Build with Rye Using AI: LLM Quickstart
Arjun Bhargava
Co-founder and CEO @ Rye
Feb 25, 2026
3 minutes read
Integrate Rye's Universal Checkout API using AI coding assistants. Copy-paste prompts, agent config files, and MCP support for Claude Code, Cursor, Windsurf, and more.
TL;DR / Key Takeaways
The LLM Quickstart lets developers integrate Rye's Universal Checkout API using their AI coding assistant of choice.
Copy-paste prompts get you from zero to a working checkout integration — no manual API doc reading required.
Drop a single config file into your repo and your AI assistant becomes a dedicated Rye integration agent that analyzes your codebase, proposes a plan, implements, and verifies.
MCP support lets tools like Claude Code and Cursor search Rye's docs on demand as you build.
Developers building with AI coding assistants shouldn't have to context-switch between their IDE and API documentation. The integration workflow should be: describe what you want, let the AI figure out how to build it.
That's exactly how the Rye LLM Quickstart works.
What We Built
The LLM Quickstart gives developers three ways to integrate Rye using AI — each designed for a different stage of the build process.
Copy-paste prompts. Four ready-made prompts cover the most common starting points: planning an integration approach, starting a build, generating a proof of concept, and going to production. Each prompt points the AI to our API context file, which contains the full Rye API reference, SDK docs, code examples, and known constraints. Paste a prompt into any AI coding tool — Claude Code, Cursor, Windsurf, ChatGPT — and the AI has everything it needs to start building.
Agent config files. Drop a single file into your repo root — CLAUDE.md for Claude Code, .cursor/rules/rye.mdc for Cursor, .windsurf/rules/rye.md for Windsurf, or .github/copilot-instructions.md for GitHub Copilot — and every AI session automatically follows a structured agentic checkout integration workflow. The agent analyzes your codebase, identifies your stack, proposes an integration plan, waits for your approval, implements step by step, and verifies the full checkout lifecycle. No additional prompting needed.
MCP server. For tools that support Model Context Protocol, connect Rye's docs directly to your IDE so the AI can search our documentation on demand. One command in Claude Code:
The AI doesn't need the full docs upfront — it pulls what it needs as you build.
Why This Matters
Rye's Universal Checkout API lets you purchase products from any merchant URL — the core infrastructure layer for agentic commerce. It's a checkout API for AI agents, but the typical integration still involves understanding the checkout intent lifecycle, choosing between multi-step and single-step flows, handling async polling, and managing error states. That's a lot of surface area to absorb before writing your first line of code.
The LLM Quickstart collapses that learning curve. Your AI assistant reads the docs, understands the constraints (US shipping only, physical products, one product per intent, 45-minute expiry), and writes an integration tailored to your specific codebase and stack — whether that's Next.js, Python/Flask, Ruby on Rails, or raw HTTP.
This is especially relevant for teams building AI shopping agents, embedded commerce experiences, and agentic purchasing workflows. Whether you're figuring out how to build an AI shopping agent from scratch or adding AI agent payment integration to an existing app, Rye's API is how your agent completes the purchase — and now your AI coding assistant can wire that up in a single session. Pair it with the Product Data API to verify availability and pricing before checkout, or check out the Chat Storefront tutorial for a full end-to-end example.
Frequently Asked Questions
How do I integrate a checkout API using an AI coding assistant?
The LLM Quickstart provides copy-paste prompts that give your AI coding assistant — Claude Code, Cursor, Windsurf, ChatGPT, or GitHub Copilot — full context on Rye's Universal Checkout API. Paste a prompt, and the AI will analyze your codebase, propose an integration plan, and implement it step by step. You can also drop an agent config file into your repo root so every AI session automatically follows the integration workflow.
What is the fastest way to add purchasing to an AI agent?
With Rye's LLM Quickstart, you can go from zero to a working checkout integration in a single AI coding session. The prompts link to an API context file that gives your AI assistant everything it needs — API reference, SDK docs, code examples, and constraints. Your agent sends a product URL to Rye's API, and Rye handles offer resolution, payment, shipping, and order confirmation. No merchant integrations required.
Does Rye support MCP for AI-powered IDEs?
Yes. Rye provides an MCP server that connects directly to tools like Claude Code and Cursor. Instead of loading the full docs upfront, your AI assistant can search Rye's documentation on demand as you build. Setup is one command: claude mcp add --transport http rye-docs https://docs.rye.com/mcp.
Start Building
The fastest way to get started: copy a prompt into your AI coding tool. For ongoing projects, add the agent config file for your IDE. For the deepest context, connect the MCP server so your AI can search our docs live.
Get your staging API key from the Rye Console and start building in minutes.
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