# PolyPay for AI Agents

PolyPay ships an [llms.txt](https://llmstxt.org) playbook that lets any AI agent — Claude, ChatGPT, Cursor, autonomous trading bots, custom LangChain workflows — read, understand, and operate PolyPay through plain HTTP without you having to write a custom integration.

## What is llms.txt?

[`llms.txt`](https://llmstxt.org) is a simple convention: a single markdown file at a well-known URL that tells LLMs how to interact with a service. Think of it as a robots.txt, but for AI agents instead of crawlers. The agent fetches the file, reads the instructions, and can then call the right endpoints with the right payloads on its own.

## Live endpoint

```
https://api.polypay.pro/llms.txt
```

Plain text, CORS-enabled, cached for 1 hour. No authentication needed to read.

## What's inside?

A complete playbook covering the five most common agent flows:

| # | Flow                            | What the agent can do                                           |
| - | ------------------------------- | --------------------------------------------------------------- |
| 1 | **Login**                       | Generate a ZK auth proof and obtain a JWT                       |
| 2 | **Create multisig account**     | Deploy a new PolyPay multisig on Horizen or Base                |
| 3 | **Single transfer**             | Propose, vote, and execute a private payroll payment            |
| 4 | **Batch transfer**              | Propose and execute multi-recipient payroll in one transaction  |
| 5 | **Gasless USDC deposit (x402)** | Fund any PolyPay multisig with USDC on Base without holding ETH |

Each flow includes the exact HTTP requests, payload shapes, ZK proof generation snippets (Noir + UltraHonk), and links to the compiled circuit artifacts the agent needs.

The full live OpenAPI / Swagger spec sits next to it at `https://api.polypay.pro/swagger` for endpoints not covered in the playbook.

## How to point an agent at PolyPay

Most agent frameworks support custom system prompts or tools. Two common ways to wire up PolyPay:

**1. Drop the URL into your prompt.**

> Fetch `https://api.polypay.pro/llms.txt` and use it to send 100 USDC from my PolyPay multisig `0x...` to the addresses in `recipients.csv`. Sign all proofs with the wallet I'm connected to.

The agent will read the playbook and follow the steps end-to-end.

**2. Add it as a tool / resource.**

If you're building with Claude Code, OpenAI Custom GPTs, Cursor, or any MCP-compatible agent, register `https://api.polypay.pro/llms.txt` as a documentation source. The agent gets persistent context for every session.

## What can agents actually do?

A few non-trivial examples that work out of the box:

* *"Run this week's payroll on PolyPay. Recipients and amounts are in the attached CSV. Use my Base mainnet multisig."*
* *"Top up the team's PolyPay treasury with 5,000 USDC from my Base wallet, gasless."*
* *"Show me the last 30 days of payroll history for our PolyPay multisig, grouped by recipient."*
* *"Propose a transfer of 1 ETH on Horizen to the address from the Telegram message, wait for the other signers, then execute when the threshold is met."*

## Compatibility

Tested against agents using:

* Anthropic Claude (Claude Code, claude.ai with browsing)
* OpenAI ChatGPT (with browsing or Custom GPT)
* Cursor / Copilot agents that follow `llms.txt`
* LangChain / LangGraph custom agents
* MCP servers exposing the URL as a resource

If your framework can fetch a URL and follow markdown instructions, it can drive PolyPay.

## For developers

The playbook is generated server-side from [`packages/backend/src/llms-txt/llms-txt.content.ts`](https://github.com/Poly-pay/polypay_app/blob/develop/packages/backend/src/llms-txt/llms-txt.content.ts). Updates to the content go through the normal PR flow — the live endpoint reflects the latest deployed commit on `main`.

Open an issue on [GitHub](https://github.com/Poly-pay/polypay_app/issues) if you spot an out-of-date example, a missing flow, or an agent framework that can't parse the playbook cleanly.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://q3labs.gitbook.io/polypay-docs/llms-txt-for-agents.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.