scientific-discovery

Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞

A blueprint-driven AutoResearch runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review.

A curated list of autonomous improvement loops, research agents, and autoresearch-style systems inspired by Karpathy's autoresearch.

Open-source autoresearch powered by autonomous coding agents. Run Claude Code, OpenCode, and Codex with grading, shared knowledge, and multi-agent evolution. Accepted at COLM 2026.

An open, curated collection of Agent Skills for scientific research — clone it, use it, extend it!

Any research. One Claw. 🦞 From any materials to research with fully autonomous & skill-driven researcher.

An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.

900+ pure-markdown skills for autonomous AI research, organized as 9 freely-composable packages over a 4-layer hierarchy (Campaign → Strategy → Tactic → SOP). Non-linear orchestration with backtracking, 6 MCP integrations. The AI is the researcher — you set the direction.

Fully Autonomous AI Research System with Self-Evolution, built natively on Claude Code

[ICLR 2025 Oral] This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Large Language Models

A virtual environment for developing and evaluating automated scientific discovery agents.

This is the official repository for HypoGeniC (Hypothesis Generation in Context) and HypoRefine, which are automated, data-driven tools that leverage large language models to generate hypothesis for open-domain research. For more details, please see the original paper using the link below.

Turn ideas into autonomous research with Git-like evidence histories—inspectable, reproducible, and reversible.

[ICML2025 Oral] LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

Source code for <Large language models surpass human experts in predicting neuroscience results>

PiFlow: Principle-aware Scientific Discovery with Multi-Agent Collaboration

Idea-Catalyst is a metacognition-driven framework for helping humans and LLMs boost their creativity during the scientific brainstorming process.

Claude Code plugin for autonomous AI research — multi-agent loops take a bare topic all the way to running experiments, with no human-written experimental code.

A curated guide for LLM-agent-driven scientific research automation — from getting started to the frontier.

A curated, accuracy-first list of benchmarks for evaluating LLMs on scientific reasoning and discovery — math, physics, chemistry, materials, biology, and agentic science.