Building Agents with Exa Web Search

{
    "tools_enabled": ["auto_search"]
}

payload = {
    "agent_config": {
        "agent_name": "Market Research Analyst",
        "description": "Analyzes market trends and competitive landscape",
        "system_prompt": """You are a senior market research analyst.
        Research current market conditions, identify trends, and provide
        data-driven insights. Always cite sources and distinguish between
        confirmed data and market speculation.""",
        "model_name": "gpt-4.1",
        "max_tokens": 4096,
        "temperature": 0.2
    },
    "task": "Analyze the current state of the electric vehicle market in Europe, including major players, market share, and growth projections.",
    "tools_enabled": ["auto_search"]
}

payload = {
    "agent_config": {
        "agent_name": "Technical Researcher",
        "description": "Researches technical topics and documentation",
        "system_prompt": """You are a technical research specialist.
        Find accurate, up-to-date technical information from official
        documentation, reputable tech publications, and authoritative sources.
        Provide code examples when relevant.""",
        "model_name": "gpt-4.1",
        "max_tokens": 4096,
        "temperature": 0.1
    },
    "task": "What are the new features in Python 3.12 and how do they improve performance?",
    "tools_enabled": ["auto_search"]
}

payload = {
    "agent_config": {
        "agent_name": "News Analyst",
        "description": "Analyzes current events and news",
        "system_prompt": """You are a news analyst who provides balanced,
        factual summaries of current events. Cross-reference multiple sources,
        note any conflicting reports, and clearly separate facts from opinions.""",
        "model_name": "gpt-4.1",
        "max_tokens": 3000,
        "temperature": 0.3
    },
    "task": "What are the latest developments in AI regulation globally?",
    "tools_enabled": ["auto_search"]
}

payload = {
    "agent_config": {
        "agent_name": "Academic Research Assistant",
        "description": "Assists with academic research and literature review",
        "system_prompt": """You are an academic research assistant.
        Find peer-reviewed sources, academic papers, and scholarly articles.
        Summarize key findings, note methodologies, and identify research gaps.
        Always provide proper citations.""",
        "model_name": "gpt-4.1",
        "max_tokens": 5000,
        "temperature": 0.2
    },
    "task": "What does recent research say about the effectiveness of spaced repetition for language learning?",
    "tools_enabled": ["auto_search"]
}

from swarms_client import SwarmsClient
import os

client = SwarmsClient(api_key=os.getenv("SWARMS_API_KEY"))

research_team = client.swarms.run(
    name="Comprehensive Research Team",
    description="Multi-agent research system with web search",
    swarm_type="SequentialWorkflow",
    task="Analyze the potential impact of quantum computing on cybersecurity over the next decade.",
    agents=[
        {
            "agent_name": "Technical Researcher",
            "description": "Researches technical aspects and current state",
            "system_prompt": """Research the current state of quantum computing,
            focusing on technical capabilities, limitations, and timeline projections.
            Use web search to find the latest developments.""",
            "model_name": "gpt-4.1",
            "role": "researcher",
            "max_loops": 1,
            "max_tokens": 4096,
            "temperature": 0.2
        },
        {
            "agent_name": "Security Analyst",
            "description": "Analyzes cybersecurity implications",
            "system_prompt": """Analyze cybersecurity implications based on the
            technical research provided. Identify vulnerabilities, potential threats,
            and required adaptations in security protocols.""",
            "model_name": "gpt-4.1",
            "role": "analyst",
            "max_loops": 1,
            "max_tokens": 4096,
            "temperature": 0.3
        },
        {
            "agent_name": "Strategic Advisor",
            "description": "Synthesizes findings into recommendations",
            "system_prompt": """Synthesize the technical research and security analysis
            into actionable strategic recommendations. Provide a timeline for action
            and prioritize recommendations by urgency and impact.""",
            "model_name": "gpt-4.1",
            "role": "advisor",
            "max_loops": 1,
            "max_tokens": 5000,
            "temperature": 0.4
        }
    ]
)

print(research_team)

system_prompt = """You are a research assistant with web search capabilities.

When responding to queries:
1. SEARCH: First search for relevant, current information
2. VERIFY: Cross-reference findings across multiple sources
3. SYNTHESIZE: Combine information into a coherent response
4. CITE: Always mention your sources
5. QUALIFY: Note any uncertainties or conflicting information

Prioritize recent sources (within the last year) for time-sensitive topics."""

"task": "What are the latest AI safety developments from the past 3 months?"

system_prompt = """...
Always cite sources in your response using this format:
- [Source Name](URL) - Key finding from this source

End your response with a 'Sources' section listing all references."""

import requests
import time

def search_with_retry(payload, headers, max_retries=3):
    """Execute search request with exponential backoff retry."""
    base_url = "https://api.swarms.world"

    for attempt in range(max_retries):
        try:
            response = requests.post(
                f"{base_url}/v1/agent/completions",
                headers=headers,
                json=payload,
                timeout=120  # Longer timeout for search operations
            )

            if response.status_code == 200:
                result = response.json()
                return {
                    "success": True,
                    "outputs": result.get("outputs"),
                    "usage": result.get("usage")
                }

            elif response.status_code == 429:
                # Rate limited - wait and retry
                wait_time = 2 ** attempt
                print(f"Rate limited. Waiting {wait_time}s before retry...")
                time.sleep(wait_time)
                continue

            elif response.status_code == 503:
                # Service temporarily unavailable
                wait_time = 5 * (attempt + 1)
                print(f"Service unavailable. Waiting {wait_time}s...")
                time.sleep(wait_time)
                continue

            else:
                return {
                    "success": False,
                    "error": f"HTTP {response.status_code}: {response.text}"
                }

        except requests.exceptions.Timeout:
            print(f"Request timed out. Attempt {attempt + 1}/{max_retries}")
            continue

        except requests.exceptions.ConnectionError:
            print(f"Connection error. Attempt {attempt + 1}/{max_retries}")
            time.sleep(2 ** attempt)
            continue

    return {
        "success": False,
        "error": "Max retries exceeded"
    }

# Usage
result = search_with_retry(payload, headers)
if result["success"]:
    print(result["outputs"])
else:
    print(f"Error: {result['error']}")

{
  "job_id": "agent-...",
  "success": true,
  "name": "Research Agent",
  "outputs": "Your comprehensive research response with synthesized information...",
  "usage": {
    "input_tokens": 500,
    "output_tokens": 1200,
    "total_tokens": 1700,
    "img_cost": 0.0,
    "total_cost": 0.025
  },
  "timestamp": "2024-01-15T00:00:00Z"
}

# Cost-efficient configuration for simple queries
efficient_config = {
    "agent_config": {
        "model_name": "gpt-4.1-mini",  # Lower cost model
        "max_tokens": 2000             # Limit output length
    },
    "tools_enabled": ["auto_search"]
}