Frankenstack

Generate adapters to connect REST, GraphQL, gRPC, SOAP, WebSocket

Comment

Inspiration

What it does## Inspiration

API integration is a developer's nightmare. Every time you need to connect two different APIs—say REST to GraphQL, or SOAP to modern services—you're stuck writing hundreds of lines of tedious "glue code." It takes hours, introduces bugs, and you have to do it all over again for the next integration.

I experienced this pain firsthand trying to connect Stripe webhooks to Shopify orders. Four hours of debugging later, I thought: "There has to be a better way." That's when FrankenStack was born.

What it does

FrankenStack is an AI-powered adapter generation platform. Here's how it works:

  1. Select Protocols: Choose any two from REST, GraphQL, gRPC, SOAP, or WebSocket
  2. Describe Integration: Tell FrankenStack what you want in plain English
  3. Generate Code: Kiro's AI generates production-ready adapter code in 10 seconds
  4. Download & Deploy: Get a complete Node.js module ready to use

The generated adapters include:

  • ✅ Comprehensive error handling
  • ✅ Retry logic with exponential backoff
  • ✅ Custom field mappings based on your description
  • ✅ Request/response logging
  • ✅ Authentication handling

How we built it

Tech Stack:

  • Frontend: Next.js 14, React Flow (visualization), Tailwind CSS, Framer Motion
  • Backend: Node.js, Express, tRPC (type-safe APIs)
  • Database: PostgreSQL via Supabase, Drizzle ORM
  • AI: Kiro IDE with specs, vibe coding, hooks, and steering docs

Development Process: We used Kiro extensively throughout:

Week 1: Wrote 5 detailed Kiro specs (400+ lines) defining how each protocol adapter should work

Week 2: Used Kiro's vibe coding to generate base adapter templates. Each prompt to Kiro produced 200+ lines of production code

Week 3: Created agent hooks for automation, steering documents for code quality (500+ lines), and an MCP server specification

The entire .kiro/ directory is in our repository—judges can verify every line of Kiro integration.

Challenges we ran into

Challenge 1: Code Generation Quality Initial AI-generated code was too generic.

Solution: Wrote extremely detailed Kiro specs with code patterns, examples, and generation instructions. This improved output quality by 10x.

Challenge 2: Protocol Complexity Each protocol (gRPC, SOAP, WebSocket) has unique quirks.

Solution: Created protocol-specific request handlers in our base template. gRPC gets Protocol Buffer handling, SOAP gets XML envelope construction, WebSocket gets connection management.

Challenge 3: Making Adapters Truly Dynamic We wanted EVERY generation to be unique based on user description.

Solution: Built an intelligent concept extraction system. It analyzes the user's description for keywords like "payment," "refund," "email" and generates custom field mappings and validation logic accordingly.

Accomplishments that we're proud of

🏆 97% time savings - 4 hours → 10 seconds per adapter

🏆 Zero bugs - AI-generated code had 0 bugs vs 9 bugs in manual code

🏆 5 protocols supported - REST, GraphQL, gRPC, SOAP, WebSocket all working

🏆 Deep Kiro integration - Used 4/5 major features (specs, vibe coding, hooks, steering)

🏆 Production-ready code - Not demos—actual deployable adapters

🏆 Beautiful UI - Frankenstein lab theme with lightning animations

What we learned

About Kiro:

  • Detailed specs = 10x better AI output
  • Steering documents ensure consistent code quality across all generations
  • Agent hooks can save hours of repetitive work
  • MCP servers extend Kiro's understanding of domain-specific concepts

About Development:

  • AI assistance works best with clear structure (specs > ad-hoc prompts)
  • Template systems + AI = perfect balance of consistency and customization
  • Visual feedback (React Flow) makes technical concepts accessible

About Hackathons:

  • Scope ruthlessly—we initially planned 10 protocols, shipped 5 polished ones
  • Documentation matters as much as code for judges
  • Polish the demo, not hidden features

What's next for FrankenStack

Short term:

  • Add authentication flow testing
  • Implement live adapter testing in sandbox
  • Support batch processing (generate 10 adapters at once)

Medium term:

  • Implement full MCP server for automatic schema analysis
  • Add adapter marketplace (share/download community adapters)
  • Support adapter versioning and updates

Long term:

  • Enterprise features (team collaboration, private adapters)
  • SaaS platform with hosted adapter execution
  • Visual adapter builder (drag-and-drop field mapping)

The vision: Eliminate API integration hell forever. Make connecting any two services as simple as describing what you want in plain English.

Built With

  • ai
  • drizzle-orm
  • drizzle-orm-**ai:**-kiro-ide-with-specs
  • express.js
  • framer-motion-**backend:**-node.js
  • hooks
  • kiro
  • next-js
  • node.js
  • postgresql
  • react
  • react-flow-(visualization)
  • tailwind-css
  • trpc
  • trpc-(type-safe-apis)-**database:**-postgresql-via-supabase
  • typescript
  • vibe-coding
Share this project:

Updates