sota-tracker-mcp
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- License 许可
- MIT
- 最近更新
- 2026-08-23
- Stars
- ★ 1
- Forks
- 0
02 资源文件 49
- 参考文档_meta.json294 B
- 参考文档BACKLOG.md4.5 KB
- 参考文档CHANGELOG.md4.3 KB
- 参考文档CLAUDE.md4.4 KB
- 脚本constants.py2.0 KB
- 参考文档CONTRIBUTING.md4.4 KB
- 参考文档data/aa_llm_latest.json4.3 KB
- 参考文档data/aa_video_latest.json196 B
- 参考文档data/civitai_latest.json10.0 KB
- 参考文档data/forbidden.json8.0 KB
- 参考文档data/hardware_profiles.example.json868 B
- 参考文档data/lmarena_latest.json7.3 KB
- 资产文件data/sota_export.csv13.7 KB
- 参考文档data/sota_export.json124.5 KB
- 参考文档EVALUATION.md5.3 KB
- 脚本fetchers/__init__.py459 B
- 脚本fetchers/artificial_analysis.py7.3 KB
- 脚本fetchers/cache_manager.py10.2 KB
- 脚本fetchers/huggingface.py5.2 KB
- 脚本fetchers/lmarena.py5.5 KB
- 脚本init_db.py64.0 KB
- 脚本migrations/__init__.py53 B
- 脚本migrations/migrate.py6.0 KB
- 参考文档package.json957 B
- 其他pyproject.toml484 B
- 参考文档README.md8.4 KB
- 参考文档requirements.txt137 B
- 脚本rest_api.py6.7 KB
- 脚本scrapers/__init__.py410 B
- 脚本scrapers/artificial_analysis.py10.5 KB
- 脚本scrapers/civitai.py5.2 KB
- 脚本scrapers/lmarena.py8.0 KB
- 脚本scrapers/run_all.py10.8 KB
- 脚本scripts/update_download_urls.py4.4 KB
- 参考文档SECURITY.md4.9 KB
- 脚本server.py39.7 KB
- 参考文档SKILLS_VS_MCP.md5.1 KB
- 脚本tests/__init__.py34 B
- 脚本tests/test_edge_cases.py5.6 KB
- 脚本tests/test_migrations.py4.0 KB
- 脚本tests/test_rest_api.py4.9 KB
- 脚本tests/test_server.py9.1 KB
- 脚本update_agents_md.py7.1 KB
- 脚本utils/__init__.py198 B
- 脚本utils/classification.py879 B
- 脚本utils/db.py1.4 KB
- 脚本utils/hardware.py7.4 KB
- 脚本utils/log.py1.3 KB
- 脚本utils/models.py1.6 KB
03 SKILL.md 正文
SOTA Tracker
The definitive open-source database of State-of-the-Art AI models.
Auto-updated daily from LMArena, Artificial Analysis, and HuggingFace.
Why This Exists
AI models are released weekly. Keeping track is impossible. This project:
- Curates authoritative data - LMArena Elo rankings, manual curation for video/image/audio models
- Updates daily via GitHub Actions
- Exports to JSON/CSV/SQLite - Use in your own projects
- Provides multiple interfaces - Static files, REST API, or MCP server
Quick Start: Use the Data
Option 1: Download JSON/CSV
# Latest data (updated daily)
curl -O https://raw.githubusercontent.com/romancircus/sota-tracker-mcp/main/data/sota_export.json
curl -O https://raw.githubusercontent.com/romancircus/sota-tracker-mcp/main/data/sota_export.csv
Option 2: Clone and Query Locally
git clone https://github.com/romancircus/sota-tracker-mcp.git
cd sota-tracker-mcp
# Query with sqlite3
sqlite3 data/sota.db "SELECT name, sota_rank FROM models WHERE category='llm_api' ORDER BY sota_rank LIMIT 10"
# List forbidden/outdated models
sqlite3 data/sota.db "SELECT name, reason, replacement FROM forbidden"
Option 3: Use with Claude Code (Recommended)
The recommended approach for Claude Code users is static file embedding (lower token cost than MCP):
# Set up daily auto-update of CLAUDE.md
cp scripts/update_sota_claude_md.py ~/scripts/
# Enable systemd timer (runs at 6 AM daily)
systemctl --user enable --now sota-update.timer
# Or run manually
python ~/scripts/update_sota_claude_md.py --update
This embeds a compact SOTA summary directly in your ~/.claude/CLAUDE.md file.
Option 4: REST API
# Start the API server
uvicorn rest_api:app --host 0.0.0.0 --port 8000
# Query endpoints
curl "http://localhost:8000/api/v1/models?category=llm_api"
curl "http://localhost:8000/api/v1/forbidden"
curl "http://localhost:8000/api/v1/models/FLUX.1-dev/freshness"
Option 5: MCP Server (Optional)
MCP support is available but disabled by default (higher token cost). To enable:
# Edit .mcp.json to add the server config
cat > .mcp.json << 'EOF'
{
"mcpServers": {
"sota-tracker": {
"command": "python",
"args": ["server.py"]
}
}
}
EOF
Data Sources
| Source | Data | Update Frequency |
|---|---|---|
| LMArena | LLM Elo rankings (6M+ human votes) | Daily |
| Artificial Analysis | LLM benchmarks, pricing, speed | Daily |
| HuggingFace | Model downloads, trending | Daily |
| Manual curation | Video, Image, Audio, Video2Audio models | As needed |
Categories
| Category | Description | Top Models (Feb 2026) |
|---|---|---|
llm_api |
Cloud LLM APIs | Gemini 3 Pro, Grok 4.1, Claude Opus 4.5 |
llm_local |
Local LLMs (GGUF) | Qwen3, Llama 3.3, DeepSeek-V3 |
llm_coding |
Code-focused LLMs | Qwen3-Coder, DeepSeek-V3 |
image_gen |
Image generation | Z-Image-Turbo, FLUX.2-dev, Qwen-Image |
video |
Video generation | LTX-2, Wan 2.2, HunyuanVideo 1.5 |
video2audio |
Video-to-audio (foley) | MMAudio V2 Large |
tts |
Text-to-speech | ChatterboxTTS, F5-TTS |
stt |
Speech-to-text | Whisper Large v3 |
embeddings |
Vector embeddings | BGE-M3 |
REST API Endpoints
| Endpoint | Description |
|---|---|
GET /api/v1/models?category=X |
Get SOTA for a category |
GET /api/v1/models/:name/freshness |
Check if model is current or outdated |
GET /api/v1/forbidden |
List outdated models to avoid |
GET /api/v1/compare?model_a=X&model_b=Y |
Compare two models |
GET /api/v1/recent?days=30 |
Models released in past N days |
GET /api/v1/recommend?task=chat |
Get recommendation for a task |
GET /health |
Health check |
Run Your Own Scraper
# Install dependencies
pip install -r requirements.txt
pip install playwright
playwright install chromium
# Run all scrapers
python scrapers/run_all.py --export
# Output:
# data/sota_export.json
# data/sota_export.csv
# data/lmarena_latest.json
GitHub Actions (Auto-Update)
This repo uses GitHub Actions to:
- Daily: Scrape all sources, update database, commit changes
- Weekly: Create a tagged release with JSON/CSV exports
To enable on your fork:
- Fork this repo
- Go to Settings → Actions → Enable workflows
- Data will auto-update daily at 6 AM UTC
File Structure
sota-tracker-mcp/
├── server.py # MCP server (optional)
├── rest_api.py # REST API server
├── init_db.py # Database initialization + seeding
├── requirements.txt # Dependencies
├── data/
│ ├── sota.db # SQLite database
│ ├── sota_export.json # Full JSON export
│ ├── sota_export.csv # CSV export
│ └── forbidden.json # Outdated models list
├── scrapers/
│ ├── lmarena.py # LMArena scraper (Playwright)
│ ├── artificial_analysis.py # AA scraper (Playwright)
│ └── run_all.py # Unified runner
├── fetchers/
│ ├── huggingface.py # HuggingFace API
│ └── cache_manager.py # Smart caching
└── .github/workflows/
└── daily-scrape.yml # GitHub Actions workflow
Contributing
Found a model that's missing or incorrectly ranked?
- For manual additions: Edit
init_db.pyand submit a PR - For scraper improvements: Edit files in
scrapers/ - For new data sources: Add a new scraper and update
run_all.py
See CONTRIBUTING.md for full developer setup and PR process.
OpenCode / Agents.md Integration
The repo now supports updating agents.md files for OpenCode agents:
# Update your agents.md with latest SOTA data
python update_agents_md.py
# Minimal version (top 1 model per category, lightweight)
python update_agents_md.py --minimal
# Custom categories and limit
python update_agents_md.py --categories llm_local image_gen --limit 3
# Force refresh from sources first
python update_agents_md.py --refresh
Automation
Add to your cron or systemd timer for daily updates:
# ~: crontab -e
@daily python ~/Apps/sota-tracker-mcp/update_agents_md.py
Or systemd:
# ~/.config/systemd/user/sota-update.service
[Unit]
Description=Update SOTA models for agents
After=network.target
[Service]
ExecStart=%h/Apps/sota-tracker-mcp/update_agents_md.py
[Install]
WantedBy=default.target
# ~/.config/systemd/user/sota-update.timer
[Unit]
Description=Daily SOTA data update
OnCalendar=daily
AccuracySec=1h
[Install]
WantedBy=timers.target
# Enable
systemctl --user enable --now sota-update.timer
See CONTRIBUTING.md for full setup guide
Data Attribution & Legal
This project aggregates publicly available benchmark data from third-party sources. We do not claim ownership of rankings, Elo scores, or benchmark results.
Data Sources (Used With Permission)
| Source | Data | Permission |
|---|---|---|
| LMArena | Chatbot Arena Elo rankings | robots.txt: Allow: / |
| Artificial Analysis | LLM quality benchmarks | robots.txt: Allow: / (explicitly allows AI crawlers) |
| HuggingFace | Model metadata, downloads | Public API |
| Open LLM Leaderboard | Open-source LLM benchmarks | CC-BY license |
Disclaimer
- All benchmark scores and rankings are the intellectual work of their respective sources
- This project provides aggregation and tooling, not original benchmark data
- Data is scraped once daily to minimize server load
- If you are a data source and wish to be excluded, please open an issue
Fair Use
This project:
- Aggregates factual data (not copyrightable)
- Adds value through tooling (API server, unified format, forbidden list)
- Attributes all sources with links
- Does not compete commercially with sources
- Respects robots.txt permissions
License
MIT - See LICENSE for details.
The code in this repository is MIT licensed. The data belongs to its respective sources (see attribution above).
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