LibrAIum 📚

Your personal library of best-practice GitHub repositories — curated by you, grown with AI.

LibrAIum（ライブラリアム）は、厳選した GitHub リポジトリをローカル Git リポジトリで管理し、MCP サーバー経由で Claude Code から直接活用できる個人専用デスクトップアプリです。

LibrAIum is a local-first desktop app for curating the best-practice public GitHub repositories you actually trust — across AI agents, web apps, games, DevOps, and any genre you define. Its differentiator: the library doubles as an MCP server, so Claude Code can search it, read each entry's Reception (real-world community signal — what issues complain about, who adopts it, known limitations), and recommend the right repos for your next project.

"Suggest the 3 best repos from my library for a RAG agent combining a vector DB and knowledge management — with setup commands."

43 repositories across 18 categories — browse them all in CATALOG.md.

• Top languages: Go (9), TypeScript (8), Python (7), C++ (6), Rust (5)

• Freshness: 1 stale, 0 archived

• Recently added: dubinc/dub, YOURLS/YOURLS, langgenius/dify, ollama/ollama, ggml-org/whisper.cpp

Features (v1.0)

• Git-native storage — one Markdown file per repo (YAML frontmatter + body) in a local git repository. Diff, merge, and back up your knowledge like code.

• Reception — the primary layer. Every entry pairs structured metadata (stars, language, freshness) with synthesized third-party signal: what issues complain about, who adopts it, known limitations, and what people migrate to — each claim sourced, and stamped with a reception_gathered date so its freshness is tracked like metadata. Where you've used a repo firsthand, an optional Personal Notes section records your own take.

• Desktop GUI (Tauri v2 + Svelte 5) — dashboard, instant fuzzy search with filters, entry editing, category master management, and a Git panel (status / commit / push).

• GitHub metadata refresh — single or bulk refresh via the GitHub API; entries automatically flagged stale when a repo stops moving, with fresher alternatives suggested from your own shelves.

• MCP server for Claude Code — eight tools: search_repos, get_repo_details, get_related, find_by_reception, suggest_for_new_project, compare_repos, get_library_overview, add_repo.

• Awesome-list export — publish your curation as a standard awesome-list Markdown document.

• Private by design — your library data stays on your machine; the GitHub token lives in the OS keychain. Network access is explicit and on-demand only (metadata refresh, add, scouting, Reception collection), and outbound requests carry only public repo identifiers — no telemetry.

Related MCP server: mcp-github-server

Quick start

Prerequisites: Rust, Node.js ≥ 20, git. (macOS/Linux/Windows; on Linux install the Tauri v2 system deps.)

npm install                 # frontend deps
npm run tauri dev           # launch the app (compiles Rust on first run)

Package a release build:

npm run tauri build

On first launch the app uses the repository's data/ directory when present (dev mode), otherwise it bootstraps ~/LibrAIum/data as a fresh git repository with the default category master. Point it anywhere via Settings → Data directory.

The MCP server

Register LibrAIum with Claude Code (user scope — available in every repo):

(cd mcp-server && npm install)              # once
node scripts/register-mcp.mjs               # show the plan (executes nothing)
node scripts/register-mcp.mjs --yes --with-skill   # register + install the libraium-first skill
node scripts/register-mcp.mjs --doctor      # diagnose: registration, data dir, live entry count

The script bakes in absolute paths from its own location and is idempotent — re-run it after moving the checkout. Manual registration (project scope, env-var form) still works: claude mcp add libraium -e LIBRAIUM_DATA_DIR="$PWD/data" -- node "$PWD/mcp-server/index.js".

Every entry is also exposed as an MCP resource (entry://<category>/<slug>), so @libraium autocomplete in Claude Code pulls a full entry — frontmatter, summary, its Reception, and any Personal Notes — into context with no tool call.

The server resolves its data directory from --data-dir, $LIBRAIUM_DATA_DIR, ./data, the repo checkout, or ~/LibrAIum/data — in that order.

Use it from every project

Register the server at user scope and install the libraium-first skill, and Claude Code will consult your library before every dependency decision — in any repo on the machine, quoting your Personal Notes as evidence. One-page setup (including a paste-in CLAUDE.md block): docs/library-first-setup.md.

Data model

data/
├── entries/<category>/<owner-repo>.md    # one repo = one file
└── master/categories.yaml                # category master (GUI-editable)

---
github_url: https://github.com/qdrant/qdrant
full_name: qdrant/qdrant
category: ai-agent
tags: [vector-db, rag, similarity-search, rust]
stars: 21400
language: Rust
last_github_push: 2026-07-05
last_checked: 2026-07-08
status: active        # active | stale | archived
source: manual        # manual | mcp | x-collection
added_date: 2026-06-20
reception_gathered: 2026-07-08   # when the ## Reception below was last synthesized
---

# qdrant

High-performance vector database…

## Reception
- Recurring complaints, named adopters, known limitations, migration signal — each sourced.

## Personal Notes        # optional — only where you've used it firsthand
- My default vector DB for RAG prototypes…

Development

npm install && (cd mcp-server && npm install)   # once, after cloning
bash scripts/verify-all.sh      # the full suite: data validation → cargo test →
                                # vite build + frontend unit tests → MCP unit+smoke
                                # tests → Rust⇔Node conformance → app binary build
                                # (CI runs this too)

Or piecewise — note that npm run build must come before the first cargo test on a fresh clone (Tauri's generate_context! embeds dist/ at compile time; verify-all.sh reorders this automatically):

npm run build                   # frontend production build
npm test                        # frontend unit tests (markdown-renderer hardening)
cd src-tauri && cargo test      # Rust unit tests (data/git/search/github layers)
cd mcp-server && npm test       # store/suggest unit tests + MCP stdio smoke test
node scripts/refresh-metadata.mjs   # dry-run GitHub metadata refresh (--write to apply)
bash scripts/make-icons.sh      # regenerate app icons (macOS)

UI work is governed by DESIGN.md — the Flexoki paper-and-ink design contract (tokens live in src/styles.css). For UI preview without compiling the Rust backend, plain npm run dev in a browser auto-installs a Tauri IPC mock (src/lib/dev/mock.js) with seeded sample data; the mock is dev-only and never reaches production builds.

Architecture (see LibrAIum_完全設計書_v1.0.md for the full Japanese design document):

Tauri v2 desktop app
├── src/                  Svelte 5 GUI (dashboard, library, detail, categories, settings/git)
├── src-tauri/src/        Rust core
│   ├── store.rs            entry CRUD, frontmatter, dedup, awesome export
│   ├── categories.rs       category master
│   ├── search.rs           fuzzy search + filters, alternative suggestions
│   ├── github.rs           metadata refresh + stale detection
│   ├── gitops.rs           git status/commit/push (wraps the git CLI)
│   └── settings.rs         config + data-dir resolution + keychain-backed token
└── mcp-server/           Node stdio MCP server (mirrors the Rust data layer)

Roadmap (v1.5+)

• X (Twitter) auto-collection pipeline with approval queue

• Semantic search over entries via local embeddings (ONNX)

• Project bootstrap generation from suggestions

• Richer multi-repo composition tools over MCP

License

MIT