I've been working on RepoBird (https://repobird.ai), a cloud coding agent built around one-shot execution instead of a chat loop: you describe a task once (usually as an issue), an agent picks it up in an isolated cloud sandbox, and you get back a pull request. Works with GitHub and GitLab.

The thing I actually wanted to solve was parallelism. Anyone who has tried running multiple agents locally knows the failure mode: git worktrees each needing their own dependency install, agents overwriting each other's files, a machine brought to its knees by three model contexts. In RepoBird every task gets its own sandboxed VM, so submitting 20 tasks is no harder than submitting one.



Three ways to trigger a run:



\- The dashboard — pick a repo, describe the task, launch

\- The CLI — \`repobird run task.json --follow\`, with JSON/YAML/Markdown/stdin task input for scripting bulk runs

\- A GitHub comment — \`@repobird run\` on any issue, with inline options like \`base:main pr-target:develop\`



One design decision that turned out to matter a lot: the agent never runs Git. It only writes code. Post-processing builds the commit from an approved staged diff, pushes to a generated output branch, and opens the PR — so Git operations are deterministic, scratch files never leak into diffs, and direct writes to your base branch are impossible by construction.



If the PR needs changes, you comment on it with new instructions and the agent pushes follow-up commits to the same branch.



There's a free tier if you want to try it on a real issue. Curious how others here are handling the "run many agents at once" problem — is anyone actually happy with local worktree setups, or has everyone hit the same wall?



Getting Started with RepoBird.ai - One-Shot Cloud Coding Agents