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Run iterative development with worker execution and reviewer approval across models.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ralph-loop-mcp" yet — see the docs or source repo.
Use the Ralph Loop approach to fix this null pointer bug in a Python API: let the worker model identify the issue and propose a fix, then have the reviewer model check edge cases, regressions, and code style, repeating until approval. Code: <paste code>
A reviewer-approved fix with revised code, review notes, and risk summary.
Use Ralph Loop to implement a login rate-limiting middleware in Node.js: the worker model should write the code and tests first, and the reviewer model should assess security, performance, and maintainability, iterating until approval.
Complete feature code, test cases, and a final approved version after iterative reviews.
Review and improve this Terraform configuration using Ralph Loop: the worker model should propose fixes and optimizations first, and the reviewer model should examine deployment risks, best practices, and reproducibility, iterating until approved. Config: <paste config>
An optimized Terraform revision with cross-model review results and final approval notes.
Detect retry loops and iteration patterns to improve debugging and repair attempts.
Orchestrate parallel code generation and review with resilient LLM workflow automation.
Runs dual-model code reviews and synthesizes one unified report.
Enable AI agents to deliver real-time haptic feedback through connected devices.
Orchestrate RFC-based multi-agent workflows with quality gates and merge queues.
Run deterministic agent orchestration with task decomposition, subagents, and review feedback.