plus a local embedding model means zero external calls.

Doing a security review? docs/business/SECURITY_COMPLIANCE.md →

CLI

Every command at a glance

server.ts

repowise init [PATH]      # index a codebase (plan screen; --yes --no-prose needs no key)
repowise generate [PATH]  # write wiki pages with a model, on demand
repowise serve [PATH]     # MCP server + local dashboard
repowise update [PATH]    # incremental update (--workspace for every repo)
repowise watch            # re-index on file change
repowise search "<q>"     # hybrid search (fulltext / semantic / symbol / path)
repowise ask "<q>"        # a synthesized answer with citations
repowise context <files>  # triage card: layer, hotspot, fix history, freshness
repowise symbol <id>      # one symbol's body, with verified line bounds
repowise why <q|path>     # decisions, rationale, git archaeology
repowise next             # what to fix first
repowise health           # code-health KPIs and lowest-scoring files
repowise risk main..HEAD  # score a branch or PR range
repowise overlap          # other branches editing the same files
repowise impacted-tests   # only the tests a diff exercises
repowise changes show ID  # one merged change and its evidence checklist
repowise check            # evaluate .repowise/policies.yaml against a change
repowise suspects         # recent changes ranked against a symptom
repowise components       # declared and derived components, owners vs activity
repowise packages where   # where a third-party package is imported
repowise report           # assessment, change evidence and inventory packs
repowise dead-code        # unreachable code by confidence tier
repowise doc-drift        # documentation the code no longer supports
repowise security         # secrets and risky patterns, tree or full history
repowise decision list    # architectural decisions
repowise export --format structurizr  # the architecture as Structurizr DSL
repowise distill pytest   # compact, errors-first, reversible command output
repowise saved            # tokens and dollars saved by distillation
repowise workspace add    # multi-repo workspace management
repowise doctor           # check setup, API keys, index drift
repowise publish          # put this repo on repowise.dev (indexed from GitHub; public repos free)
repowise uninstall        # remove what repowise wrote, and say what it left

Every command and flag: docs/reference/CLI_REFERENCE.md · config: docs/reference/CONFIG.md · something not working: docs/start/TROUBLESHOOTING.md · all docs: docs/README.md

Contributing

bash

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

New here? You do not have to read 3,000 files to start. We keep a public index of this repository built by Repowise itself, re-indexed on every push: explore repowise with repowise → (architecture, hotspots, ownership, decisions, and a ranked refactoring backlog you are welcome to pick from).

Full guide, including how to add languages and LLM providers: CONTRIBUTING.md · architecture: docs/architecture/

License

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

For commercial licensing (the enterprise security and compliance layer, SSO/SCIM, RBAC, workflow integrations, priority support and SLA, or embedding Repowise in a product without AGPL obligations), see docs/business/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.

_{⭐ 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.}

repowise.dev · Explore → · Discord · X · hello@repowise.dev

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