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RepoWeaver

Turn any codebase into an explainable architecture map.

RepoWeaver is a local-first codebase intelligence cockpit. Give it a public GitHub repository or a local folder and it maps internal dependencies, identifies architectural hubs, traces change impact, surfaces evidence-backed risk signals, and generates a short onboarding tour.

No LLM key. No backend. No source upload.

Real repository analysis

The screenshot below is a live analysis of this public repository, Cosimo-Di-Rondo/RepoWeaver—not a mockup. RepoWeaver loaded the source from GitHub and detected 12 source files, 15 internal relationships, 93 symbols, and 5 architectural hotspots.

RepoWeaver analyzing the real Cosimo-Di-Rondo/RepoWeaver repository

The selected App.tsx node shows its real inbound/outbound dependencies, complexity, symbols, and evidence-backed findings in the inspector.

Why this exists

Opening a new repository usually starts with the same archaeology: find the entry point, follow imports, guess which modules are central, and work out what a change might break. Existing code graph tools are powerful but often require a graph database, embeddings, or a model provider before they become useful.

RepoWeaver makes the first five minutes useful:

  • Architecture map — internal file dependencies grouped by module and role.
  • Impact tracing — reverse dependency traversal shows a change's downstream blast radius.
  • Evidence-backed risks — each signal links to a file, line, and named rule.
  • Generated code tour — entry point → architecture hub → risk boundary → safety net.
  • Two import paths — public GitHub repositories and browser-local folders.
  • Portable report — export the full analysis as JSON.

Quick start

npm install
npm run dev

Open http://127.0.0.1:4173. A built-in demo repository is ready immediately, or choose Analyze repo to load your own code.

npm test       # static analysis and URL parsing tests
npm run build  # type-check and production build

How it works

flowchart LR
    A[GitHub URL or local folder] --> B[Browser source loader]
    B --> C[Language-aware import scanner]
    C --> D[Normalized dependency graph]
    C --> E[Symbol and risk rules]
    D --> F[Architecture map]
    D --> G[Reverse impact traversal]
    D --> H[Generated code tour]
    E --> I[Evidence panel and JSON report]
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The analysis engine is intentionally deterministic. It normalizes file paths, resolves relative JS/TS and Python imports, extracts common top-level symbols, scores branch complexity, and runs a small set of reviewable rules. The UI never invents an explanation: visible findings come from those rule results.

Repository layout

src/
├── components/GraphCanvas.tsx  # deterministic graph layout and interactions
├── data/demo.ts                # instant, self-contained product demo
├── lib/analyzer.ts             # dependency, symbol, risk, tour, impact engine
├── lib/github.ts               # GitHub and local-folder ingestion
├── App.tsx                     # product views and import workflow
└── styles.css                  # responsive visual system

Product principles

  1. Useful before AI — architecture understanding should not depend on a paid model.
  2. Evidence over confidence — a line number beats a vague “AI detected a risk” claim.
  3. Progressive depth — overview first, then file evidence, then blast radius.
  4. Local-first by default — local source is read with browser file APIs and remains on-device.
  5. Demo in one click — the bundled repository exercises every major feature.

Current scope

  • Dependency resolution is strongest for JavaScript, TypeScript, JSX/TSX, and Python.
  • Source inventory also recognizes Go, Rust, Java, CSS/SCSS, Vue, and Svelte files.
  • GitHub's unauthenticated API limit applies to public-repository imports.
  • The current graph is file-level; AST adapters and symbol-level call graphs are natural next steps.

See the implementation roadmap and the 2026-08-11 GitHub research notes.

Origin and attribution

RepoWeaver is an original implementation. Its product decisions were informed by patterns observed in successful open-source products—graph-native context, node-based exploration, one-step ingestion, auditability, and polished local tooling—but it does not copy their code or branding. The research notes document those influences explicitly.

License

MIT

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Turn any codebase into an explainable architecture map — local-first dependency graphs, impact analysis, risk signals, and code tours.

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