Marvin

The fully autonomous scientist agent.

Define a research mission and Marvin will take care of the rest — reviewing literature, generating truly novel hypotheses, performing experiments, and rigorous analysis — end to end.

Why we built this

The bottleneck in ML research today isn’t compute or data. It’s the preparation. More research is being produced now than at any point in history, and the pace is only increasing. Researchers must ingest and synthesize growing volumes of information before they can actually start their research. And even once they start, a lot of the research cycle is still spent on logistics rather than the science itself.

We built Marvin because nothing out there worked well enough for our own research. The existing options were either too dumb (chasing red herrings down rabbit holes or proposing smart-sounding ideas that were anything but), too wasteful (channeling Ralph Wiggum on experiments that were never going to work), or too opaque (poor documentation, no reasoning traces or “logic trail” that forms the bedrock of scientific reproducibility.)

Marvin takes the information overload and busywork out of research. It does deep research, generates and tests novel and plausible hypotheses, and documents every actionable step with a full logic trail with minimal to no human supervision. Context is kept fresh and up-to-date between all sessions and agents with our custom memory management system. Designed for ML research teams by ML researchers.

The research cycle, automated

Search
Retrieve and cross-check across relevant scientific databases, prior art, and published methodologies.
Hypothesize
Marvin synthesizes literature with experimental findings to generate truly novel but plausible hypotheses.
Design
Experiment in batches with scenario trees by asking the right questions.
Execute
Agents build, audit, review, and execute the plan. Scalable from local, hybrid, and cloud compute infrastructure.
Analyze
Marvin analyzes the entire data corpus to extract actionable insights with the rigor expected of a PhD-level researcher.
Document
Track not just the paper trail, but the logic trail. Every iteration, every interpretation, every decision.

Your repo’s “logic trail”

my-project/
  ├── research_state.md — goals, status, findings
  ├── docs/
  │   ├── iteration_001.md — scoreboard, analysis, lessons
  │   ├── iteration_002.md
  │   └── handoff.md — structured summary for the paper
  ├── experiments/
  │   └── batch_001/
  └── literature/

Get started

$ marvin init
$ marvin plan # scope your research interactively
$ marvin run # autonomous research loop

What’s next

Marvin is in active development. If you’re an ML engineer, scientist, or even a hobbyist interested in trying it, reach out. We’d love to hear about your project’s needs and discuss if Marvin can help.