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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Fully Autonomous AI Research System with Self-Evolution, built natively on Claude Code
| Date | Stars |
|---|---|
| 2026-07-31 | 266 |
| 2026-08-06 | 267 |
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<p align="center"> <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> </p> <p align="center"> <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"> </p> > Inspired by the pioneering work of [The AI Scientist](https://github.com/SakanaAI/AI-Scientist), [FARS](https://analemma.ai/blog/introducing-fars/), and [AutoResearch](https://github.com/karpathy/autoresearch), Sibyl takes the vision further by building natively on [Claude Code](https://docs.anthropic.com/en/docs/claude-code) to fully leverage its agent ecosystem — skills, plugins, MCP servers, and multi-agent teams. [中文文档](README_CN.md) 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, experiments expand with better baselines and ablations, writing tightens under 6-agent cross-review, and resource utilization optimizes through GPU scheduling feedback. The quality gate decides when to stop or pivot — no human in the loop. - **Self-Evolving System** — Most AI research tools are static — they run the same way every time. Sibyl evolves. It extracts lessons from every research iteration (issues, success patterns, efficiency metrics), keeps them time-weighted and context-filtered, and injects the relevant ones back into agent prompts. Across projects, the system accumulates institutional knowledge — each project makes every future project better. - **Claude Code Native** — Not a wrapper around API calls. Built directly on Claude Code's architecture (fork skills, agent teams, MCP tools), inheriting its full ecosystem: SSH remote execution, multi-model collaboration (Claude + GPT-5.4 cross-review), Feishu/Lark cloud sync, and more. ### Use Cases - **Automated ML Research** — Give
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:130741d48106a59a, topic:ai-agent, topic:autonomous-agents
matched fp:130741d48106a59a, topic:mcp