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<img src="image/icon.jpg" width="200" alt="Sibyl Research System — Autonomous AI Scientist">
<h1 align="center">Sibyl Research System</h1>
<p align="center"><b>Fully Autonomous AI Scientist · From Idea to Paper, Zero Human Intervention</b></p>
<p align="center"><i>Multi-Agent Scientific Discovery · GPU Experiment Execution · Self-Evolving Research Pipeline</i></p>
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<a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
<img src="https://img.shields.io/badge/Agents-20+-blue" alt="20+ AI Agents">
<img src="https://img.shields.io/badge/Pipeline-19_Stages-green" alt="19-Stage Pipeline">
<img src="https://img.shields.io/badge/Python-3.12+-3776ab" alt="Python 3.12+">
<img src="https://img.shields.io/badge/Claude_Code-Native-blueviolet" alt="Claude Code Native">
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Inspired by the pioneering work of The AI Scientist, FARS, and AutoResearch, Sibyl takes the vision further by building natively on Claude Code to fully leverage its agent ecosystem — skills, plugins, MCP servers, and multi-agent teams.
中文文档
Sibyl is a fully autonomous AI scientist that drives end-to-end ML research — from literature survey and hypothesis generation to GPU experiment execution and conference-ready paper writing. It operates as an autonomous research organization: 20+ specialized AI agents debate ideas, design and run GPU experiments, write papers, and critically review their own work — all without human intervention.
Key capabilities: automated literature review, multi-agent idea debate, experiment planning & GPU-parallel execution, multi-agent paper writing & peer review, autonomous iteration with quality gates, and cross-project self-evolution. Supports NeurIPS/ICML/ICLR-level output with LaTeX compilation.
What truly sets Sibyl apart is its dual-loop architecture:
- Inner Loop — Research Iteration: Each project automatically iterates across every dimension — refining hypotheses based on experiment results, re-planning experiments, rewriting papers, pivoting to alternative ideas when needed — until quality meets publication standards.
- Outer Loop — System Self-Evolution: Sibyl learns from the research process itself. After every iteration, it classifies issues across 8 categories, accumulates reusable lessons, and automatically updates its own agent prompts, scheduling strategies, and architectural patterns. The system that runs your research is itself getting better at running research.
What Makes Sibyl Different?
- Autonomous Multi-Dimensional Iteration — Not just "run experiments and write a paper." Every aspect of the research improves automatically across iterations: ideas sharpen through multi-agent debate, experim...