Batched grid workflow

curl -X POST "https://api.swarms.world/v1/batched-grid-workflow/completions" \
  -H "x-api-key: $SWARMS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Product Review Analysis Grid",
    "description": "Multiple analysts reviewing multiple product categories",
    "agent_completions": [
      {
        "agent_name": "Technical Analyst",
        "description": "Focuses on technical specifications and performance",
        "system_prompt": "You are a technical analyst. Evaluate products based on specifications, performance metrics, build quality, and technical innovation.",
        "model_name": "gpt-4o",
        "max_loops": 1,
        "temperature": 0.3
      },
      {
        "agent_name": "User Experience Analyst",
        "description": "Focuses on usability and user satisfaction",
        "system_prompt": "You are a UX analyst. Evaluate products based on ease of use, user interface, customer satisfaction, and overall user experience.",
        "model_name": "gpt-4o",
        "max_loops": 1,
        "temperature": 0.4
      },
      {
        "agent_name": "Value Analyst",
        "description": "Focuses on pricing and value proposition",
        "system_prompt": "You are a value analyst. Evaluate products based on pricing, cost-effectiveness, ROI, and overall value for money.",
        "model_name": "gpt-4o",
        "max_loops": 1,
        "temperature": 0.3
      }
    ],
    "tasks": [
      "Analyze the smartphone market segment and top products",
      "Analyze the laptop market segment and top products",
      "Analyze the tablet market segment and top products"
    ],
    "max_loops": 1
  }'

import requests

API_BASE_URL = "https://api.swarms.world"
API_KEY = "your_api_key_here"

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

# Example with multiple loops for iterative refinement
workflow_config = {
    "name": "Creative Writing Grid with Refinement",
    "description": "Multiple writing styles with iterative improvement",
    "agent_completions": [
        {
            "agent_name": "Technical Writer",
            "description": "Clear, precise technical writing",
            "system_prompt": "You are a technical writer. Write clearly and precisely, focusing on accuracy and comprehensibility. In subsequent loops, refine based on previous output.",
            "model_name": "gpt-4o",
            "max_loops": 1,
            "temperature": 0.4
        },
        {
            "agent_name": "Creative Writer",
            "description": "Engaging, narrative-driven writing",
            "system_prompt": "You are a creative writer. Write engagingly with vivid descriptions and narrative flow. In subsequent loops, enhance the storytelling elements.",
            "model_name": "gpt-4o",
            "max_loops": 1,
            "temperature": 0.7
        },
        {
            "agent_name": "Academic Writer",
            "description": "Formal, research-oriented writing",
            "system_prompt": "You are an academic writer. Write formally with citations, evidence-based arguments, and scholarly tone. In subsequent loops, strengthen the academic rigor.",
            "model_name": "gpt-4o",
            "max_loops": 1,
            "temperature": 0.3
        }
    ],
    "tasks": [
        "Write about the impact of artificial intelligence on society",
        "Write about climate change solutions"
    ],
    "max_loops": 3  # Run 3 iterations for refinement
}

response = requests.post(
    f"{API_BASE_URL}/v1/batched-grid-workflow/completions",
    headers=headers,
    json=workflow_config
)

if response.status_code == 200:
    result = response.json()
    print(f"Workflow completed with {workflow_config['max_loops']} refinement loops")
    print(f"Total tokens: {result['usage']['total_tokens']}")
    print(f"Total cost: ${result['usage']['token_cost']:.4f}")

    # Compare different writing styles for each topic
    for task_idx, task_results in enumerate(result['outputs']):
        print(f"\n{'='*60}")
        print(f"Topic {task_idx + 1}")
        print('='*60)
        for agent_name, output in task_results.items():
            print(f"\n{agent_name}:")
            print(output[:200] + "...")
else:
    print(f"Error: {response.status_code} - {response.text}")