Despite rapid advances in vision-language-action (VLA) models, deploying robot intelligence remains fragmented: different SDKs for different robots, different policy frameworks with incompatible interfaces, an unbridged simulation-to-reality gap, and training pipelines that demand specialist expertise. We present Strands Robots, an open-source Python SDK that unifies the complete robot lifecycle—simulation, control, training, and deployment—behind natural language. Our central contribution is a Policy abstraction layer with a plugin registry supporting 18 VLA/WFM providers (50+ aliases) under a single three-method interface, enabling zero-code-change transfer between simulation and real hardware. We scale this abstraction across three axes: robot diversity (35 bundled models from MuJoCo Menagerie spanning arms, humanoids, quadrupeds, and dexterous hands), simulation fidelity (three backends: MuJoCo CPU, Newton GPU-differentiable with 4,096+ parallel environments, and Isaac Sim with RTX rendering), and policy ecosystem breadth (from 42M-parameter ONNX humanoid controllers at 135 Hz to 14B-parameter world action models). Built on the Strands Agents framework, every capability is exposed as a tool callable via natural language, enabling AI agents to autonomously design scenes, run experiments, collect data, train policies, and deploy to hardware. We demonstrate that this unified approach achieves practical results: GEAR-SONIC humanoid whole-body control at 135 Hz on Jetson AGX Thor, Cosmos Predict 2.5 achieving 98.5% on LIBERO-10, and seamless integration with NVIDIA's GR00T N1.5/N1.6, Cosmos Transfer 2.5, DreamGen, and DreamZero pipelines. Strands Robotsestablishes a practical foundation for autonomous robot development where the barrier between idea and physical action is a single line of Python.