io.github.Anandb71/arbor
编码与调试by anandb71
面向代码的 Graph-Native 智能层,帮助以图结构方式理解、组织并分析代码关系。
用图结构把分散的代码关系串起来,帮你更快看懂复杂项目与依赖脉络;Graph-Native 智能层让跨文件分析和重构决策都更有把握。
什么是 io.github.Anandb71/arbor?
面向代码的 Graph-Native 智能层,帮助以图结构方式理解、组织并分析代码关系。
README
v2.5.0 — The Last Excuse · PageRank 23x faster (149.8ms → 6.6ms on a 10k-node graph). Indexing goes parallel across every core. A 178k-LOC codebase cold-indexes in 1.6s. "Indexing is slow" was the last argument for letting your agent navigate with
grep -r— it's gone. Every number reproducible: BENCHMARKS.md · Release notes →
Why Arbor
Most AI coding tools treat code as text. Arbor builds a semantic dependency graph — functions, classes, and modules as nodes; calls, imports, and inheritance as edges — then answers execution-aware questions with deterministic precision:
| Question | Arbor answer |
|---|---|
| If I change this symbol, what breaks? | Blast radius with depth, confidence, and risk level |
| Who calls this — directly and transitively? | Caller/callee traversal on the call graph |
| What's the shortest path between A and B? | A* path through real dependencies |
| Is this PR too risky to merge? | CI gate on blast-radius thresholds |
No keyword guessing. No embedding hallucinations. One graph, every interface.
What's new in v2.5.0
| Change | Measured |
|---|---|
| PageRank rewrite — flat call-graph adjacency replaces per-iteration traversal | 149.8ms → 6.6ms on a 10k-node graph (23x), verified side-by-side vs the old implementation |
| Parallel indexing — parse fans out across all cores, deterministic assembly | Arbor: 253ms → 95ms · tokio (178k LOC): 2.7s → 1.6s |
| Warm-start centrality — watcher recomputes seed from previous scores | Converges in ~2 rounds after a one-file patch instead of the full 20-iteration budget |
| Convergence early-exit | Iteration stops at 1e-9 max delta — the budget is a ceiling, not a sentence |
Zero breaking changes — cargo install arbor-graph-cli --force and everything is just faster. Think a number is wrong? cargo bench -p arbor-graph and prove it: BENCHMARKS.md.
| Feature | What it does |
|---|---|
MCP 2026-07-28 | Stateless server/discover, response caching (ttlMs/cacheScope), dual-version fallback for 2025-03-26 clients |
| Tasks extension | tasks/get · tasks/update · tasks/cancel — cold-start indexing returns task handles, not errors |
| MCP Apps | Interactive blast-radius graph (ui://arbor/blast-radius) and architecture map (ui://arbor/architecture-map) inside agent hosts |
| HTTP transport | arbor bridge --http --port 3333 — stateless MCP behind load balancers |
Real get_blast_radius | Git-diff-aware impact analysis via shared arbor-graph::compute_blast_radius |
| Pagination | offset / limit / hasMore on search_symbols and get_map |
| Benchmarks | Criterion suite + CI regression gate — see BENCHMARKS.md |
Quickstart
# Install
cargo install arbor-graph-cli
# Index your project (one command)
cd your-project && arbor setup
# Explore before you edit
arbor map . --exclude-test # ranked project skeleton (~1k tokens)
arbor refactor parse_file # blast radius of changing a symbol
arbor diff # impact of uncommitted git changes
# Wire up your AI agent
claude mcp add --transport stdio --scope project arbor -- arbor bridge
Agent workflow: call get_map first → search_symbols / get_file_graph to locate code → Read only the target file. Full MCP guide →
For AI agents (MCP)
Arbor ships a production MCP server via arbor bridge. Stdio is the default; HTTP is opt-in for remote/enterprise.
# Stdio (Claude, Cursor, VS Code)
arbor bridge
# HTTP (MCP 2026-07-28)
arbor bridge --http --port 3333
Cursor / VS Code
{
"mcpServers": {
"arbor": {
"type": "stdio",
"command": "arbor",
"args": ["bridge"]
}
}
}
Templates: templates/mcp/ · Setup scripts: scripts/setup-mcp.sh · scripts/setup-mcp.ps1
16 MCP tools
| Tier | Tools | Use when |
|---|---|---|
| Orientation | get_map | First call — token-budgeted project skeleton ranked by PageRank |
| Surgical | list_entry_points · get_callers · get_callees · search_symbols · get_file_graph · get_node_detail | Navigate to a specific symbol or file |
| Broad | get_logic_path · analyze_impact · find_path · get_knowledge_path | Trace dependencies, blast radius, paths |
| Agent-native | get_blast_radius · explain_symbol · audit_security · get_architecture_overview · batch_query | PR impact, onboarding, security audit, bulk lookup |
Every tool returns { ok, tool, data, meta: { suggested_next_tool, suggested_next_args } } so agents chain calls without re-prompting.
Registry: io.github.Anandb71/arbor · Official API lookup · Glama listing
CLI reference
| Command | Description |
|---|---|
arbor setup | One-shot init + index |
arbor map | Ranked, token-budgeted project skeleton |
arbor query <term> | Fuzzy symbol search (supports | OR) |
arbor callers / callees <sym> | One-hop graph traversal |
arbor entry-points | HTTP handlers, main, jobs, webhooks |
arbor file-graph <path> | Symbols + edges in one file |
arbor inspect <sym> | Full symbol detail |
arbor path <a> <b> | Shortest call-graph path |
arbor refactor <sym> | Blast radius before refactoring |
arbor diff | Git-change impact report |
arbor check | CI safety gate (--max-blast-radius N) |
arbor summary | Auto-generate PR description |
arbor agent review | Autonomous PR architecture review |
arbor agent onboard | Codebase onboarding guide |
arbor agent guard | Real-time architectural safety gate |
arbor bridge | MCP server (add --http for HTTP transport) |
arbor watch | Live re-index on file changes |
arbor gui | Native desktop UI |
All query commands support --json. map additionally supports --tokens N, --focus "pattern", --focus-changed.
Visual tour
<p align="center"> <img src="docs/assets/visualizer-screenshot.png" alt="Arbor visualizer screenshot" width="760" /> </p>Full recording: media/recording-2026-01-13.mp4
Installation
# Rust / Cargo
cargo install arbor-graph-cli
# Homebrew (macOS/Linux)
brew install Anandb71/tap/arbor
# Scoop (Windows)
scoop bucket add arbor https://github.com/Anandb71/arbor && scoop install arbor
# npm wrapper (cross-platform)
npx @anandb71/arbor-cli
# Docker
docker pull ghcr.io/anandb71/arbor:latest
No-Rust installers:
- macOS/Linux:
curl -fsSL https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.sh | bash - Windows:
irm https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.ps1 | iex
Pinned installs: docs/INSTALL.md
Language support
Production parsers: Rust · TypeScript / JavaScript · Python · Go · Java · C / C++ · C# · Dart
Fallback parsers: Kotlin · Swift · Ruby · PHP · Shell
CI & pull requests
arbor diff --markdown
arbor check --max-blast-radius 30 --markdown
arbor summary
GitHub Action (pre-built binary, ~5s vs ~3–5min compile):
name: Arbor Check
on: [pull_request]
jobs:
arbor:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: Anandb71/arbor@v2.4.0
with:
command: check . --max-blast-radius 30 --markdown
comment-on-pr: true
github-token: ${{ secrets.GITHUB_TOKEN }}
Architecture
arbor-core (Tree-sitter parsing)
└── arbor-graph (petgraph + PageRank + impact analysis)
├── arbor-cli — CLI + MCP bridge
├── arbor-mcp — MCP protocol server
├── arbor-server — WebSocket JSON-RPC
├── arbor-watcher — incremental file watcher
└── arbor-gui — desktop UI
Docs: Quickstart · Architecture · Graph schema · MCP integration · Benchmarks · Roadmap · Philosophy
Release channels: GitHub Releases · crates.io · GHCR · npm · VS Code / Open VSX · Homebrew · Scoop — Releasing guide
Philosophy
- Consumer first — beautiful, intuitive, instantly useful
- Accessibility second — works across ecosystems, runs anywhere
- Affordability next — minimal overhead, from laptops to monoliths
Arbor is local-first: no mandatory data exfiltration, offline-capable, open source. Security policy →
Contributing
cargo build --workspace
cargo test --workspace
cargo clippy --workspace --all-targets --all-features
CONTRIBUTING.md · Good first issues · Code of conduct
Contributors
<!-- CONTRIBUTORS:START --> <p align="center"> <a href="https://github.com/Anandb71" title="Anandb71" style="text-decoration:none; margin:6px; display:inline-block;"> <img src="https://avatars.githubusercontent.com/u/169837340?v=4" alt="Anandb71" width="72" height="72" loading="lazy" style="border-radius:50%; border:2px solid #30363d; box-sizing:border-box;" /> </a> <a href="https://github.com/holg" title="holg" style="text-decoration:none; margin:6px; display:inline-block;"> <img src="https://avatars.githubusercontent.com/u/1383439?v=4" alt="holg" width="72" height="72" loading="lazy" style="border-radius:50%; border:2px solid #30363d; box-sizing:border-box;" /> </a> <a href="https://github.com/cabinlab" title="cabinlab" style="text-decoration:none; margin:6px; display:inline-block;"> <img src="https://avatars.githubusercontent.com/u/66889299?v=4" alt="cabinlab" width="72" height="72" loading="lazy" style="border-radius:50%; border:2px solid #30363d; box-sizing:border-box;" /> </a> <a href="https://github.com/Karthiksenthilkumar1" title="Karthiksenthilkumar1" style="text-decoration:none; margin:6px; display:inline-block;"> <img src="https://avatars.githubusercontent.com/u/182195883?v=4" alt="Karthiksenthilkumar1" width="72" height="72" loading="lazy" style="border-radius:50%; border:2px solid #30363d; box-sizing:border-box;" /> </a> <a href="https://github.com/zacwolfe" title="zacwolfe" style="text-decoration:none; margin:6px; display:inline-block;"> <img src="https://avatars.githubusercontent.com/u/2164736?v=4" alt="zacwolfe" width="72" height="72" loading="lazy" style="border-radius:50%; border:2px solid #30363d; box-sizing:border-box;" /> </a> <a href="https://github.com/sanjayy-j" title="sanjayy-j" style="text-decoration:none; margin:6px; display:inline-block;"> <img src="https://avatars.githubusercontent.com/u/178475117?v=4" alt="sanjayy-j" width="72" height="72" loading="lazy" style="border-radius:50%; border:2px solid #30363d; box-sizing:border-box;" /> </a> <a href="https://github.com/sathguru07" title="sathguru07" style="text-decoration:none; margin:6px; display:inline-block;"> <img src="https://avatars.githubusercontent.com/u/182798669?v=4" alt="sathguru07" width="72" height="72" loading="lazy" style="border-radius:50%; border:2px solid #30363d; box-sizing:border-box;" /> </a> </p> <p align="center"><sub><strong>7 contributors</strong> | <a href="https://github.com/Anandb71/arbor/graphs/contributors">View all</a></sub></p> <!-- CONTRIBUTORS:END -->License
MIT — see LICENSE.
常见问题
io.github.Anandb71/arbor 是什么?
面向代码的 Graph-Native 智能层,帮助以图结构方式理解、组织并分析代码关系。
相关 Skills
前端设计
by anthropics
面向组件、页面、海报和 Web 应用开发,按鲜明视觉方向生成可直接落地的前端代码与高质感 UI,适合做 landing page、Dashboard 或美化现有界面,避开千篇一律的 AI 审美。
✎ 想把页面做得既能上线又有设计感,就用前端设计:组件到整站都能产出,难得的是能避开千篇一律的 AI 味。
网页应用测试
by anthropics
用 Playwright 为本地 Web 应用编写自动化测试,支持启动开发服务器、校验前端交互、排查 UI 异常、抓取截图与浏览器日志,适合调试动态页面和回归验证。
✎ 借助 Playwright 一站式验证本地 Web 应用前端功能,调 UI 时还能同步查看日志和截图,定位问题更快。
网页构建器
by anthropics
面向复杂 claude.ai HTML artifact 开发,快速初始化 React + Tailwind CSS + shadcn/ui 项目并打包为单文件 HTML,适合需要状态管理、路由或多组件交互的页面。
✎ 在 claude.ai 里做复杂网页 Artifact 很省心,多组件、状态和路由都能顺手搭起来,React、Tailwind 与 shadcn/ui 组合效率高、成品也更精致。
相关 MCP Server
GitHub
编辑精选by GitHub
GitHub 是 MCP 官方参考服务器,让 Claude 直接读写你的代码仓库和 Issues。
✎ 这个参考服务器解决了开发者想让 AI 安全访问 GitHub 数据的问题,适合需要自动化代码审查或 Issue 管理的团队。但注意它只是参考实现,生产环境得自己加固安全。
Context7 文档查询
编辑精选by Context7
Context7 是实时拉取最新文档和代码示例的智能助手,让你告别过时资料。
✎ 它能解决开发者查找文档时信息滞后的问题,特别适合快速上手新库或跟进更新。不过,依赖外部源可能导致偶尔的数据延迟,建议结合官方文档使用。
by tldraw
tldraw 是让 AI 助手直接在无限画布上绘图和协作的 MCP 服务器。
✎ 这解决了 AI 只能输出文本、无法视觉化协作的痛点——想象让 Claude 帮你画流程图或白板讨论。最适合需要快速原型设计或头脑风暴的开发者。不过,目前它只是个基础连接器,你得自己搭建画布应用才能发挥全部潜力。