from dotenv import load_dotenv
from fastapi import FastAPI
from swarms_tools import exa_search
from swarms import Agent
from x402.fastapi.middleware import require_payment
# Load environment variables
load_dotenv()
app = FastAPI(title="Research Agent API")
# Initialize the research agent
research_agent = Agent(
agent_name="Research-Agent",
system_prompt="You are an expert research analyst. Conduct thorough research on the given topic and provide comprehensive, well-structured insights with citations.",
model_name="gpt-5.4",
max_loops=1,
tools=[exa_search],
)
# Apply x402 payment middleware to the research endpoint
app.middleware("http")(
require_payment(
path="/research",
price="$0.01",
pay_to_address="0xYourWalletAddressHere",
network_id="base-sepolia",
description="AI-powered research agent that conducts comprehensive research on any topic",
input_schema={
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Research topic or question",
}
},
"required": ["query"],
},
output_schema={
"type": "object",
"properties": {
"research": {
"type": "string",
"description": "Comprehensive research results",
}
},
},
)
)
@app.get("/research")
async def conduct_research(query: str):
"""
Conduct research on a given topic using the research agent.
Args:
query: The research topic or question
Returns:
Research results from the agent
"""
result = research_agent.run(query)
return {"research": result}
@app.get("/")
async def root():
"""Health check endpoint (free, no payment required)"""
return {
"message": "Research Agent API with x402 payments",
"endpoints": {
"/research": "Paid endpoint - $0.01 per request",
},
}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
