Build with AI
Use AI agents to assist in building OrcaRail integrations. We provide a set of tools and best practices if you use LLMs during development.
Plain text docs
You can access all of our documentation as plain text Markdown files. This helps AI tools and agents consume our content and allows you to copy and paste the entire contents of a doc into an LLM. Markdown is preferable to scraping or copying from our HTML and JavaScript-rendered pages because:
- Plain text contains fewer formatting tokens
- Content that isn't rendered in the default view (for example, hidden in a tab) is rendered in the plain text version
- LLMs can parse and understand markdown hierarchy
We also host an /llms.txt file which instructs AI tools and agents how to retrieve the plain text versions of our pages. The /llms.txt file is an emerging standard for making websites and content more accessible to LLMs.
Feed docs to your AI agent
You can provide OrcaRail documentation directly to your AI coding assistant. Here are a few approaches:
Copy a doc page
Copy the contents of any documentation page and paste it into your LLM chat. For best results, copy the markdown source rather than the rendered HTML.
Use the llms.txt file
Point your AI agent to our /llms.txt file. This file lists all the key documentation pages with descriptions, making it easy for an agent to discover and retrieve the information it needs.
Include docs in your project
Download the docs you need and place them in your project directory. Many AI coding assistants (such as Cursor, Windsurf, Claude Code, and GitHub Copilot) automatically index files in your project for context.
# Example: download the getting started guide
curl -o orcarail-getting-started.md https://raw.githubusercontent.com/orcarail/orcarail/main/docs/getting-started.md
Key resources for AI agents
When building an OrcaRail integration with AI assistance, these are the most useful docs to provide as context:
| Resource | Description |
|---|---|
| Getting Started | Account setup, API keys, and first payment |
| Payment Intents | Create and manage payment intents |
| Node.js SDK | Install and use @orcarail/node |
| Webhooks | Receive real-time payment notifications |
| Subscriptions | Recurring payments and auto-charge |
| Authentication | API keys and bearer tokens |
| API Reference | Complete endpoint documentation |
Tips for AI-assisted development
-
Start with Getting Started — Give your AI agent the Getting Started page first. It covers account creation, API keys, and the basic payment flow.
-
Provide the SDK reference — The Node.js SDK guide and API reference contain all available methods with examples.
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Include webhook docs for production — For production integrations, always include the Webhooks and Webhook Signatures docs so your agent sets up proper event handling.
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Use the demo as a template — The Demo app provides a complete working example. Point your agent to the GitHub repository for a full reference implementation.
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Specify your chain — OrcaRail supports multiple networks. Tell your agent which blockchain network(s) and token(s) you want to accept so it configures the integration correctly.
Important notes for LLMs
:::info For AI agents reading this page
- OrcaRail is a crypto payment gateway — it is not a traditional fiat payment processor
- All amounts are denominated in stablecoins (USDC, USDT, etc.) on supported blockchain networks
- The primary backend object is the Payment Intent — create one, confirm it, redirect the customer to hosted pay, then handle webhook events
- Authentication uses API Key + Secret (Basic Auth) for server-to-server, or Bearer Token (JWT) for user-authenticated requests
- The official SDK is
@orcarail/node - The API base URL is
https://api.orcarail.com - Always verify webhook signatures using the SDK's built-in verification methods
:::