synthetic-sciences/openscience
The open-source AI workbench for scientific research observed · 2026-08-28
Health v2 · maintenance only
80/100
- Activity 99
- Release rhythm 99
- Longevity 4
Flags: young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 0
- age_days: 61
- days_rel: 7
- days_push: 7
- n_releases_24m: 46
Adoption not part of the score
3342 stars · 452 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
OpenScience is an open-source AI workbench for scientific research that runs the full research loop: literature review, hypothesis formation, code writing and execution, experiments, and write-up. It provides a browser-based workspace with an adaptive research agent, 311 bundled domain skills, and direct access to major scientific databases like UniProt, PDB, and arXiv.
Use cases
- automate literature review for a research topic
- run machine learning experiments from a natural language goal
- query scientific databases like UniProt and arXiv via an AI agent
- write and execute analysis code for biology or chemistry research
- generate research write-ups and LaTeX papers automatically
- fine-tune and evaluate models with DeepSpeed, PEFT, or TRL
- manage a research workspace with files, terminal, and session history
When to choose
- you want an AI collaborator to run end-to-end scientific research workflows
- you need model-agnostic tooling that works with frontier, open-weight, or local models
- you work in ML, biology, physics, or chemistry and want integrated domain skills and database access
- you prefer open-source tools with an extensible plugin and MCP ecosystem
When to avoid
- you need a fully offline tool with no account linking
- you only want a simple chatbot without code execution or experiment running
- your research domain lacks bundled skills and requires heavy customization
- you require guaranteed reproducibility of agent-driven experiments
Facets
application · maturity active
agent-framework llm-inference rag machine-learning developer-tools cli chat-interface artificial-intelligence large-language-models data-science developer-tools bun browser cli cross-platform ai-workbench scientific-research co-scientist literature-review hypothesis-testing scientific-databases research-automation model-agnostic typescript-sdk mcp ai-agents research nodejs
2 sources
- readme: https://github.com/synthetic-sciences/openscience · fetched 2026-08-28 · 3232105eaa90
- homepage: https://openscience.sh · fetched 2026-08-29 · b9888cb26200
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| synthetic-sciences/openscience | main | 80 |
For agents
markdown · JSON · MCP: product_card(name="synthetic-sciences/openscience")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem