Auto-claude-code-research-in-sleep vs EvoScientist
Auto-claude-code-research-in-sleep is much bigger: 17.8k stars against 5.0k. Over the days we have tracked them Auto-claude-code-research-in-sleep moved +29.2% and EvoScientist +15.2%, so Auto-claude-code-research-in-sleep is growing faster right now.
Auto-claude-code-research-in-sleep leads on noise control. EvoScientist 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
- 17.8k
- Tracked growth
- +29.2%
- Maturity
- ●●●●●
- Last commit
- 4h ago
- Language
- Python
- License
- MIT
- Cost to run
- Your API key or model subscription
Auto-distills a growing memory/knowledge graph into reusable AutoSkills and a multi-agent research loop, enabling long-running self-evolving AI scientists.
- Stars
- 5.0k
- Tracked growth
- +15.2%
- Maturity
- ●●●●●
- Last commit
- 1h ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Requires your cloud model API keys (per-use billing)
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 | EvoScientist |
|---|---|---|
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 | ●●●●● Diff + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Severity gating & audits | ●●●●● Basic filtering & gating |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Skill & per-call config | ●●●●● Extensible skills & config |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud APIs (your key) | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Several providers | ●●●●● Multiple cloud providers |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Config + API key | ●●●●● Config + API key |
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.
- Noise control: Severity gating & audits (5/5 against 3/5)
Pick EvoScientist if…
Self-evolving research — pick EvoScientist when you want an opinionated, multi-agent system that autonomously builds a persistent knowledge graph and drafts reusable skills for iterative scientific workflows.
What people want from each one
wanshuiyin/Auto-claude-code-research-in-sleep
EvoScientist/EvoScientist
Questions people ask
Is Auto-claude-code-research-in-sleep better than EvoScientist?
Auto-claude-code-research-in-sleep leads on noise control. EvoScientist 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 EvoScientist keeps my code private?
Auto-claude-code-research-in-sleep: Cloud APIs (your key) (3/5). EvoScientist: Self-hostable (4/5).
What does each one cost to run?
Auto-claude-code-research-in-sleep: Your API key or model subscription. EvoScientist: Requires your cloud model API keys (per-use billing).
Full profiles: wanshuiyin/Auto-claude-code-research-in-sleep and EvoScientist/EvoScientist. Everything else in ML experiment agents.