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Kosmos

Kosmos AI Agent
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(5 / 5 from 1 ratings)

Kosmos is an autonomous AI scientist that automates data-driven discovery across domains like neuroscience, metabolomics, materials science, and genetics. Given an open-ended research objective and one or more datasets, Kosmos runs long research campaigns of up to ~12 hours, iterating between data analysis, literature search, and hypothesis generation. It uses a structured world model to coordinate agents and maintain coherence over hundreds of rollouts, typically executing tens of thousands of lines of code and reading around 1,500 papers per run before synthesizing a fully cited scientific report whose statements are traceable to code cells or primary literature. Independent scientists have estimated that a 20-cycle Kosmos run can compress roughly six months of PhD-level work into a single day, with most statements judged accurate and reproducible. Researchers can access Kosmos via the Edison Scientific platform with a credit-based pricing model, while an open-source implementation on GitHub allows teams to self-host and extend the system using multiple model providers.

AI Agent Store research

What the evidence says about Kosmos

Kosmos is Edison Scientific's hosted AI Scientist for long-horizon, data-driven scientific discovery. Given a research objective and dataset, it coordinates literature search, hypothesis generation, and data-analysis agents over many cycles before producing a traceable scientific report; it is a specialized research agent, not a general chatbot.

Last reviewed August 29, 2026

Verified capabilities

  • Long-horizon scientific research

    Kosmos can run for up to 12 hours, coordinating parallel literature search, hypothesis generation, and data analysis across as many as 200 agent rollouts.[2]

  • Structured research memory

    A structured world model shares information between literature and data-analysis agents so the system can maintain coherence across a large research campaign.[1], [2]

  • Traceable scientific reports

    Report statements are linked back to supporting primary literature or analysis code so researchers can inspect the provenance of conclusions.[1], [2]

Where it fits best

  • Scientists with a defined objective and analyzable dataset who need a long-running literature-and-data investigation with traceable evidence.[1], [2]

Buying and deployment notes

Edison launched Kosmos at $200 per run, expressed as 200 credits at $1 per credit, with some free-tier usage for academics. The announcement says future pricing may rise.[1]

Platforms: Edison Scientific web platform[1]

Deployment: Edison-hosted service[1]

Important considerations
  • Edison says Kosmos can go down rabbit holes or chase statistically significant but scientifically irrelevant findings, and the team sometimes repeats a run to sample different research paths.[1]
  • The launch post describes rough edges in the interface and notes that effective prompting takes practice because the system behaves more like a deep-research instrument than a chatbot.[1]
  • Published accuracy and human-time-equivalence figures are research results and collaborator estimates, not a guarantee that every new run or conclusion will be correct.[1], [2]
Sources and research method (2)

We record only claims tied to public sources checked by our team or listing workflow. Counts above are derived directly from this profile, not a subjective rating.

  1. Kosmos: An AI Scientist for Autonomous Discovery | Edison ScientificOfficial site · checked 2026-08-29
  2. Kosmos: An AI Scientist for Autonomous DiscoveryDocumentation · checked 2026-08-29

Autonomy level

75%

Reasoning: Kosmos demonstrates high autonomy through its ability to run extended, self-directed research campaigns lasting up to 12 hours with minimal human intervention during execution. The system autonomously coordinates 200+ agent rollouts across parallel data analysis and literature search tasks, executing approximately 42,000 lines of code and reading 1...

Comparisons


Custom Comparisons

Some of the use cases of Kosmos:

  • Running end-to-end research campaigns that combine data analysis, literature review, and hypothesis generation.
  • Screening complex biological, chemical, or materials datasets to uncover mechanisms, biomarkers, and targets.
  • Rapidly validating or stress-testing internal results before committing wet-lab, clinical, or engineering resources.
  • Generating fully cited discovery reports where every claim links back to code execution or primary papers.
  • Exploring multiple alternative hypotheses in parallel by running independent trajectories on the same datasets.
  • Building custom AI research workflows by extending the open-source implementation with domain-specific tools.

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