Note
To add a tool, please fork our repo and open a pull request! We will help you integrate it. See CONTRIBUTING.md for layout and conventions.
Contact us and let us know how we can help!
Welcome! This repository contains the open-source implementation of proto-tools, a Python package containing a large suite of computational biology and biological AI tools, all accessible through a single, consistent Python interface. Language models, structure predictors, inverse folding, sequence analysis, gene annotation, conformational dynamics, genomic scoring, and more are all available through a single pip install command.
Every tool runs in its own automatically managed isolated environment, so all dependency wrangling is handled for you. In addition, proto-tools implements extensive infrastructure for features such as device management and GPU fan-out, making it easy to call tools in quick succession. You can use it as a standalone Python library, as part of the broader proto-language optimization system, or as an MCP server that exposes the same tools to AI agents. Tools can run on local compute or on Modal.
Proto-tools is open source under an MIT license. Contributions are welcome!
Proto-tools requires Python 3.10+:
pip install git+https://github.com/evo-design/proto-tools.gitTo also run the MCP server, which exposes these tools to coding agents, install the mcp extra:
pip install "proto-tools[mcp] @ git+https://github.com/evo-design/proto-tools.git"Note
A direct PyPI install (pip install proto-tools) will be available soon.
Note
If you are developing or contributing to this project, follow the setup instructions in CONTRIBUTING.md instead.
All persistent data (model weights and tool environments) is cached under the PROTO_HOME directory on first use (defaults to ~/.proto/).
To customize the storage location, you can specify a path via the following environment variable:
# Add to your shell profile:
export PROTO_HOME=/path/to/your/proto_homeFor shared filesystems, model weights can be reused to avoid downloading duplicate copies. The PROTO_MODEL_CACHE environment variable lets you point just the weights at that shared location (sharing tool environments is not recommended): export PROTO_MODEL_CACHE=/path/to/shared/weights. See notes/storage.md for all details and options.
A few tools use gated models or software that require accepting a license / terms-of-use first (e.g. ESM3, AlphaGenome, AlphaFold3, X3DNA). See notes/gated-models.md for the full list and per-model access steps.
proto-tools enables users to scale their tool use beyond their local machine through an
integration with Modal, a serverless compute platform that
allows users to execute models and tools in remote containers. To learn more about
setting up Modal, see proto_tools/modal/README.md
for instructions related to account setup, deploying a tool, and costs, or the Cloud Inference guide for a runnable walkthrough.
Tip
You're all set up! Start with the quickstart, then check out the rest of the guides — short notebooks covering tool environments, persistent execution, device management, parallel multi-GPU runs, and remote execution.
binder_design/ # De novo antibody / binder design pipelines ├── bindcraft/ ├── freebindcraft/ └── germinal/ causal_models/ # Autoregressive sequence models ├── evo1/ ├── evo2/ ├── progen2/ └── progen3/ database_retrieval/ # Sequence and structure database access ├── alphafold_db/ ├── alphamissense_db/ ├── ccd_lookup/ ├── ensembl/ ├── interproscan/ ├── ncbi/ ├── pdb/ ├── pubchem/ ├── sequence_fetch/ └── uniprot/ gene_annotation/ # Sequence annotation ├── crispr_tracr_rna/ ├── meme/ ├── minced/ ├── miranda/ ├── promoter_calculator/ └── pyhmmer/ inverse_folding/ # Sequence design from structures ├── esm_if1/ ├── fampnn/ ├── ligandmpnn/ └── proteinmpnn/ masked_models/ # Masked language models ├── ablang/ ├── codonfm/ ├── esm2/ ├── esm3/ └── esmc/ molecular_docking/ # Protein-ligand binding pose prediction └── vina/ mutagenesis/ # Random sequence mutagenesis ├── random_nucleotide/ └── random_protein/ orf_prediction/ # Open reading frame detection ├── orfipy/ └── prodigal/ rna_splicing/ # RNA splice site prediction ├── pangolin/ ├── splice_transformer/ └── spliceai/ sequence_alignment/ # Sequence search and multiple sequence alignment ├── blast/ ├── mafft/ └── mmseqs2/ sequence_scoring/ # Genomic and regulatory scoring ├── alphagenome/ ├── borzoi/ ├── deeppbs_specificity/ ├── enformer/ ├── malinois/ ├── na_mpnn_specificity/ ├── parade/ ├── primer3/ ├── puffin/ └── segmasker/ structure_alignment/ # Structure comparison ├── foldmason/ ├── foldseek/ ├── pymol_rmsd/ ├── tmalign/ └── usalign/ structure_design/ # De novo structure generation └── rfdiffusion3/ structure_dynamics/ # Conformational dynamics └── bioemu/ structure_prediction/ # 3D structure prediction ├── alphafold2/ ├── alphafold3/ ├── boltz2/ ├── chai1/ ├── esmfold/ ├── esmfold2/ ├── opendde/ ├── protenix/ ├── rf3/ ├── viennarna/ └── x3dna/ structure_scoring/ # Structure quality scoring ├── dssp/ ├── ipsae/ ├── metal3d/ ├── pdockq2/ ├── pyrosetta/ └── structure_metrics/
Runnable walkthroughs of the core framework features live in guides/ and are also available on our docs page:
- Quickstart — installation, the
Input + Config → run → Outputcontract, and running a tool - Tool Environments — how isolated environments are built and cached on first call.
- Tool Persistence — keep models warm across calls
- Device Management — GPU allocation, LRU eviction, CPU offload
- Parallel Execution — fan out work across every GPU with
ToolPool - Cloud Inference — deploy tools to a Modal workspace you own, then call them with
device="modal"
Each specific tool also ships a minimal examples/example.ipynb under proto_tools/tools/{category}/{tool}/examples/.
Run tools through natural language with any coding agent (Claude Code, Gemini CLI, OpenAI Codex CLI, etc.). Point the agent at proto-tools agent-context: it prints a primer covering the Input → Config → run_*() → Output pattern, the offline CLI discovery verbs, persistence and parallel execution, and links to the long-form notes on GitHub. The command ships in the wheel, so it works on a plain pip install with no repo checkout.
If you've cloned the repo for contributing, agents also pick up CLAUDE.md (symlinked as AGENTS.md/GEMINI.md) and the task-specific guides in .claude/skills/ automatically.
See CONTRIBUTING.md for full developer setup, storage configuration, PR format, code style, and testing conventions.
If you use Proto in your research, please cite our preprint:
Merchant AT, Guo D, Viggiano B, Brennan-Almaraz LE, Hur E, Mai T, Yin P, King SH, Ashley E, Hie BL. A high-level programming language for generative biology with Proto. bioRxiv (2026). doi: 10.64898/2026.06.22.733870
@article{Merchant2026.06.22.733870,
author = {Merchant, Aditi T and Guo, Daniel and Viggiano, Ben and Brennan-Almaraz, Lucas Emmanuel and Hur, Evelyn and Mai, Tina and Yin, Peter and King, Samuel H and Ashley, Euan and Hie, Brian L},
title = {A high-level programming language for generative biology with Proto},
elocation-id = {2026.06.22.733870},
year = {2026},
doi = {10.64898/2026.06.22.733870},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/10.64898/2026.06.22.733870},
journal = {bioRxiv}
}Please also cite the underlying tools you use in your work, in addition to our preprint. Every tool's citation is available on its documentation page.
Thank you to 
Modal
for sponsoring the compute used to develop and test the remote execution layer, and for making it
straightforward to host this catalogue.
Thank you to 
Arc Institute
for supporting this work's development.
Thank you to everyone who has contributed to proto-tools. Contributions of every size are
welcome — see CONTRIBUTING.md to get started.