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Up to three tools. The table shows the data as-is, without picking a “best”.
| Field | Dagu | Miller | uv |
|---|---|---|---|
| Platforms | Universal, MCP | Universal, MCP, Claude Code, Codex, Cursor | Universal |
| Local support | Yes | Yes | Yes |
| Paid API | No | No | No |
| License | GPL-3.0 | BSD-2-Clause | MIT OR Apache-2.0 |
| Stars | 4491 | 10051 | 91340 |
| Last activity | October 28, 2026 | October 21, 2026 | October 20, 2026 |
| Install complexity | سهل للبداية؛ يحتاج ضبط أمان قبل المشاركة / Easy to start; security configuration required before sharing | EASY | Easy on supported systems; Homebrew must already be installed for this command. Windows and standalone installation options are explained in the guide. |
| Category | Workflow, DevOps, Monitoring | Workflow, Data | Workflow, DevOps |
| Best use | Best for a developer or small team with connected scripts that need schedules, history, and a clear failure point. It adds dependencies, retries, and an operations UI without an external database in single-server mode. A simple independent command may not need it, and some team controls such as additional users, SSO, RBAC, and audit logging currently require a paid license. | Ideal for developers, data analysts, and operators who need to clean a CSV export, inspect JSON Lines, or summarize a large file from the terminal without starting a Python project. Use it for quick, repeatable file processing; concurrent transactions and advanced visual analysis still call for a database or complementary analytics tool. | 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. |