Astro LLM
БесплатноНе проверенAn MCP server that enables agents to evaluate LLMs daily through capability benchmarks and value alignment tests, providing tools to list models, get almanac, j
Описание
An MCP server that enables agents to evaluate LLMs daily through capability benchmarks and value alignment tests, providing tools to list models, get almanac, judge dilemmas, match user values, and score models.
README
The LLM almanac. One dashboard that tells you, every day, (1) which model is sharp today and whether you should spend or save your tokens, and (2) which model actually shares your values — all wrapped in an astrology / weather-report skin that's fun on the surface and a real personal eval harness underneath.
It looks like a fortune-telling almanac. It's actually a benchmark.
▶ Live demo: https://2-actual-hack.vercel.app · Repo: https://github.com/EthanPany/astro-llm

Five completely different themed layouts (almanac · oracle · court · ink · aurora), a step-by-step value-match quiz, and a live model-fortune board. Full walkthrough: docs/showcase.mp4.
Why
Coding plans (Claude Code, Codex, …) feel slightly different day to day — sometimes the same model thinks longer, sometimes it's sharper, sometimes worse. And which model "gets" your taste is deeply personal. Astro-LLM turns both into a daily reading:
| Organ | Astrology name | What it really measures |
|---|---|---|
| Capability probe | 运势 fortune | Is the model sharp today? — 18 deterministic, auto-graded tasks → an IQ, latency, cost, refusal rate, trend. |
| Value matcher | 缘分 affinity | Does it judge like you? — a bank of subjective dilemmas; we measure agreement (footrule + verdict match). |
| MCP server | — | How an agent runs the test on itself. |
| Harmonize loop | 调和 harmony | Can we nudge a model toward you? — inject your taste, re-test on held-out dilemmas. |
The "luck" is the skin; the numbers underneath are real. See the honesty note at the bottom of the dashboard.
Quickstart (local)
Requirements: Python 3.11+, uv, Node 20+.
cp .env.example .env # paste your API keys (all optional — see below)
make install # backend venv (uv) + frontend deps
make seed # probe live models, write committed seed JSON (optional)
make dev-backend # API on http://localhost:8000 (terminal 1)
make dev-frontend # UI on http://localhost:5173 (terminal 2)
Open http://localhost:5173.
No keys? It still works. The repo ships committed seed data, so the dashboard,
verdict grid and value-match all render offline. Keys unlock live re-probes
(↻ 实时巡检), the live custom-dilemma panel, and the harmonize loop.
Providers
ANTHROPIC_API_KEY, OPENAI_API_KEY, DEEPSEEK_API_KEY are wired and verified.
GEMINI_API_KEY / QWEN_API_KEY are implemented; without them those models show
clearly-labelled simulated data. Adding a key flips them to live on the next make seed.
Deploy (one command)
make up # docker compose: builds the frontend, serves SPA + API on :8000
# or:
docker build -t astro-llm . && docker run -p 8000:8000 --env-file .env astro-llm
In production FastAPI serves the built SPA and the API from the same origin, so there's nothing else to configure. The image bakes in the seed data, so it renders even with zero keys.
Architecture
backend/ FastAPI + httpx + pydantic + SQLite (uv-managed)
app/
providers/ one adapter per provider (OpenAI/Anthropic/DeepSeek/Gemini/Qwen)
benchmarks/ dilemma bank, capability tasks, judge + capability runners,
matching (footrule + agreement), scoring (IQ + suggestion)
astrology/ deterministic zodiac / numerology / fortune / compatibility
loop/ harmonize (train/test guardrail)
storage/ SQLite DAL + seed generator
service.py assembly: runs + astrology -> AlmanacCards + headline
api/routes.py REST API | mcp_server.py MCP tools | scheduler.py
frontend/ React + Vite + TypeScript, handwritten CSS, GSAP, hand-drawn SVG charts
Full data contracts: ARCHITECTURE.md.
API
GET /api/dashboard · GET /api/models · GET /api/dilemmas ·
POST /api/match · POST /api/harmonize · POST /api/judge · GET /api/health
MCP
make mcp # python -m app.mcp_server (stdio)
Tools: list_models, get_almanac, judge_dilemma, match_me,
score_model_against_user. Point any MCP client (e.g. Claude Desktop) at it and an
agent can score itself against the benchmark.
Hourly almanac
Set ENABLE_SCHEDULER=true to probe available models on an interval (PROBE_INTERVAL_MINUTES),
persisting real runs to SQLite so the trend becomes measured data over time.
Tests
make test # 34 tests: astrology, matching, scoring, graders, judge parsing,
# harmonize, API (offline via TestClient + monkeypatched seed)
Design
Handwritten CSS only — no Tailwind, no component library, no chart library. Charts are hand-drawn SVG. Type: Fraunces (display) + Space Grotesk (UI) + Space Mono (numbers). GSAP drives entrance reveals, number count-ups, and chart draw-on.
Honesty note
The capability score is a real stopwatch + autograder. The value-match measures how a model was tuned to present itself, not a hidden soul. The harmonize loop is scored on held-out dilemmas the model never saw during tuning — the guardrail that keeps "alignment" from collapsing into a flattery machine. The astrology is for fun; we say so.
⚠ Rotate any API keys that were shared in plaintext before making this repo public.
Установка Astro LLM
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/EthanPany/astro-llmFAQ
Astro LLM MCP бесплатный?
Да, Astro LLM MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Astro LLM?
Нет, Astro LLM работает без API-ключей и переменных окружения.
Astro LLM — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Astro LLM в Claude Desktop, Claude Code или Cursor?
Открой Astro LLM на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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