io.github.Anandb71/arbor

v3.1.0 · Find code that calls retiring AI models: arbor retirements lists the direct OpenAI, Anthropic and Google SDK calls in a Python project that use a retired or retiring model, with the listed replacement and what has to change, locally or as a GitHub Action. arbor usages and the MCP tool find_api_usages list every such SDK call with its routing and model. 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:

No keyword guessing. No embedding hallucinations. One graph, every interface.

Where the graph is unsure, it says so — edges carry a confidence, and ambiguous resolutions are labelled rather than hidden. An honest unknown beats a confident wrong answer.

What's new in v3.1.0

A feature release: two commands for Python code that calls AI model APIs. Both run locally, with no account, API key or telemetry.

Python only for now; graph extraction is unchanged (a saved graph is rebuilt once on first use, as after every upgrade). See the 3.0.5 release notes for the previous release.

A fix release. Every item below was a wrong answer, not a missing feature, and each ships with a regression test.

Also new: arbor receipt. After each coding-agent turn it explains, in plain English, what changed and what was touched that you didn't ask for, with arbor receipt undo to put a turn back.

Cached graphs from 3.0.0 are rebuilt automatically on first use.

One fix, measured.

Symbol resolution consults the importing file. When a bare name matched definitions in several modules, resolve_ref fell through to SameDir and attached the edge to whichever definition sat in the caller's own directory — not a dropped edge, a confidently misrouted one, stamped at 0.55 confidence.

GraphBuilder already kept a per-file import map, but only apply_import_validation read it, and that scores an edge after one has been chosen. It never saw the references going to the wrong node. Consulting it between the same-file and same-directory checks keeps a local definition shadowing an import, while letting a written import beat mere adjacency. Resolution::ViaImport scores 0.93, above SameDir's 0.55.

Measured

A fixture of 260 modules across 10 layers, each layer defining the same 26 function names. Ground truth is derived from the generator's own edge list, so the expected answer is exact rather than estimated.

Previously flat at about 22 regardless of the real answer. Now it tracks. Risk on the largest hub moves from LOW to CRITICAL.

Total edge count barely moves (1335 → 1334). That is the signature of misrouting rather than loss: the edges were always there, pointing at the wrong nodes.

Breaking

• Resolution gains a ViaImport variant — an exhaustive match will not compile
• Edges land on different nodes, so cached graphs, stored node ids, and centrality baselines from 2.6.0 will differ

Call cycles are condensed before PageRank. Each strongly connected component is ranked once and that mass is shared across its members, so a closed ring does not fill the top of the ranking and a cycle that calls out keeps its members together. The number CentralityScores reports is still the v2.6.0 percentile, i / (n - 1).

Known and still open

Written down rather than left to be discovered:

• Small targets now over-report (36 → 61, 16 → 46). Safer direction than silence, but not yet correct.
• Dynamic and reflective imports (importlib, __import__, import(), eval(require(...))) are unresolvable by construction and are documented as expected misses in the fixture rather than counted as defects.

Correctness, not speed. Each of these was silently wrong before.

New capability — concept search. Substring matching cannot find get_authenticated from login; they share no substring. Identifiers are now tokenized and expanded through curated concept clusters, and docstrings, signatures, and paths are indexed alongside names. Deterministic, offline, no model. Available on the library as ArborGraph::search_ranked (arbor query remains literal-substring for now).

New capability — hunk-level impact. changed_node_ids_for_ranges keeps only symbols whose lines actually changed, instead of every symbol in a touched file.

Measured on identical node sets, after the duplicate-extraction fix:

Graph caches from earlier versions are invalidated — centrality now means something different, so a stale cache would be read wrong.

Think a number is wrong? cargo bench -p arbor-graph and prove it: BENCHMARKS.md.

Quickstart

# Install (crates.io can lag the latest release: see docs/INSTALL.md)
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
arbor diff --base origin/main       # impact of this branch, as its PR shows it

# Wire up your AI agent
claude mcp add --transport stdio --scope project arbor -- arbor bridge

After every turn: arbor hook claude makes Claude Code show a receipt of what it changed, what it touched that you didn't ask for, and what to test. Receipts →

Agent workflow: call get_map first → search_symbols / get_file_graph to locate code → Read only the target file. Full MCP guide →

Find code that calls retiring AI models

OpenAI shuts down gpt-4o, gpt-4o-mini, gpt-4.1 and gpt-4.1-mini on 14 January 2027, and Anthropic and Google retire models on their own dates. arbor retirements lists the direct openai, anthropic and Google SDK calls that use a retired or retiring model. For each one it shows the replacement the provider lists, the arguments that have to change with it, and the choices your repo makes first (such as whether gpt-6-luna should reason). Calls through LangChain, LiteLLM or other wrappers are not checked yet.

npx @anandb71/arbor-cli retirements .

3 calls use models OpenAI shuts down on 14 Jan 2027 (88 days)
  app/chat.py:12  gpt-4o (MODEL, line 8) → gpt-6-luna
                  also change: max_tokens → max_completion_tokens
                  decide: keep reasoning off (add reasoning_effort="none"; keep temperature) or let the model reason (remove temperature)
  app/chat.py:21  gpt-4o-mini (line 22) → gpt-6-luna  fallback of an environment or config lookup: confirm the deployed value
                  decide: keep reasoning off (add reasoning_effort="none") or let the model reason (no change)
                  check: stop and logit_bias are not supported on gpt-6-luna (400).

• Runs locally. No account, no API key, no telemetry; no code leaves your machine.
• Honest about confidence. A literal is confirmed only where the usage scan has no doubt about the call. os.getenv("MODEL", "gpt-4o") fallbacks, parameter defaults, class attributes a subclass may override and calls whose routing is not certain are marked confirm first with the reason, and fail the check only with --count-unconfirmed. Calls through Azure, OpenRouter or a proxy are listed apart (--include-routed), because the provider's dates may not apply there; a base_url on the provider's own host is a direct call. Models Arbor cannot read, and code it did not analyze, are counted, not hidden.
• Arbor does not decide for you. When the replacement behaves differently by design (gpt-6-luna runs at medium reasoning effort by default, so it can reason, where gpt-4o did not; claude-sonnet-5-5 thinks by default, where the Sonnet models it replaces did not), the report shows both options and what each costs, and changes nothing until you choose.
• CI ready. --json, --markdown, --annotations, --markdown-out <file> and --fail-within <days>. Exit code 1 means a confirmed call uses a retired model, or one retiring inside your window; the shutdown day itself counts as retired (UTC).
• Scope today: Python with the openai, anthropic and Google GenAI SDKs. TypeScript and JavaScript are coming.

GitHub Action (warns by default; it fails the build only if you ask it to):

      - uses: Anandb71/arbor@v3.1.0
        with:
          mode: retirements
          retirements-fail-on: never   # or: retired, or a number of days such as 90

How it works, the data sources and the full scope →

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

17 MCP tools

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

All query commands support --json. map additionally supports --tokens N, --focus "pattern", --focus-changed.

Visual tour

Installation

# macOS / Linux: prebuilt binary from the latest GitHub release
curl -fsSL https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.sh | bash

# Windows (PowerShell)
irm https://raw.githubusercontent.com/Anandb71/arbor/main/scripts/install.ps1 | iex

# Scoop (Windows)
scoop install https://raw.githubusercontent.com/Anandb71/arbor/main/packaging/scoop/arbor.json

# Rust / Cargo (crates.io can lag the latest release)
cargo install arbor-graph-cli

# npm wrapper (cross-platform)
npx @anandb71/arbor-cli

# Docker
docker pull ghcr.io/anandb71/arbor:latest

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

Adding languages →

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@v3.1.0
        with:
          command: check . --max-blast-radius 30 --markdown
          comment-on-pr: true
          github-token: ${{ secrets.GITHUB_TOKEN }}

mode: retirements runs arbor retirements instead. It writes a job summary that lists every call to a retiring model and annotates the 10 most urgent lines, GitHub's limit per step (guide).

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 · Receipts · Benchmarks · Roadmap · Philosophy

Release channels: GitHub Releases · crates.io · GHCR · npm · VS Code / Open VSX · Scoop — Releasing guide

Philosophy

1. Consumer first — beautiful, intuitive, instantly useful
2. Accessibility second — works across ecosystems, runs anywhere
3. 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

License

MIT — see LICENSE.