Summary

This repository provides a Cursor CLI implementation of the Ralph Wiggum autonomous iteration technique. It addresses the challenge of LLM context pollution and memory limitations by externalizing state to files and Git, enabling agents to operate with deliberate context management. This allows for more robust and reproducible AI-driven development workflows, particularly for complex tasks.

How It Works

The core approach treats LLM context like volatile memory, contrasting with traditional programming's malloc/free. Instead of relying on the LLM's limited context window, Ralph persists progress, tool calls, and lessons learned in files (.ralph/) and Git commits. When the context approaches its limit (e.g., 80k tokens), the agent is reset with a fresh context, picking up precisely where it left off via Git history. This prevents "context pollution" and the "gutter" effect where models get stuck referencing outdated or incorrect information.

Quick Start & Requirements

• Primary install / run command:

  curl -fsSL https://raw.githubusercontent.com/agrimsingh/ralph-wiggum-cursor/main/install.sh | bash
  

  Then run:

  ./.cursor/ralph-scripts/ralph-setup.sh
  

• Non-default prerequisites and dependencies:
  • A Git repository (git init).
  • cursor-agent CLI (installable via curl https://cursor.com/install -fsS | bash).
  • Optional: gum for an enhanced interactive UI (installer offers to install, or brew install gum).
• Links:
  • Cursor CLI install: https://cursor.com/install
  • Original Ralph technique: https://github.com/geoffreyhuntley/ralph (implied by README)
  • gum installation: https://github.com/charmbracelet/gum (implied by README)

Highlighted Details

• Interactive setup with an optional gum-based UI for model selection and configuration.
• Accurate token tracking and real-time monitoring of context health via activity.log.
• Gutter detection mechanism to identify and signal when an agent is stuck in a loop (repeated failures, file thrashing).
• Learning from failures: agents update .ralph/guardrails.md with "Signs" to prevent repeated mistakes in future iterations.
• State persistence in Git, ensuring continuity and enabling agents to resume from previous commits.
• Optional workflow for creating new branches and opening Pull Requests upon task completion.

Maintenance & Community

The project is authored by Agrim Singh, implementing Geoffrey Huntley's original Ralph technique. No specific community channels (like Discord/Slack) or detailed contributor information beyond the author are provided in the README.

Licensing & Compatibility

• License type: MIT License.
• Compatibility notes: The MIT license generally permits commercial use and integration with closed-source projects without significant restrictions.

Limitations & Caveats

The Ralph Wiggum technique is described as "deterministically bad in an undeterministic world," suggesting inherent limitations or predictable failure modes. The system relies on the cursor-agent CLI, and its effectiveness may depend on the clarity and testability of the task defined in RALPH_TASK.md. The "gutter detection" and "learning from failures" mechanisms require careful monitoring and potential manual intervention or refinement of guardrails.