This repository is the NVIDIA BioNeMo Agent Toolkit — a collection of 31 skills that give coding/scientific agents (Claude Code, Codex, any SKILL.md-compatible harness) domain knowledge for life-science workflows: protein structure prediction, molecular docking, generative chemistry, and related tasks. Skills execute in the user's own environment (workstation, notebook, cluster) and wrap NVIDIA NIM (NVIDIA Inference Microservices) APIs — both hosted endpoints and local Docker deployments. The toolkit is not an installable Python package; it is a skill catalog with supporting tooling, dual-licensed (Apache-2.0 OR CC-BY-4.0).
This repository is a comprehensive catalog of containerized AI agents for scientific computing, primarily in chemistry, biology, materials science, and engineering domains. It includes over 40 agents (e.g., AutoDock Vina, AlphaFold, OpenFOAM, GWP predictor) each packaged as a Docker container with a utility module, example inputs, and tests. The platform also provides a Python CLI tool (`discovery`) for submitting jobs to an Azure-based HPC service, managing storage, and monitoring job status. The repository is hybrid in nature, containing both production-ready application components (CLI, agent deployment infrastructure) and research artifacts (training pipelines for the GWP predictor, model weights, and data ingestion scripts). The agent architecture follows a consistent pattern: each agent has a `quick_setup`/`save_final_results` lifecycle and is designed to run in a containerized environment with mounted `/input` and `/output` directories. The CLI is well-structured with Typer and Rich, and includes extensive tests. However, the codebase exhibits significant duplication (e.g., `molecular_utils.py` and `io_utils.py` appear identically in multiple agents) and some hardcoded paths, which reduces maintainability. The GWP predictor training pipeline is a notable research component with a multi-step ETL process and active learning, but it is tightly coupled to specific external data sources. Overall, the repository serves as a strong foundation for deploying scientific AI agents on Azure, with moderate reusability of individual components.