EvoScientist vs openevolve
The two are close in size: 4.6k stars for EvoScientist, 7.3k for openevolve. Over the days we have tracked them EvoScientist moved +7.2% and openevolve +12.8%, so openevolve is growing faster right now.
openevolve leads on model freedom and setup ease. 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
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)
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 | EvoScientist | 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. | ●●●●● Basic filtering & gating | ●●●●● Cascade & novelty filters |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Extensible skills & config | ●●●●● Extensive configs & prompts |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Run fully local |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Multiple cloud 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 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.
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.
- 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
EvoScientist/EvoScientist
algorithmicsuperintelligence/openevolve
Questions people ask
Is EvoScientist better than openevolve?
openevolve leads on model freedom and setup ease. EvoScientist does not take any axis by a clear margin. EvoScientist is worth picking when 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.
Which of EvoScientist and openevolve keeps my code private?
EvoScientist: Self-hostable (4/5). openevolve: Run fully local (5/5).
What does each one cost to run?
EvoScientist: Requires your cloud model API keys (per-use billing). openevolve: Your API key; per-iteration LLM costs (local models nearly free).
Full profiles: EvoScientist/EvoScientist and algorithmicsuperintelligence/openevolve. Everything else in ML experiment agents.