Use llms.txt and llms-full.txt to give AI assistants complete context about the AgentRef documentation. Feed the entire docs into Claude, ChatGPT, Cursor, or any LLM-powered tool.
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.
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.
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.
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 payoutsand create a payout for each affiliate that meets the threshold."# Less good"Help me with AgentRef."
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 verifythe webhook signature correctly.
You can fetch these endpoints from your code for automated workflows:
// Fetch the full docs as context for an LLM callconst response = await fetch('https://www.agentref.co/docs/llms-full.txt');const docsContext = await response.text();// Use as system prompt contextconst 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 callresponse = httpx.get("https://www.agentref.co/docs/llms-full.txt")docs_context = response.text# Use as system prompt contextmessages = [ { "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 usecurl -o agentref-docs.md https://www.agentref.co/docs/llms-full.txt# Quick previewcurl -s https://www.agentref.co/docs/llms.txt | head -50
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.
