Evo-1 is a research codebase for a lightweight Vision-Language-Action (VLA) model accepted at CVPR 2026. It provides a complete pipeline for training (two-stage pretraining + finetuning using DeepSpeed and InternVL3-1B backbone) and evaluating robot manipulation policies on simulation benchmarks (Meta-World, LIBERO, LIBERO-Plus, RoboTwin) and real robots (xArm6, Aloha, SO100). The primary data flow involves converting raw robot datasets (LeRobot v2.1 format) into normalized state/action pairs with delta-timestep support, computing per-dataset norm statistics, and training a flow-matching action head with a frozen VLM backbone. The repo also includes a WebSocket-based inference server and clients for real-robot deployment. Although the core is research-oriented, it incorporates the full LeRobot framework for data collection and robot control, giving it a hybrid character. The codebase is well-structured with reusable components for dataset processing, normalization, and video handling, but the embedded LeRobot copy and multiple evaluation environments introduce maintenance overhead.