autoresearch
AI agents running research on single-GPU nanochat training automatically
No commits in 6 months — may not be actively maintained. See maintained alternatives →
Star Growth
Overview
autoresearch 是一个创新的自主AI研究工具，让AI代理在单GPU环境下自动进行LLM训练实验。该工具基于简化的nanochat实现，允许AI代理在夜间自主运行：修改训练代码、进行5分钟训练、评估结果改进、保留或丢弃更改，然后重复这个过程。你醒来时会看到完整的实验日志和（希望）改进的模型。核心理念是通过编程`program.md`文件来指导AI代理，而不是直接修改Python代码。代理可以自由修改架构、超参数、优化器、批量大小等所有训练要素。系统使用固定的5分钟时间预算和val_bpb（验证每字节比特数）作为评估指标，确保不同架构变更之间的公平比较。这代表了自主AI研究的新范式，where研究不再需要人类持续监督。
Deep Analysis
Karpathy's pioneering concept of AI agents autonomously running ML experiments overnight — vs traditional hyperparameter search tools that don't modify architecture or code
⚡ Capabilities
- • Autonomous AI-driven LLM training experiments
- • Automated code modification, training, evaluation loop
- • Fixed 5-minute time-budgeted experiments for fair comparison
- • Experiment logging with val_bpb metric tracking
- • Agent-editable single-file training setup (train.py)
- • Markdown-based agent programming (program.md)
🔗 Integrations
✓ Best For
- ✓ Researchers exploring autonomous ML experiment iteration
- ✓ Learning about AI-driven research automation
- ✓ Overnight autonomous hyperparameter/architecture search
✗ Not Ideal For
- ✗ Production model training or deployment
- ✗ Teams without GPU access
Languages
Deployment
Pricing Detail
⚠ Known Limitations
- ⚠ Requires NVIDIA GPU (tested on H100, works on smaller GPUs with forks)
- ⚠ Single-GPU only, no distributed training
- ⚠ Narrow scope: only LLM training optimization experiments
- ⚠ Results not comparable across different hardware
Pros
- + 完全自主的夜间实验能力，无需人工干预即可进行数百次训练迭代
- + 简洁的三文件架构设计，降低复杂性同时保持实验灵活性
- + 固定时间预算确保不同实验配置之间的公平比较和评估
Cons
- - 限制为单GPU环境，无法扩展到大规模分布式训练
- - 5分钟的固定训练窗口可能限制复杂模型或大数据集的充分训练
- - 需要NVIDIA GPU硬件支持，增加了使用门槛
Use Cases
- • 自动超参数调优，让AI代理探索最佳学习率、批量大小和优化器设置
- • 神经网络架构搜索，自主试验不同的模型设计和层配置
- • 夜间无人值守的研究实验，充分利用计算资源进行持续优化
Getting Started
Alternatives
Works with autoresearch
Tools that integrate with autoresearch, often used together in the same stack.
Compare autoresearch
Maintain autoresearch?
Show your live rank in your README, or put autoresearch in front of every visitor to AgentoolRank.