ralph-tui is a terminal-first task runner that orchestrates AI agent work as a structured queue of tasks, making “agentic execution” feel more like operating a build tool than chatting in a textbox. It prioritizes a disciplined loop: select the next task, construct the right prompt and context, execute the agent, detect completion, and move forward until the workload is done. By centering everything in a TUI, it encourages repeatable workflows where you can watch progress, adjust priorities, and keep your attention on the execution pipeline rather than juggling windows and tabs. The project is designed for real engineering workflows where tasks are interdependent and completion criteria matter, so it emphasizes explicit state, iteration, and predictable handoffs between steps. It also supports the idea of “skills” or reusable behaviors that can be applied to tasks, helping teams standardize how their agent approaches planning, implementation, and review.
Features
- Terminal UI for managing and running agent-driven task queues
- Priority-based task selection and iterative execution loop
- Clear completion detection and task lifecycle handling
- Skill-style reusable behaviors to standardize agent performance
- Workflow-friendly structure suited for multi-step engineering tasks
- Designed for repeatability and visibility in agent operations