+----------------+ +----------------+ +----------------+
| User enters |------>| Next.js UI |------>| FastAPI |
| signup info | | (Browser) | | /api/research |
+----------------+ +----------------+ +-------+--------+
 |
 +-----------------------------------------------+
 |
 v
+----------------+ +----------------+ +----------------+
| Research |------>| Linkup Search |------>| Research |
| Agent | | API Tools | | Report |
+----------------+ +----------------+ +----------------+

def linkup_search(
 query: str,
 output_type: str = "sourcedAnswer",
 max_results: int = 5
) -> str:
 """
 Search the web using Linkup API for fresh, trusted information.

 Args:
 query: The search query string.
 output_type: "sourcedAnswer" for natural language answer with citations.
 max_results: Maximum number of results (default 5).

 Returns:
 JSON string with search results or sourced answer with citations.
 """
 response = linkup_client.search(
 query=query,
 depth="standard",
 output_type=output_type,
 max_results=max_results,
 )
 # Process and return results...

def linkup_fetch(url: str) -> str:
 """
 Fetch and extract content from a web page URL using Linkup API.
 Use this to get detailed content from specific pages.

 Args:
 url: The fully qualified URL to fetch.

 Returns:
 JSON string with the extracted markdown content from the page.
 """
 response = linkup_client.fetch(url=url)
 # Process and return content...

AGENT_INSTRUCTIONS_TEMPLATE = """You are a research analyst who helps sales teams understand new signups.

## YOUR CLIENT'S BUSINESS

You are researching on behalf of a business with the following description:
{business_description}

## RESEARCH PROCESS

1. Search for company info (1-2 searches)
2. Search for the person's role and LinkedIn (1-2 searches)
3. Write the final report DIRECTLY as your response

## REPORT FORMAT

# Research Report: [Person Name] at [Company]

## Executive Summary
2-3 sentence overview of who this person is and what their company does.

## Company Profile
- **Company**: Name and website
- **Industry**: What they do
- **Products/Services**: Key offerings
- **Size/Funding**: If known

## Person Profile
- **Name**: Full name
- **Role**: Current position
- **Background**: Professional experience
- **LinkedIn**: URL if found

## Recommendations for Outreach
- How the client's business (described above) could help them
- Specific use cases based on their industry
- Personalization hooks from research

## Sources
- List URLs consulted
"""

@app.post("/api/research")
async def research(request: ResearchRequest, node: NodeDep):
 agent_name = f"researcher_{secrets.token_hex(4)}"

 instructions = AGENT_INSTRUCTIONS_TEMPLATE.format(
 business_description=request.business_description
 )

 agent = await Agent.start(
 node=node,
 name=agent_name,
 instructions=instructions,
 model=Model("claude-sonnet-4-5"),
 tools=[
 Tool(linkup_search),
 Tool(linkup_fetch),
 FilesystemTools(visibility="conversation"),
 ],
 )

 message = f"""Research this new signup and write a report:

 - **Name**: {request.name}
 - **Email**: {request.email}
 - **Organization**: {request.organization}
 """

 async def stream_response():
 async for response in agent.send_stream(message, timeout=300):
 yield json.dumps(response.snippet, default=json_serializer) + "\n"

 return StreamingResponse(stream_response(), media_type="application/json")

AGENT_INSTRUCTIONS_TEMPLATE = """You are a research analyst focused on technical evaluation.

## RESEARCH PROCESS

1. Search for the company's tech stack and engineering blog
2. Search for the person's technical background and GitHub
3. Identify technical challenges they might face

## REPORT FORMAT

Focus on:
- Technical infrastructure and tools they use
- Engineering team size and structure
- Open source contributions
- Technical pain points your product solves
"""

def check_company_funding(company_name: str) -> str:
 """Check recent funding rounds for a company."""
 # Implement using Crunchbase API or similar
 pass

agent = await Agent.start(
 node=node,
 name=agent_name,
 instructions=instructions,
 model=Model("claude-sonnet-4-5"),
 tools=[
 Tool(linkup_search),
 Tool(linkup_fetch),
 Tool(check_company_funding), # Add new tool
 ],
)

# For faster responses
model=Model("claude-haiku-3-5")

# For more capable research
model=Model("claude-sonnet-4-5")

{
 "name": "Jane Doe",
 "email": "jane@example.com",
 "organization": "Example Corp",
 "business_description": "We provide cloud-based project management software."
}

{"messages": [...], "type": "conversation_snippet"}

import requests

def on_user_signup(user):
 response = requests.post(
 "https://your-zone.cluster.autonomy.computer/api/research",
 json={
 "name": user.name,
 "email": user.email,
 "organization": user.company,
 "business_description": "We provide cloud-based analytics software."
 },
 stream=True
 )

 # Collect the streamed response
 report = ""
 for line in response.iter_lines():
 if line:
 data = json.loads(line)
 for msg in data.get("messages", []):
 if msg.get("content", {}).get("text"):
 report += msg["content"]["text"]

 # Save to your CRM, send to Slack, etc.
 save_research_report(user.id, report)