agentomics 0.1.0rc1

Package for running Agentomics - an autonomous agentic system for ML model development

Agentomics

NEWS:

• Agentomics to be presented at ISMB 2026
• Agentomics has been published in Bioinformatics journal link to published paper
• Agentomics now supports any data type and supplementary material

Autonomous agentic system for supervised machine learning model development.

Made for biomedical data, Agentomics outperformed human experts and created new state-of-the-art models for problems in Protein Engineering, Drug Discovery, and Regulatory Genomics.

How it works

1. Input is a folder-based dataset split + optional data description
2. Agentomics autonomously experiments with various ML models and strategies
3. Output is a trained model ready for inference and a detailed PDF report summarizing the development process and achieved metrics

For more details see: link to published paper

Quick Start

Install Docker, then:

pip install agentomics

# Set at least one supported provider credential
export OPENROUTER_API_KEY=...   # or OPENAI_API_KEY / ANTHROPIC_API_KEY

# Download an example dataset into ./datasets
agentomics-download-dataset
# To see all available examples add the --list option

agentomics-run

Recommended model: gpt-5.1-codex-max

Outputs are saved to outputs/<agent_id>/, including PDF reports in outputs/<agent_id>/reports/pdf.

See Installation, Datasets, CLI Options, and Running Inference for details.

API Calls

Agentomics can be run via:

• your local Codex subscription via codex login
• a supported provider API key such as OpenRouter, OpenAI, Anthropic, or a configured OpenAI-compatible provider
• local Ollama models for offline/private runs

Documentation

For more details visit https://biogemt.github.io/agentomics-ml/

Key Features

• Generic: Agentomics can use folder-based inputs for classification and regression tasks.
• Secure: Agents execute code securely in Docker with read-only mounts to your file system and are only allowed to write in a Docker Volume.
• Reproducible: Outputs include models, scripts, and conda environments needed to run inference or re-train models with one bash command.
• Trustworthy: If you provide a test set, Agentomics fully abstracts LLMs from accessing it, allowing you to rely on programmaticly computed and reported test set metrics.
• Various LLM providers: OpenAI, OpenRouter, or local models via Ollama
• Reliability: Thanks to our functional validators, Agentomics creates a working model 100% of the time (when using recommended settings).

Run Output Structure Example

Each completed run is written to outputs/<agent_id>/. The key paths are:

outputs/<agent_id>/
├── best_iteration_snapshot/
│   ├── model_training/
│   │   ├── train.py
│   │   └── training_artifacts/
│   ├── model_inference/
│   │   └── inference.py
│   └── runtime_info/
│       └── environment.yml
├── run/
│   ├── shared/
│   │   ├── config.json
│   │   └── splits/
│   └── iteration_*/
└── reports/
    ├── markdown/
    └── pdf/

Use best_iteration_snapshot/ for inference or re-training. run/ keeps the full iterative workspace, and reports/ contains the human-readable summaries.

Roadmap

Agentomics is in active development. We welcome any raised Issues and suggestions. You can also Email Us.

Features coming soon:

• Better local model support and configuration
• Remote GPU support for GCP

Citation

If you use Agentomics in your work, please cite:

Martinek et al. (2026). Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-Art Solutions for Biomedical Machine Learning Tasks. Bioinformatics (https://doi.org/10.1093/bioinformatics/btag250)

License

MIT. See LICENSE.