Eliminating Conda/CUDA dependency hell in computational biology pipelines

Evo Design released proto-tools, an open-source Python package that provides a unified interface for computational biology and biological AI tools, eliminating dependency conflicts by running each tool in an automatically managed isolated environment. The package supports language models, structure predictors, inverse folding, sequence analysis, and more, and is available under an MIT license via pip install from GitHub.

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 https://github.com/evo-design/proto-language optimization system, or through the proto-client https://github.com/evo-design/proto-client Python SDK for hosted access over the Proto Bio API. 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.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 /evo-design/proto-tools/blob/main/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 home For 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 /evo-design/proto-tools/blob/main/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 /evo-design/proto-tools/blob/main/notes/gated-models.md for the full list and per-model access steps. Tip You're all set up To learn what features are available in the library, check out the guides /evo-design/proto-tools/blob/main/guides — four short notebooks covering tool environments, persistent execution, device management, and parallel multi-GPU runs. 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/ ├── esm2/ ├── esm3/ └── esmc/ 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/ ├── 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/ ├── 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/ /evo-design/proto-tools/blob/main/guides and are also available on our docs page https://proto.evodesign.org/docs/tools/introduction : Tool Environments /evo-design/proto-tools/blob/main/guides/tool environments.ipynb — how isolated environments are built and cached on first call. Tool Persistence /evo-design/proto-tools/blob/main/guides/tool persistence.ipynb — keep models warm across calls Device Management /evo-design/proto-tools/blob/main/guides/device management.ipynb — GPU allocation, LRU eviction, CPU offload Parallel Execution /evo-design/proto-tools/blob/main/guides/parallel execution.ipynb — fan out work across every GPU with ToolPool 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/ /evo-design/proto-tools/blob/main/.claude/skills automatically. See CONTRIBUTING.md /evo-design/proto-tools/blob/main/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} }