Most AI coding tools still expect you to sit there, prompting one task at a time. Stint takes a different approach: queue up a list of development goals, walk away, and let multiple Claude instances work in parallel across isolated git branches.
The open-source project handles the orchestration that developers currently do manually. You define goals in plain language, Stint breaks them into subtasks, spins up separate Claude workers - each with its own context window (the amount of text the model can process at once) - and assigns them to individual git branches. When each worker finishes, the tool commits directly and merges results back. A web dashboard shows real-time progress across all active workers.
The isolation model is the interesting part here. Each agent operates in its own git branch, which means one worker's changes can't corrupt another's mid-task. That solves a real problem with multi-agent coding setups, where parallel file edits often create merge conflicts or silently overwrite each other's work.
Setup is minimal compared to heavier agent frameworks. There's no complex configuration or pipeline definition - you queue goals and the system handles decomposition and assignment. That said, this is an early-stage project, and the tradeoff for simplicity is less fine-grained control over how tasks get split and assigned.
Stint sits in a growing category of tools trying to make AI coding agents more autonomous. Projects like Claude Code already let you hand off multi-step tasks, but Stint adds a layer on top for batching and parallelizing multiple independent goals. For solo developers or small teams running repetitive development tasks - migrations, test writing, boilerplate generation - the fire-and-forget model could save real time. For complex, interdependent features, you'll likely still want hands-on oversight.