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glyphibe

Graph-first code comprehension with deterministic local analysis and evidence-backed learning

styayur/glyphibecommunitylistedunreviewed

Set up

Runs on your computer. Pick your agent and operating system.

This project does not publish an install method we can turn into a config. Follow the README instead.

README

synced Oct 29, 2026

Glyphibe

Understand a codebase by following its connections. Glyphibe turns files, functions and smaller source structures into a graph. Select a node to inspect evidence, trace calls, explore possible modification effects, or learn a programming concept through a question, hint, answer and review.

Source version 1.0.0. Installers are unsigned. Published distributions and their approval status are listed in GitHub Releases. The release gates distinguish completed checks, remaining work and performance limits. 中文说明.

Captured from the actual installed Windows application; this is source from the project's original public test fixture. See the release gates for validation status.

Capabilities

  • Local Python, Rust, JavaScript and TypeScript/TSX Tree-sitter parsing, with conservative cross-file calls, imports, references and type relations.
  • One evidence-bearing graph shared by desktop, CLI and standard stdio MCP. MCP lets an external Agent query named tools within a chosen repository.
  • Six granularities from domain to expression, on architecture and explanation planes, with many-to-many source mappings and explicit stale relationships.
  • Deterministic Louvain communities, separate directory components, cycle detection, call traces and directional modification reachability.
  • Original bilingual concept knowledge, prerequisite routes and four independent teaching depths. Questions gradually reveal syntax and evidence.
  • Frontier, Dense, Cluster and true 3D Galaxy views on a full-window canvas, with glass islands, keyboard controls, focus mode and opaque fallback.
  • External SQLite caches, content hashes, identity reconciliation, bounded scanning, cancellation and actual file progress.

The core works without a model, API key or cloud service. It never executes a scanned project or installs its dependencies. Explicit GitHub knowledge imports use the network and remain untrusted local-only material.

Install published packages

Open Releases and read the validation notes for the published version. Windows x64 distributions are:

FileUse
Glyphibe_1.0.0_x64-setup.exeDesktop installer; launch Glyphibe after installation
Glyphibe-1.0.0-windows-x64-portable.zipExtract together, then run glyphibe-desktop.exe
Glyphibe-1.0.0-windows-x64-cli.zipExtract together; run glyphibe.exe --help without a GUI

Keep the included licenses directory. The separate CLI executable should be accompanied by Glyphibe-1.0.0-licenses.zip. Desktop requires Microsoft WebView2. The CLI requires the Microsoft Visual C++ v14 x64 Runtime. Packages are unsigned and Windows may display publisher/SmartScreen warnings. Compare Get-FileHash FILE -Algorithm SHA256 with the published SHA256SUMS. See the installation guide and release gates for platform and lifecycle evidence. A source version alone does not mean a download has been approved or published.

Build from source

Install Rust 1.98.1, Node.js 24 or newer, and the platform Tauri prerequisites. Windows needs MSVC Build Tools and WebView2. From this trusted project's checkout:

cargo install --locked --path crates/glyphibe-cli
glyphibe analyze tests/fixtures/python
glyphibe --repo tests/fixtures/python search calculate
cd apps/desktop
npm ci
npm run tauri -- dev

npm run tauri -- build --bundles nsis builds a Windows development installer. Build success is separate from installation verification. Unsigned programs can trigger Windows publisher or SmartScreen warnings.

First use

  1. Choose Open folder in the desktop app; ordinary directories without Git work too. The cache stays in the app's user cache directory.
  2. Search for a filename/function or select a node. Evidence shows provenance and mappings; Source shows a freshness-checked source excerpt.
  3. Switch between Frontier (local neighborhood), Dense (current-level network), Cluster (communities/components) and Galaxy (rotatable 3D).
  4. Change Architecture through six levels. Follow maps_to or switch planes to move between structure and explanation. Explanation independently chooses one of four learning depths.
  5. In Learn, answer a question before revealing its hint and answer. Knowledge links concepts, prerequisites and attribution.
  6. Adjust impact direction and hops in Evidence to highlight possible dependencies and inspect paths. Reachability is not a failure probability.

Use Ctrl+K to search, Ctrl+O to open, F for focus and Esc to restore chrome. Pin/collapse islands or choose dark/light and opaque panels. The browser imports glyphibe export JSON; full source teaching uses the desktop Rust host.

See the desktop guide, CLI/MCP guide and knowledge/teaching guide.

Limits, evidence and privacy

Observed means syntax facts; Inferred means algorithmic derivation; Proposed means an unaccepted suggestion; Verified requires independent evidence. A structural community does not prove a business domain. Dynamic calls, reflection, unproven cross-language bridges, wildcard/re-exports, TypeScript path aliases and custom Rust source layouts have limitations. LSP enrichment is not implemented. Lessons explain recorded syntax and concepts, not runtime values or proven business intent. Source stays local during core analysis.

The benchmark reports separate measurements from unverified targets. Density changes projection rather than facts; 2D has a 10,000-node cap and Galaxy a 2,000-node maximum with device fallback. Default scan budgets are 20,000 files, 2 MiB per file, 256 MiB source and 250,000 extracted facts. Broader platform and installation claims require recorded validation.

Original code and original default knowledge use MPL-2.0. Dependencies retain their licenses: THIRD_PARTY_NOTICES, inventory, research. Restricted reference source was not copied. Read SECURITY.md, CONTRIBUTING.md and SUPPORT.md.

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