ralph-loop

Automating iterative code refinement loops involves routing your request to a loop engine that repeatedly executes tasks like linting or testing until no errors remain. You simply describe the refinement task and specify the completion criteria in conversation context.

You can trigger a loop by describing the task you want repeated and the criterion for completion in conversation. The engine identifies explicit loop prompts, such as running until no errors are detected, and leverages conversation context to extract parameters automatically.

Yes, the iterative loop engine supports development tasks including testing, linting, and data processing. It applies to conversations where you ask to keep trying until a condition is met, parsing diverse verbal loop cues to automate these workflows until success criteria are satisfied.

The iterative loop engine handles diverse verbal loop cues without requiring rigid syntax. You can phrase requests naturally, such as asking it to keep trying until completion or until no errors are found, and the engine will extract the task and criteria from the conversation context.

You should avoid automated AI task loops for tasks that lack a clear completion criterion or require human judgment between iterations. The engine relies on extracting a defined success condition, so open-ended tasks without specific stopping criteria are not suitable for this loop approach.