​
Batch Processing

This example demonstrates how to process multiple tasks simultaneously using batch processing. This is ideal for scenarios where you need to analyze multiple documents, perform parallel analyses, or handle bulk operations efficiently.

​
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

​
Installation

pip3 install -U swarms-client

​
Get Your Swarms API Key

  1. Visit https://swarms.world/platform/api-keys
  2. Create an account or sign in
  3. Generate a new API key
  4. Store it securely in your environment variables

​
Code

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))

​
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

​
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

​
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

​
Environment Setup

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

​
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

​
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