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What This Example Shows

  • Processing multiple agent tasks in a single request
  • Configuring different agents for different types of analysis
  • Efficient batch execution for multiple related tasks
  • Handling diverse task types with specialized agents

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Quick Start

  • Python
  • JavaScript
  • cURL
import os
from swarms_client import SwarmsClient
from dotenv import load_dotenv
import json

# Load environment variables
load_dotenv()

# Initialize the client
client = SwarmsClient(
    api_key=os.getenv("SWARMS_API_KEY"),
)

# Define multiple batch requests
batch_requests = [
{
    "agent_config": {
        "agent_name": "Bloodwork Diagnosis Expert",
        "description": "Expert in blood work interpretation.",
        "system_prompt": (
            "You are a doctor who interprets blood work. Give concise, clear explanations and possible diagnoses."
        ),
        "model_name": "claude-sonnet-4-20250514",
        "max_loops": 1,
        "max_tokens": 1000,
        "temperature": 0.5,
    },
    "task": (
        "Blood work: Hemoglobin 10.2 (low), WBC 13,000 (high), Platelets 180,000 (normal), "
        "ALT 65 (high), AST 70 (high). Interpret and suggest diagnoses."
    ),
},
{
    "agent_config": {
        "agent_name": "Radiology Report Summarizer",
        "description": "Expert in summarizing radiology reports.",
        "system_prompt": (
            "You are a radiologist. Summarize the findings of radiology reports in clear, patient-friendly language."
        ),
        "model_name": "claude-sonnet-4-20250514",
        "max_loops": 1,
        "max_tokens": 1000,
        "temperature": 0.5,
    },
    "task": (
        "Radiology report: Chest X-ray shows mild cardiomegaly, no infiltrates, no effusion. Summarize the findings."
    ),
},
]

# Execute batch processing
result = client.agent.batch.run(body=batch_requests)

print(json.dumps(result, indent=4))

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Batch Processing Benefits

  • Efficiency: Process multiple tasks in parallel
  • Cost Optimization: Reduce API call overhead
  • Consistency: Apply similar processing across multiple items
  • Scalability: Handle large volumes of work efficiently

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Use Cases

Batch processing is ideal for:
  • Document Analysis: Review multiple contracts, reports, or documents
  • Data Processing: Analyze large datasets with multiple perspectives
  • Content Generation: Create variations of content for different audiences
  • Quality Assurance: Review multiple code files, designs, or content pieces
  • Customer Support: Process multiple support tickets with specialized agents

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Expected Output

The batch processor will return results for each task:
  • Bloodwork analysis with diagnosis and recommendations
  • Radiology report summary in patient-friendly language
  • Each result maintains the structure and quality of individual agent runs

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Environment Setup

Create a .env file in your project directory: 
SWARMS_API_KEY=your_api_key_here

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Advanced Batch Processing

You can extend this pattern to:
  • Dynamic Batching: Group similar tasks automatically
  • Priority Processing: Assign importance levels to different tasks
  • Result Aggregation: Combine multiple results into unified insights
  • Error Handling: Gracefully handle failures in individual batch items

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Next Steps

After mastering batch processing, explore:
  • Multi-agent swarms for complex collaborative workflows
  • Sequential workflows for dependent tasks
  • Concurrent workflows for parallel execution
  • Agent routing for intelligent task distribution
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