This topic covers the platforms and toolchains used to develop, deploy, and operationalize AI-driven protein engineering and computational biology workflows — from “biohub” foundation models and protein‑design suites to marketplaces, data platforms, and research tools that connect models to lab and enterprise systems. No related articles were provided; the overview synthesizes the supplied tool descriptions and current sector trends as of 2026. Why it matters: protein engineering increasingly relies on large, specialized models and end-to-end design suites that must be integrated with experiment tracking, data governance, and automation. Organisations now look for solutions that combine model access, provenance, reproducibility, and secure deployment (cloud, hybrid, or on‑prem) while meeting regulatory and IP constraints. Key tool classes and roles: AI Tool Marketplaces host and version biohub models and protein‑design modules for discovery teams; AI Data Platforms manage sequence, structure, assay and metadata with lineage and access controls; AI Research Tools provide development, orchestration, and CI/CD for model-driven experiments. Representative infrastructure tools include LangChain (developer SDK and orchestration layer for composing LLM/agent-driven pipelines), Kore.ai (enterprise multi‑agent orchestration, governance, and observability), Notion (knowledge, protocols, and experiment documentation), n8n (workflow automation with AI nodes and integrations for lab/inventory systems), and Replit (interactive cloud IDE and lightweight deployment for reproducible model development). Practical considerations: choose platforms that enable reproducible training/inference, fine‑tuning with secure datasets, audit trails for regulatory needs, and easy integration to lab automation. The emphasis is on interoperable pipelines that link protein‑design suites to data and operational tooling rather than single‑vendor lock‑in.