Concurrent Workflow Example

import requests
import os

API_BASE_URL = "https://api.swarms.world"
API_KEY = os.environ.get("SWARMS_API_KEY", "your_api_key_here")

headers = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
}

def analyze_competitors(competitors: list[str], industry: str) -> dict:
    """Analyze multiple competitors in parallel."""

    # Create one agent per competitor
    agents = []
    for competitor in competitors:
        agents.append({
            "agent_name": f"{competitor} Analyst",
            "description": f"Analyzes {competitor}'s market position",
            "system_prompt": f"""You are a competitive intelligence analyst for {competitor}.

Analyze across these dimensions:
1. COMPANY OVERVIEW - Founded, size, funding, leadership
2. PRODUCTS - Core offerings, recent launches, unique features
3. MARKET POSITION - Target segments, market share, partnerships
4. PRICING - Model, price points, enterprise approach
5. STRENGTHS & WEAKNESSES - Top 3 of each

Be specific with facts and numbers.""",
            "model_name": "gpt-4.1",
            "max_loops": 1,
            "temperature": 0.3
        })

    swarm_config = {
        "name": f"Competitive Intelligence: {industry}",
        "description": "Parallel competitor analysis",
        "swarm_type": "ConcurrentWorkflow",
        "task": f"Analyze market position in the {industry} industry for: {', '.join(competitors)}",
        "agents": agents,
        "max_loops": 1
    }

    response = requests.post(
        f"{API_BASE_URL}/v1/swarm/completions",
        headers=headers,
        json=swarm_config,
        timeout=180
    )

    return response.json()

# Define competitors to analyze
competitors = ["OpenAI", "Anthropic", "Google DeepMind", "Cohere", "Mistral AI"]

# Run parallel analysis
result = analyze_competitors(competitors, "AI/LLM Providers")

# Display results
execution_time = result.get("execution_time", 0)
num_competitors = len(result.get("output", []))

print(f"Analyzed {num_competitors} competitors in {execution_time:.1f}s")
print(f"Sequential would take ~{execution_time * num_competitors:.1f}s ({num_competitors}x slower)")

for output in result.get("output", []):
    competitor = output["role"].replace(" Analyst", "")
    content = output["content"]

    print(f"\n{'='*50}")
    print(f"{competitor.upper()}")
    print(f"{'='*50}")
    print(content[:600] + "...")

print(f"\nTotal cost: ${result['usage']['billing_info']['total_cost']:.4f}")

Analyzed 5 competitors in 28.4s
Sequential would take ~142.0s (5x slower)

==================================================
OPENAI
==================================================
COMPANY OVERVIEW
- Founded: 2015, San Francisco
- Employees: ~1,500+
- Valuation: $80B+ (2024)
- Leadership: Sam Altman (CEO)

PRODUCTS
- Core: GPT-4, ChatGPT, DALL-E, Whisper
- Recent: GPT-4 Turbo, Custom GPTs, GPT Store...

==================================================
ANTHROPIC
==================================================
COMPANY OVERVIEW
- Founded: 2021, San Francisco
- Employees: ~500...

Total cost: $0.2156