Auto-claude-code-research-in-sleep vs openevolve
Auto-claude-code-research-in-sleep is much bigger: 15.7k stars against 7.3k. Over the days we have tracked them Auto-claude-code-research-in-sleep moved +14.6% and openevolve +12.8%, so Auto-claude-code-research-in-sleep is growing faster right now.
openevolve leads on privacy and model freedom and setup ease. Auto-claude-code-research-in-sleep does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Provides a lightweight, skill-based autonomous research workflow with built-in cross-model reviewer loops and deterministic integrity audits (Anti-Autoresearch), targeted at reproducible ML research rather than generic agent tasks.
- Stars
- 15.7k
- Tracked growth
- +14.6%
- Maturity
- ●●●●●
- Last commit
- 2d ago
- Language
- Python
- License
- MIT
- Cost to run
- Your API key or model subscription
Combines MAP-Elites, island-based evolution and LLM ensembles to autonomously discover novel, hardware-optimized algorithms with reproducible scientific pipelines.
- Stars
- 7.3k
- Tracked growth
- +12.8%
- Maturity
- ●●●●●
- Last commit
- 48d ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Your API key; per-iteration LLM costs (local models nearly free)
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | Auto-claude-code-research-in-sleep | openevolve |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Diff + related files | ●●●●● File + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Severity gating & audits | ●●●●● Cascade & novelty filters |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Skill & per-call config | ●●●●● Extensive configs & prompts |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud APIs (your key) | ●●●●● Run fully local |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Several providers | ●●●●● Any provider & local |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Config + API key | ●●●●● One-command start |
Which one to pick
Pick Auto-claude-code-research-in-sleep if…
Research-focused — pick ARIS when you need an audited, multi-model research automation stack with reviewer gating, integrity checks, and per-skill customization.
Pick openevolve if…
Research-grade reproducibility — pick this when you need deterministic, scientific-grade autonomous algorithm discovery and hardware-aware code optimization with flexible local or cloud LLM backends.
- Privacy: Run fully local (5/5 against 3/5)
- Model freedom: Any provider & local (5/5 against 3/5)
- Setup ease: One-command start (5/5 against 3/5)
What people want from each one
wanshuiyin/Auto-claude-code-research-in-sleep
algorithmicsuperintelligence/openevolve
Questions people ask
Is Auto-claude-code-research-in-sleep better than openevolve?
openevolve leads on privacy and model freedom and setup ease. Auto-claude-code-research-in-sleep does not take any axis by a clear margin. Auto-claude-code-research-in-sleep is worth picking when research-focused — pick ARIS when you need an audited, multi-model research automation stack with reviewer gating, integrity checks, and per-skill customization.
Which of Auto-claude-code-research-in-sleep and openevolve keeps my code private?
Auto-claude-code-research-in-sleep: Cloud APIs (your key) (3/5). openevolve: Run fully local (5/5).
What does each one cost to run?
Auto-claude-code-research-in-sleep: Your API key or model subscription. openevolve: Your API key; per-iteration LLM costs (local models nearly free).
Full profiles: wanshuiyin/Auto-claude-code-research-in-sleep and algorithmicsuperintelligence/openevolve. Everything else in ML experiment agents.