AI-Scientist-v2 vs EvoScientist
The two are close in size: 7.1k stars for AI-Scientist-v2, 4.6k for EvoScientist. Over the days we have tracked them AI-Scientist-v2 moved +2.7% and EvoScientist +7.2%, so EvoScientist is growing faster right now.
EvoScientist leads on customization. AI-Scientist-v2 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
End-to-end autonomous scientific discovery: it uses progressive agentic tree search to ideate, run experiments, analyze results, and draft papers without human-authored templates.
- Stars
- 7.1k
- Tracked growth
- +2.7%
- Maturity
- ●●●●●
- Last commit
- 260d ago
- Language
- Python
- License
- NOASSERTION
- Cost to run
- Your API key, per-run (README cites ≈$15–$20 for experiments + ≈$5 for writing with default models)
Auto-distills a growing memory/knowledge graph into reusable AutoSkills and a multi-agent research loop, enabling long-running self-evolving AI scientists.
- Stars
- 4.6k
- Tracked growth
- +7.2%
- Maturity
- ●●●●●
- Last commit
- 3h 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 | AI-Scientist-v2 | 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. | ●●●●● Related files | ●●●●● Diff + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Config-based controls | ●●●●● Basic filtering & gating |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Configurable params | ●●●●● Extensible skills & config |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud APIs (your keys) | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Multiple providers | ●●●●● Multiple cloud providers |
Setup ease What it takes to get a first useful run out of it. | ●●●●● GPU install required | ●●●●● Config + API key |
Which one to pick
Pick AI-Scientist-v2 if…
End-to-end autonomy — pick this when you want a research pipeline that generates hypotheses, executes experiments, and produces writeups using configurable agentic tree search across multiple LLM providers.
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.
- Customization: Extensible skills & config (5/5 against 3/5)
What people want from each one
SakanaAI/AI-Scientist-v2
EvoScientist/EvoScientist
Questions people ask
Is AI-Scientist-v2 better than EvoScientist?
EvoScientist leads on customization. AI-Scientist-v2 does not take any axis by a clear margin. AI-Scientist-v2 is worth picking when end-to-end autonomy — pick this when you want a research pipeline that generates hypotheses, executes experiments, and produces writeups using configurable agentic tree search across multiple LLM providers.
Which of AI-Scientist-v2 and EvoScientist keeps my code private?
AI-Scientist-v2: Cloud APIs (your keys) (3/5). EvoScientist: Self-hostable (4/5).
What does each one cost to run?
AI-Scientist-v2: Your API key, per-run (README cites ≈$15–$20 for experiments + ≈$5 for writing with default models). EvoScientist: Requires your cloud model API keys (per-use billing).
Full profiles: SakanaAI/AI-Scientist-v2 and EvoScientist/EvoScientist. Everything else in ML experiment agents.