ralph-loop

Automate iterative coding tasks with fresh context per iteration by using filesystem-based memory. This approach enforces a clean slate for each executor run, tracking progress in PRD.md and PROGRESS.md to avoid cross-task contamination.

Track AI agent progress during multi-step development workflows by using filesystem-backed state files. PRD.md and PROGRESS.md serve as persistent memory for planning and progress tracking, while git versioning commits every completed task for granular rollback.

Phase-driven orchestration for coding automation structures workflows into Phase 0 planning, Phase 1 coordination, and Phase 2 completion. This enforces clear state files and per-task verification to ensure reliable completion and an auditable history.

You can use git versioning to audit autonomous coding agent tasks through per-task commits. Every completed task is committed automatically, enabling granular rollback and traceability for each iteration within the development workflow.

You need clear state files like PRD.md and PROGRESS.md to start an autonomous coding loop. These files provide the persistent filesystem memory required for planning and progress tracking, ensuring each fresh context iteration executes reliably.

AI agents lose context during multi-step development workflows due to context pollution. Solving this requires enforcing fresh context per iteration and utilizing filesystem-based memory to maintain progress tracking without carrying over previous state data.