LLMs Context

AgentRef publishes machine-readable documentation endpoints that follow the llms.txt standard. These endpoints let you feed the full AgentRef documentation into any AI assistant as context — enabling more accurate answers, code generation, and integration help.

Endpoints

URL	Description	Best For
`https://www.agentref.co/docs/llms.txt`	Concise overview of all documentation pages with links	Quick orientation, smaller context windows
`https://www.agentref.co/docs/llms-full.txt`	Complete documentation content in plain Markdown	Full context, comprehensive answers

What is llms.txt?

The llms.txt standard provides a way for websites to publish their content in a format optimized for large language models. Instead of making an LLM crawl HTML pages, you give it a single plain-text file containing everything it needs.

llms.txt — A structured index of all documentation pages with titles, descriptions, and URLs. Think of it as a table of contents for AI.
llms-full.txt — The complete documentation rendered as plain Markdown in a single file. Every page, every code example, every table — all in one response.

Using with AI Assistants

Claude

Paste the URL directly in a conversation:

Read https://www.agentref.co/docs/llms-full.txt and then help me set up
an affiliate program with 20% recurring commission.

Or use Claude Projects to add it as a knowledge source:

Create a new Claude Project
Add https://www.agentref.co/docs/llms-full.txt as a knowledge file
All conversations in the project will have full AgentRef context

ChatGPT

Use the URL in a prompt or with Browse mode:

Fetch https://www.agentref.co/docs/llms-full.txt and use it as context.
I need to integrate AgentRef webhooks into my Express.js app.

Cursor

Add the docs to your project context in .cursorrules or paste the URL:

@https://www.agentref.co/docs/llms-full.txt

Help me create a conversion tracking integration using the AgentRef Node SDK.

Other LLM Tools

Any tool that accepts URL context or plain text can use these endpoints. The content is plain Markdown with no special formatting requirements.

Tips for Optimal Context Usage

Choose the right endpoint

Use llms.txt when you only need the AI to know what documentation exists and where to find it. This is lighter on tokens and works well for navigation questions.
Use llms-full.txt when you need the AI to have deep knowledge of AgentRef’s APIs, SDKs, and features. This gives the best answers but uses more context window.

Scope your questions

Even with full documentation context, you’ll get better results by being specific:

# Good
"Using the AgentRef Node SDK, show me how to list pending payouts
and create a payout for each affiliate that meets the threshold."

# Less good
"Help me with AgentRef."

Combine with code context

For integration tasks, give the AI both the AgentRef docs and your own code:

I'm building a Next.js app. Here's my current webhook handler:
[your code]

Read https://www.agentref.co/docs/llms-full.txt and help me verify
the webhook signature correctly.

Use llms.txt for discovery

When you are not sure which part of the docs you need, start with llms.txt:

Read https://www.agentref.co/docs/llms.txt and tell me which pages
are relevant for setting up fraud detection.

Then follow up with the specific pages or switch to llms-full.txt for the complete context.

Programmatic Access

You can fetch these endpoints from your code for automated workflows:

// Fetch the full docs as context for an LLM call
const response = await fetch('https://www.agentref.co/docs/llms-full.txt');
const docsContext = await response.text();

// Use as system prompt context
const messages = [
  {
    role: 'system',
    content: `You are an AgentRef integration assistant. Here is the full documentation:\n\n${docsContext}`
  },
  {
    role: 'user',
    content: 'How do I set up webhook signature verification?'
  }
];

import httpx

# Fetch the full docs as context for an LLM call
response = httpx.get("https://www.agentref.co/docs/llms-full.txt")
docs_context = response.text

# Use as system prompt context
messages = [
    {
        "role": "system",
        "content": f"You are an AgentRef integration assistant. Here is the full documentation:\n\n{docs_context}",
    },
    {
        "role": "user",
        "content": "How do I set up webhook signature verification?",
    },
]

# Download for offline use
curl -o agentref-docs.md https://www.agentref.co/docs/llms-full.txt

# Quick preview
curl -s https://www.agentref.co/docs/llms.txt | head -50

Content Updates

The llms.txt endpoints reflect the latest published documentation. When docs are updated, the endpoints serve the new content immediately — no caching delay.

Bookmark https://www.agentref.co/docs/llms-full.txt in your AI tools. Whenever you’re building an AgentRef integration, load it as context for the most accurate, up-to-date assistance.

Getting Started

Merchant Guide

Agent Integration

Tracking

Integration Guides

Webhooks

For Affiliates

Resources

Endpoints

What is llms.txt?

Using with AI Assistants

Claude

ChatGPT

Cursor

Other LLM Tools

Tips for Optimal Context Usage

Choose the right endpoint

Scope your questions

Combine with code context

Use llms.txt for discovery

Programmatic Access

Content Updates

​Endpoints

​What is llms.txt?

​Using with AI Assistants

​Claude

​ChatGPT

​Cursor

​Other LLM Tools

​Tips for Optimal Context Usage

​Choose the right endpoint

​Scope your questions

​Combine with code context

​Use llms.txt for discovery

​Programmatic Access

​Content Updates

Endpoints

What is llms.txt?

Using with AI Assistants

Claude

ChatGPT

Cursor

Other LLM Tools

Tips for Optimal Context Usage

Choose the right endpoint

Scope your questions

Combine with code context

Use llms.txt for discovery

Programmatic Access

Content Updates