by WecoAI · Agent Tool · ★ 704
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Awesome AutoResearch A curated list of AutoResearch use cases with optimization traces and open source implementations. Every entry includes a link to the actual optimization trajectory so you can see what the agent tried, not just the final result. What is AutoResearch? AutoResearch is, at its core, a prompt. Karpathy released it as a single markdown file - , that instructs a coding agent (Claude Code, Codex, or similar) to follow an optimization workflow. The agent edits one file (, that trains a language model), runs for a fixed 5 minutes on a GPU, checks whether the metric improved, and either commits the change or reverts it. Then it loops forever. The specific that ships with AutoResearch is written for one task: training a GPT model. But the structure - iteratively optimizing a file against an evaluation metric, with a discard/keep loop - turns out to be portable. In the weeks since release, the community has adapted it to GPU kernel optimization, template engine optimization, tabular ML engineering, and more. The for each of these looks different, but the loop is the same. Use Cases The original - optimize nanoGPT training code. 20 improv
| Stars | 704 |
| Forks | 52 |
| Category | Agent Tool |
| License | CC0-1.0 |
| Quality Score | 37.652/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-30 |
| Created | 2026-03-22 |
| Platforms | claude-code |
| Est. Tokens | ~17k |
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awesome-autoresearch is Curated list of AutoResearch use cases with optimization traces and open source implementations. It is categorized as a Agent Tool with 704 GitHub stars.
You can find installation instructions and usage details in the awesome-autoresearch GitHub repository at github.com/WecoAI/awesome-autoresearch. The project has 704 stars and 52 forks, indicating an active community.
awesome-autoresearch is released under the CC0-1.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-autoresearch on Agent Skills Hub include awesome-autoresearch, awesome-autoresearch, awesome-openclaw-agents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.