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Open CoScientist Agents

external · status: dormant · focus: ideation · discipline: general · started: 2025

Project page: https://github.com/conradry/open-coscientist-agents

Source: projects/landscape/open-coscientist.yml

Positioning

An open-source implementation of Google DeepMind's AI co-scientist (arXiv:2502.18864), built on LangGraph and GPT Researcher. Realizes the co-scientist's multi-agent design — literature review, generation, reflection, evolution, meta-review, supervisor, and a tournament-style hypothesis competition with ELO ranking.

Distinctive contribution

Makes a major closed industrial design (DeepMind's co-scientist) inspectable and runnable: every agent role and the ELO tournament loop are present in code, with a Streamlit dashboard for inspecting hypothesis competition transcripts. Useful as a reference implementation against which the original paper's claims can be audited.

Evaluation scores

Dimension Score (0–3) Note
Lifecycle coverage 1 Four upstream stages culminating in ranked hypotheses; no execution or drafting.
Autonomy level 3 Supervisor agent orchestrates the loop without per-step approval.
Architectural transparency 3 MIT-licensed; every agent role visible in source; references the original DeepMind paper.
Inputs supported 2 Research-goal inputs; multi-LLM (Gemini 2.5 Pro + Claude Sonnet 4 + o3) collaboration.
Outputs / reproducibility 1 Tournament transcripts persisted; LLM nondeterminism and live web search limit run-to-run determinism.
Internal evaluation 1 Demo-quality evaluation; tournament metrics are internal to the loop, not external validation.
Openness 3 MIT-licensed; reproducible setup with PyPI install path.
Maturity / traction 1 53 stars; last push 2025-07; appears semi-maintained.
Cross-family policy 3 Requires Gemini 2.5 Pro + Claude Sonnet 4 + o3 in collaboration — explicitly multi-family by design.
Runtime assurance 2 ELO tournament + meta-review + reflection agents provide debate-based runtime gating.
Cross-platform portability 1 LangGraph + GPT-Researcher dependency; single Python entry.

Scored on 2026-05-18. See the evaluation rubric.

Tags

Pipeline stages: literature-discovery literature-synthesis hypothesis-generation research-design

Architectural features: multi-agent tool-use rag-knowledge-base iterative-loop debate-consensus

Inputs: research-goal

Outputs: ranked-hypotheses tournament-transcripts research-report

Data sources: web-search

Knowledge sources: web-search gpt-researcher

Limitations

  • Requires API keys from multiple commercial providers (Gemini + Anthropic + OpenAI) for the full design.
  • Independent implementation; fidelity to the original DeepMind system is the implementer's best effort, not author-verified.
  • Last push 2025-07.

Papers describing this project

  • Towards an AI co-scientist — Gottweis, J., Weng, W.-H., Daryin, A., Tu, T., Palepu, A., Sirkovic, P., et al. (2025). arXiv (Google DeepMind). arXiv:2502.18864