Kye Gomez
7 days ago
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# X402 Payment Integration with Swarms Agents
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X402 is a protocol that enables seamless cryptocurrency payments for API endpoints. This guide demonstrates how to monetize your Swarms agents by integrating X402 payment requirements into your FastAPI applications.
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With X402, you can:
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| Feature | Description |
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|-----------------------------------------------------|----------------------------------------------|
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| Charge per API request | Monetize your agents on a per-call basis |
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| Accept cryptocurrency payments | e.g., Base, Base Sepolia, and more |
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| Payment gate protection for agent endpoints | Secure endpoints with pay-to-access gates |
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| Create pay-per-use AI services | Offer AI agents as on-demand paid services |
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## Prerequisites
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Before you begin, ensure you have:
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- Python 3.10 or higher
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- A cryptocurrency wallet address (for receiving payments)
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- API keys for your AI model provider (e.g., OpenAI)
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- An Exa API key (if using web search functionality)
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## Installation
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Install the required dependencies:
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```bash
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pip install swarms x402 fastapi uvicorn python-dotenv swarms-tools
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```
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## Environment Setup
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Create a `.env` file in your project root:
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```bash
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# OpenAI API Key
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OPENAI_API_KEY=your_openai_api_key_here
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# Exa API Key (for web search)
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EXA_API_KEY=your_exa_api_key_here
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# Your wallet address (where you'll receive payments)
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WALLET_ADDRESS=0xYourWalletAddressHere
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```
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## Basic X402 Integration Example
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Here's a complete example of a research agent with X402 payment integration:
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```python
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from dotenv import load_dotenv
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from fastapi import FastAPI
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from swarms_tools import exa_search
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from swarms import Agent
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from x402.fastapi.middleware import require_payment
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# Load environment variables
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load_dotenv()
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app = FastAPI(title="Research Agent API")
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# Initialize the research agent
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research_agent = Agent(
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agent_name="Research-Agent",
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system_prompt="You are an expert research analyst. Conduct thorough research on the given topic and provide comprehensive, well-structured insights with citations.",
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model_name="gpt-4o-mini",
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max_loops=1,
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tools=[exa_search],
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)
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# Apply x402 payment middleware to the research endpoint
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app.middleware("http")(
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require_payment(
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path="/research",
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price="$0.01",
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pay_to_address="0xYourWalletAddressHere",
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network_id="base-sepolia",
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description="AI-powered research agent that conducts comprehensive research on any topic",
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input_schema={
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "Research topic or question",
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}
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},
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"required": ["query"],
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},
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output_schema={
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"type": "object",
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"properties": {
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"research": {
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"type": "string",
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"description": "Comprehensive research results",
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}
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},
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},
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)
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)
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@app.get("/research")
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async def conduct_research(query: str):
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"""
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Conduct research on a given topic using the research agent.
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Args:
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query: The research topic or question
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Returns:
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Research results from the agent
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"""
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result = research_agent.run(query)
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return {"research": result}
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@app.get("/")
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async def root():
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"""Health check endpoint (free, no payment required)"""
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return {
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"message": "Research Agent API with x402 payments",
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"endpoints": {
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"/research": "Paid endpoint - $0.01 per request",
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},
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}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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```
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## Running Your Service
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Start the server:
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```bash
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python research_agent_x402_example.py
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```
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Or with uvicorn directly:
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```bash
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uvicorn research_agent_x402_example:app --host 0.0.0.0 --port 8000 --reload
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```
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Your API will be available at:
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- Main endpoint: `http://localhost:8000/`
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- Research endpoint: `http://localhost:8000/research`
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- API docs: `http://localhost:8000/docs`
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## Next Steps
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1. Experiment with different pricing models
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2. Add multiple agents with specialized capabilities
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3. Implement analytics to track usage and revenue
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4. Deploy to production (see [Deployment Solutions](../deployment_solutions/overview.md))
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5. Integrate with your existing payment processing
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