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AI Integration with LLMs.txt

Learn how to leverage AI coding assistants to integrate Purse API faster and more efficiently using our standardized LLMs.txt file.

What is LLMs.txt?​

We support LLMs.txt files for making the Purse documentation available to large language models and AI coding assistants. This allows AI tools to understand our API structure, integration patterns, and best practices when helping you build payment integrations.

Available Documentation File​

The following file is available at the root of our documentation:

  • /llms.txt: Curated documentation overview optimized for AI agents, including:
    • Getting started guides and authentication
    • API integration tutorials and best practices
    • Checkout integration modes comparison
    • API reference links
    • Key concepts and glossary
    • Payment partners directory
Optimized for AI Context

The LLMs.txt file is specifically structured to provide AI assistants with the most relevant information about Purse integration, following the standardized format from llmstxt.org.

Usage with AI Tools​

Cursor​

Use the @Docs feature in Cursor to include the Purse documentation in your project context.

  1. Open Cursor in your project
  2. Type @Docs in the chat
  3. Add the URL: https://docs.upstreampay.com/llms.txt
  4. Cursor will automatically fetch and index the documentation

Read more about Cursor @Docs

Windsurf​

Reference the LLMs.txt file using @web in your conversations or add it to your .windsurfrules file for persistent context.

In conversation:

@web https://docs.upstreampay.com/llms.txt

Read more about Windsurf @web

In rules:

# Purse Payment Integration Context
@docs https://docs.upstreampay.com/llms.txt

Read more about Windsurf memories & rules

GitHub Copilot​

While GitHub Copilot doesn't directly support LLMs.txt files, you can reference specific documentation URLs in comments to guide code generation:

// Reference: https://docs.upstreampay.com/docs/integrate/getting-started
// Implement Purse OAuth2 authentication

ChatGPT & Claude​

When using ChatGPT or Claude for integration help, you can:

  1. Reference the file directly:

    Please read https://docs.upstreampay.com/llms.txt and help me integrate Purse payment API

  2. Ask specific questions with context:

    Based on Purse documentation at https://docs.upstreampay.com/llms.txt,
    how do I implement a headless checkout with saved payment methods?
Best Practice

For the most accurate assistance, always reference the LLMs.txt file when starting a new conversation about Purse integration with AI assistants.

What's Included​

The LLMs.txt file provides AI assistants with:

🚀 Getting Started​

  • Prerequisites and API credentials setup
  • OAuth2 authentication flow
  • Token management best practices

🔧 API Integration​

  • Step-by-step payment tutorials
  • Payment operations (capture, refund, void)
  • Webhook integration and validation
  • Security and idempotency best practices

💳 Checkout Modes​

  • Headless SDK: Full control with API-driven customization
  • Hosted Fields: Secure embedded payment fields
  • Hosted Form: Simple pre-built payment form
  • Hosted Page: Fully hosted payment interface

📚 Reference Materials​

  • Complete API v2 OpenAPI specification
  • Payment concepts and glossary
  • Convention guide and SDK CDN URLs
  • 100+ payment partners directory

Example Use Cases​

Building Your First Payment Integration​

@Docs https://docs.upstreampay.com/llms.txt

Help me create a simple payment flow with Purse API:
1. Authenticate with OAuth2
2. Create a client session
3. Process a payment
4. Handle webhooks

Implementing Headless Checkout​

Using Purse documentation, implement a headless checkout with:
- Credit card payment
- Saved payment methods
- 3DS authentication
- Custom UI styling

Migrating from Hosted Page to Headless​

I'm currently using Purse Hosted Page. Help me migrate to Headless SDK
while maintaining the same payment flow and user experience.

Benefits of Using LLMs.txt​

✅ Faster Integration: AI assistants understand Purse patterns and best practices
✅ Accurate Code: Generated code follows our conventions and security guidelines
✅ Up-to-date: Always references the latest documentation structure
✅ Context-aware: AI understands relationships between different integration modes
✅ Error Prevention: AI knows common pitfalls and how to avoid them

Next Steps​

Feedback Welcome

If you have suggestions for improving our AI integration support, please contribute to the roadmap.