Getting out of Model Lock-in - My TS Ralph Implementation for Cursor Chat

Breaking Free from Model Lock-in: My TypeScript Ralph Implementation for Cursor Chat

As developers, we’ve all experienced the frustration of vendor lock-in. You discover a fantastic tool like Ralph loop: an AI assistant for code review and development tasks, only to realize it’s permanently tied to a single model provider. This limitation sparked my journey to build a flexible, TypeScript-based alternative that works with any AI model, specifically designed to integrate seamlessly with Cursor’s chat interface.

What is Ralph loop

Ralph is an autonomous AI agent loop that runs AI coding tools (Amp or Claude Code) repeatedly until all PRD items are complete. Each iteration is a fresh instance with clean context. Memory persists via git history, progress.txt, and prd.json.

It was first brought up by Geoffrey Huntley.

The goal of the Ralph loop is to let you build in an entirely different way where you are orchestrating agents in a linear sequence to do a lot of different pieces of related work as part of a PRD doc.

Workflow

  1. Start: Reference @scripts/ralph/ralph.ts in Cursor chat or run pnpm ralph:single
  2. Review: The script shows the next story to implement and the instructions
  3. Execute: Follow the instructions in prompt.md to implement the story
  4. Update: Mark the story as complete in prd.json by setting passes: true
  5. Continue: Run the script again to move to the next story

Key files

  • prd.json - Product Requirements Document with user stories
  • prompt.md - Instructions for the AI agent
  • progress.txt - Progress log (auto-created)
  • .last-branch - Tracks the current branch
  • ralph.ts - Main script (TypeScript version for Cursor chat)
  • ralph.sh - Original bash script (uses amp/claude CLI) from snarktank/ralph

The Problem with Model Lock-in

Ralph loop is an excellent AI assistant framework for developers, but its dependence on a specific model provider creates several challenges:

  • Limited flexibility: You’re constrained by one model’s capabilities, pricing, and availability
  • Vendor dependency: API changes, pricing adjustments, or service disruptions directly impact your workflow
  • No experimentation: Can’t compare different models for specific tasks or optimize for cost and performance
  • Integration barriers: Difficult to embed into existing tools like Cursor’s chat interface

The last point was particularly frustrating to me. Cursor has become central to my development workflow, and I wanted Ralph loops capabilities available within that environment without switching contexts.

Why TypeScript Was the Right Choice

TypeScript offered several advantages for building a flexible AI assistant framework (and it’s my comfort zone):

Strong Type Safety

Working with AI APIs involves complex, nested data structures. TypeScript’s compile-time type checking catches errors before runtime.

Superior Tooling Support

TypeScript’s deep integration with VS Code (the foundation of Cursor) provides exceptional IntelliSense, refactoring tools, and error detection (exactly what you need when working with AI APIs that frequently change).

Vibrant Ecosystem

Access to npm’s extensive library collection meant I could leverage existing solutions for HTTP requests, configuration management, and logging rather than building everything from scratch (or rather than using bash scripts, which I’m not really fond of, dispite acknowledging its advantages).

Cursor Integration Strategy

While Cursor doesn’t have a formal plugin system (at least at the time of writing this), you can integrate Ralph loop through several approaches:

Method 1: Reference the Script Directly (the one I use)

In Cursor chat, simply reference the script:

@scripts/ralph/ralph.ts

This will load the script and execute one iteration, showing you:

  • The current status (completed/total stories)
  • The next story to work on
  • The full prompt instructions
  • Current progress log

Then you (or the AI assistant) can execute the instructions to implement the story.

Method 2: Run via Terminal

Run a single iteration:

pnpm ralph:single

Or run with a max iteration limit:

pnpm ralph --max-iterations 5

Key Benefits Achieved

After just a few hours of using this implementation, the advantages have been significant:

Cost Optimization

By routing simple tasks to more affordable models (like Compose on Cursor chat) and complex problems to premium models (like GPT-4 Turbo or Claude Sonnet 4.5), I’ve reduced my AI API costs without sacrificing quality.

Risk Mitigation

No single provider’s outage or pricing change can disrupt my workflow. The abstraction layer allows seamless failover between providers.

Enhanced Developer Experience

Having Ralph loop’s capabilities integrated into my Cursor workflow eliminates context switching. I can request code reviews, generate documentation, or debug issues without leaving my editor.

Conclusion

Building a TypeScript-based, model-agnostic version of Ralph loop has been one of the most impactful projects in my development toolkit. It transformed a rigid, single-provider tool into a flexible assistant that leverages the best of multiple AI models while integrating seamlessly with my primary development environment.

The key insight is that vendor lock-in isn’t inevitable. With thoughtful abstraction and TypeScript’s powerful type system, you can create AI-powered tools that maintain consistency while offering the flexibility to choose the right model for each task.

This approach has not only saved me significant costs but also improved the quality of AI assistance I receive. Different models excel at different tasks, and having them all available through a unified interface is remarkably powerful.

If you’re frustrated by AI tool limitations or vendor lock-in, I encourage you to experiment with building your own abstractions. The initial investment pays substantial dividends in flexibility, cost savings, and deeper understanding of how these systems work.

I found myself reviewing more code than actually writing them (which can also be fun), and testing more the code that AI wrote, because we all know it can generate some really bad code.

We’re jumping into a whole new world, and I get why everyone’s pumped. But we should learn the lessons, not just chase the trendy terms and tools.

Remember: the best tools aren’t just powerful: they’re adaptable. By building flexibility into your AI tooling from the start, you ensure they remain valuable as the AI landscape continues to evolve at breakneck speed.

Check out my ralph-ts repository to see this project in details :)