No related articles were provided; this overview synthesizes the supplied tool descriptions and widely reported trends in automated scientific and materials‑science labs as of 2026‑01‑27. Automated lab platforms combine robotics, high‑throughput instrumentation, and machine learning to close the design–test–analyze loop for chemistry, materials and biology. DeepMind’s automated‑lab efforts emphasize advanced predictive models and model‑guided experiment planning, leveraging expertise in large multimodal models and systems research; as part of Alphabet, they can be expected to interoperate with cloud AI and infrastructure services. Competitors span specialist startups, academic consortia and large cloud vendors that stitch together modular stacks: AI Automation Platforms for experiment orchestration; AI Data Platforms for standardized, queryable experimental records and provenance; Low‑Code Workflow Platforms for building reproducible pipelines; AI Research Tools for model development and simulation; and AI Governance Tools for auditing, versioning and safety. Key tooling examples relevant to these stacks include LangChain (agent engineering and stateful orchestration for experiment agents), Claude and Google Gemini (conversational and multimodal assistants for analysis, protocol drafting and interpretability), Notion (knowledge, protocol and SOP management), Replit (rapid, cloud‑native development and deployment of lab automation code), and emerging decentralized infra like Tensorplex Labs for distributed compute and model hosting. Current trends include tighter closed‑loop automation, simulation‑first materials design, low‑code interfaces to lower the barrier to lab automation, and greater emphasis on data standards, reproducibility and governance. Evaluations should weigh model fidelity, integration with lab hardware and LIMS, data traceability, low‑code developer experience, and governance controls for safety and provenance.