Deep Research Agent

from openai import OpenAI

client = OpenAI(
    base_url="https://api.yachtsy.ai/v1",
    api_key="your-api-key"
)

# Comprehensive market comparison with streaming
response = client.chat.completions.create(
    model="yachtsy-deep-research-agent",
    messages=[{
        "role": "user",
        "content": "Comprehensive analysis of Catalina 34 vs Hunter 34 vs Beneteau 343. Include market positioning, resale value, and ownership costs."
    }],
    stream=True,
    max_tokens=4000
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

const analysisStream = await client.chat.completions.create({
    model: 'yachtsy-deep-research-agent',
    messages: [{
        role: 'user',
        content: 'Investment analysis for purchasing a 40ft cruising sailboat. Budget $200k-300k. Include depreciation, maintenance costs, and best value models.'
    }],
    stream: true,
    max_tokens: 4000
});

for await (const chunk of analysisStream) {
    if (chunk.choices[0]?.delta?.content) {
        process.stdout.write(chunk.choices[0].delta.content);
    }
}

curl -N -X POST https://api.yachtsy.ai/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "yachtsy-deep-research-agent",
    "messages": [{
      "role": "user",
      "content": "Deep technical analysis of Bavaria Cruiser 37 vs Jeanneau Sun Odyssey 379. Focus on build quality, performance, and long-term reliability."
    }],
    "stream": true,
    "max_tokens": 4000
  }'

import time

start_time = time.time()
response_text = ""

stream = client.chat.completions.create(
    model="yachtsy-deep-research-agent",
    messages=[{
        "role": "user",
        "content": "Research the sailboat market trends for 2024-2025"
    }],
    stream=True
)

print("Research starting...")
for chunk in stream:
    if chunk.choices[0].delta.content:
        token = chunk.choices[0].delta.content
        response_text += token
        print(token, end="", flush=True)

print(f"\n\nCompleted in {time.time() - start_time:.1f} seconds")
print(f"Total response: {len(response_text)} characters")

# Request specific research methodology
response = client.chat.completions.create(
    model="yachtsy-deep-research-agent",
    messages=[{
        "role": "system",
        "content": "Conduct thorough research using multiple data sources. Include confidence levels for each claim and cite specific sources where possible."
    }, {
        "role": "user",
        "content": "Research the reliability and common issues of Catalina Yachts from 1985-2000. Include owner feedback and surveyor reports."
    }],
    stream=True,
    temperature=0.1  # Low temperature for factual research
)

# Multi-model comparison with market context
response = client.chat.completions.create(
    model="yachtsy-deep-research-agent",
    messages=[{
        "role": "user",
        "content": """
        Comprehensive competitive analysis:
        
        Models to compare:
        - Catalina 34 (1985-2001)
        - Hunter 34 (1985-1995) 
        - Beneteau 343 (2005-2012)
        
        Analysis areas:
        - Build quality and construction
        - Sailing performance and handling
        - Interior layout and comfort
        - Market pricing and availability
        - Owner satisfaction and community
        - Resale value trends
        
        Provide quantitative data where available and confidence levels for each assessment.
        """
    }],
    stream=True,
    max_tokens=4000
)

## Executive Summary
The Catalina 34 represents excellent value in the 34-foot cruising market...

## Detailed Analysis
**Production Data**: 1,800+ units built (High confidence - Catalina records)
**Price Range**: $35,000-$65,000 (Medium confidence - YachtWorld analysis)
...

## Market Context
Mid-size cruisers (32-36ft) remain in high demand...

## Technical Deep Dive
| Specification | Catalina 34 | Hunter 34 | Beneteau 343 |
|---------------|-------------|-----------|--------------|
| LOA | 34'6" | 34'5" | 34'9" |
...

## Research Sources & Methodology
- Catalina Yachts production records (High confidence)
- SailboatData.com specifications (High confidence)  
- YachtWorld pricing analysis (Medium confidence)
...

## Recommendations
1. For budget-conscious buyers: Prioritize 1990+ models...
2. For performance: Consider wing keel option...

# Use Deep Research for analysis, then Listings for current inventory
research_response = client.chat.completions.create(
    model="yachtsy-deep-research-agent",
    messages=[{"role": "user", "content": "Research best 35ft cruising sailboats under $100k"}]
)

# Then search for specific models identified in research
listings_response = client.chat.completions.create(
    model="yachtsy-listings-agent",
    messages=[{"role": "user", "content": "Find Catalina 34 and Hunter 34 boats under $100k"}]
)

try:
    response = client.chat.completions.create(
        model="yachtsy-deep-research-agent",
        messages=[{"role": "user", "content": "research query"}],
        stream=True
    )
    
    for chunk in response:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="")
            
except Exception as e:
    if "timeout" in str(e).lower():
        print("Research query too complex. Try breaking it into smaller parts.")
    else:
        print(f"Research failed: {str(e)}")