
Repowise
dev.repowise由 repowise.dev 發布v0.60.1更新於 Nov 3, 2026
Codebase intelligence for AI coding agents — graph, git history, docs, decisions, code health.
發布方自己的服務:Repowise (Hosted)
概覽
Repowise 在本機為程式碼庫建立索引(程式碼、git 歷史、測試與決策),讓編碼助理透過十個 MCP 工具直接查詢,不必反覆讀取檔案。
- 可以做什麼
- 取得陌生程式碼庫的架構概覽、模組地圖與進入點
- 搜尋程式碼與文件,並取得附引用與信心分數的答案
- 依檔案、模組或符號檢視摘要、簽章、呼叫者、被呼叫者、歸屬與指標
- 評估變更風險、熱點、共變檔案、測試缺口與缺陷歷史
- 查詢架構決策及其證據、依信心分級的無用程式碼,以及健康評分與重構方案
- 適用情境
- 適合編碼助理必須反覆 grep 與開啟檔案才能理解大型或陌生程式碼庫的情況,也適合在合併前取得風險、測試影響與程式碼健康結論而不消耗模型呼叫。面向本機單人或團隊儲存庫,而非一次性腳本。
- 執行需求
- 以本機程序方式從 PyPI 安裝(uv、pipx 或 pip),儀表板與 MCP 伺服器需要 Node.js 20 以上版本,首次執行會下載介面。建立索引不需要 API 金鑰。可選的模型撰寫文件、決策擷取與重構程式碼產生需自備 LLM 供應商金鑰。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Repowise,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
[repowise: codebase intelligence for you and your coding agent]
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.
[Set up in one line] [Open the live Repowise demo]
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
[Explore Repowise's own code] [Repowise code health] [PyPI version] [License: AGPL 3.0]
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:
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:
serve opens the dashboard in your browser. After a first index it starts with a short
guided tour, and Setup is where you connect agents, turn on sync and add coverage or a model.
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 adds the global repowise MCP entry
when there is none; it never moves one that serves another repo. 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 distilltrims 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.mdandAGENTS.mdcurrent from the index. - See your own savings.
repowise savedand 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.
Every question it answers
Understand the code
Change it safely
Improve it continuously
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.mdandAGENTS.mdfrom 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 pytestkeeps every failure and drops the noise, and nothing is lost: anexpandcommand 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 →
[Start free on hosted repowise.dev] [Join the Foresight waitlist]
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.
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.
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 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.
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
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.
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
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
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 [Python] [TypeScript] [JavaScript] [Svelte] [Vue] [Astro] [Java] [Kotlin] [Go] [Rust] [C++] [C#] [Scala] [Ruby] [Apex (Salesforce)]
Good [C] [Swift] [PHP] [Dart] [Object Pascal / Delphi] [COBOL] [GDScript / Godot] [VB.NET] [Elixir] [F#] [Objective-C] · Partial [Luau] [Razor / Blazor]
The full ladder, and what each rung gives you
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 [Claude Code] [Codex CLI]
Good tier [VS Code] [Cursor] [OpenCode] [Hermes] [GitHub Copilot CLI] [Kiro]
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
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.
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.
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 · [email protected]
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 withrepowise 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
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
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 [email protected].
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 · [email protected]
來源:README.md,提交 775a4e0
工具
0版本歷史
7- v0.60.1最新Nov 3, 2026
- v0.59.0Oct 25, 2026
- v0.58.0Oct 20, 2026
- v0.57.0Oct 15, 2026
- v0.56.0Oct 12, 2026
- v0.55.0Oct 4, 2026
- v0.17.1Sep 16, 2026
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