CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration

Complex image creation and editing often exceed a single generation or editing model: a request may require synthesizing, localizing, segmenting, editing, compositing, reading text, and enhancing within one workflow. Such requests are complex in three respects: they involve long-horizon, state-dependent execution, heterogeneous tools whose outputs feed subsequent operations, and multiple visual assets, including multiple input images. Existing multimodal tool-use agents mostly target perception, search, or domain-specific editing, or plan over a single image without training, and large-scale supervision for executable, multi-asset creation trajectories remains scarce. We introduce CanvasCraft, a large-scale multimodal tool-use dataset, and CanvasAgent, an agent that learns to orchestrate heterogeneous visual tools while tracking intermediate visual assets. CanvasCraft contains $140K$ fully annotated executable trajectories and $10K$ RL task specifications stratified by reasoning difficulty, trajectory length, and tool diversity. CanvasAgent is trained with SFT and then GRPO, using a hybrid reward that combines outcome- and process-level signals. On CanvasCraft-Test (250 tasks), CanvasAgent raises the overall reward of its Qwen3-VL-8B base from 0.426 to 0.821, improving both final-image alignment and trajectory quality, and human evaluation shows the same trend.