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Up to three tools. The table shows the data as-is, without picking a “best”.
| Field | goose | act | uv |
|---|---|---|---|
| Platforms | Universal, MCP | GitHub | Universal |
| Local support | Yes | Yes | Yes |
| Paid API | No | No | No |
| License | Apache-2.0 | MIT | MIT OR Apache-2.0 |
| Stars | 55585 | 72435 | 91070 |
| Last activity | October 23, 2026 | — | October 20, 2026 |
| Install complexity | MEDIUM | — | Easy on supported systems; Homebrew must already be installed for this command. Windows and standalone installation options are explained in the guide. |
| Category | Code Review, Multi-Agent, Workflow | Testing, Git, DevOps | Workflow, DevOps |
| Best use | Best for an individual developer or small team that wants an AI agent to complete an inspectable task in a working copy: understand files, make a bounded change, and run a test. Start with manual approval and scope the folder and tools. Do not give it broad access to production or sensitive data. | Best for a developer or small team that repeatedly edits GitHub Actions workflows and wants to test a job before pushing. It provides a local run log, while a final GitHub check remains necessary. It is not suitable when exact hosted-runner parity is required. | For a Python developer or small team that wants a shared setup rather than relying on manually installed packages. Commit the dependency definition and lockfile, then sync the environment and run commands with uv. It does not isolate the operating system or untrusted code, or replace application compatibility tests. |