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Concurrent Workflow Example

A ConcurrentWorkflow runs multiple agents simultaneously, allowing for parallel execution of tasks. This architecture drastically reduces execution time for tasks that can be performed in parallel, making it ideal for high-throughput scenarios where agents work on similar tasks concurrently.

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How Concurrent Workflow Works

In a concurrent workflow:
  1. Parallel Execution: All agents receive the same task and execute simultaneously
  2. Independent Processing: Each agent works independently without dependencies on others
  3. Aggregated Results: Outputs from all agents are collected and returned together
  4. Maximum Efficiency: Execution time is determined by the slowest agent, not the sum of all agents

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Basic Example: Multi-Analyst Financial Review

This example demonstrates three analysts working in parallel to provide comprehensive insights: 
from swarms import Agent, ConcurrentWorkflow

# Create agents for different analysis tasks
market_analyst = Agent(
    agent_name="Market-Analyst",
    system_prompt="Analyze market trends and provide insights on the given topic.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

financial_analyst = Agent(
    agent_name="Financial-Analyst", 
    system_prompt="Provide financial analysis and recommendations on the given topic.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="Assess risks and provide risk management strategies for the given topic.",
    model_name="gpt-4o-mini", 
    max_loops=1,
)

# Create concurrent workflow
concurrent_workflow = ConcurrentWorkflow(
    agents=[market_analyst, financial_analyst, risk_analyst],
    max_loops=1,
)

# Run all agents concurrently on the same task
results = concurrent_workflow.run(
    "Analyze the potential impact of AI technology on the healthcare industry"
)

print(results)

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How This Example Works

  1. Task Distribution: The task “Analyze the potential impact of AI technology on the healthcare industry” is sent to all three agents simultaneously
  2. Parallel Processing: Each analyst processes the task independently at the same time:
    • Market Analyst examines market trends
    • Financial Analyst evaluates financial implications
    • Risk Analyst identifies potential risks
  3. Result Collection: All three analyses are collected and returned as a dictionary
  4. Comprehensive Output: You receive multiple perspectives on the same topic in the time it takes for the slowest agent to complete

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Common Use Cases

ConcurrentWorkflow excels at:
  • Multi-Perspective Analysis: Getting different viewpoints on the same topic
  • Batch Processing: Processing multiple similar items simultaneously
  • Content Generation: Creating multiple variations of content at once
  • Parallel Research: Researching different aspects of a topic concurrently
  • Competitive Analysis: Analyzing multiple competitors simultaneously
  • A/B Testing: Generating multiple approaches to compare

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Real-World Examples

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Investment Analysis Team

Analyze a stock from multiple financial perspectives: 
from swarms import Agent, ConcurrentWorkflow

# Create specialized financial analysts
technical_analyst = Agent(
    agent_name="Technical-Analyst",
    system_prompt="Perform technical analysis using chart patterns, indicators, and price action.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

fundamental_analyst = Agent(
    agent_name="Fundamental-Analyst",
    system_prompt="Analyze company financials, earnings, valuation metrics, and business fundamentals.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

sentiment_analyst = Agent(
    agent_name="Sentiment-Analyst",
    system_prompt="Analyze market sentiment, news, social media, and investor sentiment indicators.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

quant_analyst = Agent(
    agent_name="Quant-Analyst",
    system_prompt="Perform quantitative analysis using statistical models, algorithms, and data-driven metrics.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Create investment analysis workflow
investment_workflow = ConcurrentWorkflow(
    agents=[technical_analyst, fundamental_analyst, sentiment_analyst, quant_analyst],
    max_loops=1,
)

# Analyze a stock from all angles simultaneously
analysis = investment_workflow.run("Should we invest in NVIDIA stock right now?")

# Process results from each analyst
for analyst_name, analysis_result in analysis.items():
    print(f"\n=== {analyst_name} ===")
    print(analysis_result)

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Content Variation Generator

Generate multiple content variations for A/B testing: 
from swarms import Agent, ConcurrentWorkflow

# Create agents for different content styles
formal_writer = Agent(
    agent_name="Formal-Writer",
    system_prompt="Write in a professional, formal tone suitable for corporate communications.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

casual_writer = Agent(
    agent_name="Casual-Writer",
    system_prompt="Write in a friendly, conversational tone that resonates with general audiences.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

technical_writer = Agent(
    agent_name="Technical-Writer",
    system_prompt="Write with technical precision and detail for expert audiences.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

persuasive_writer = Agent(
    agent_name="Persuasive-Writer",
    system_prompt="Write compelling, persuasive copy that drives action and engagement.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Create content generation workflow
content_workflow = ConcurrentWorkflow(
    agents=[formal_writer, casual_writer, technical_writer, persuasive_writer],
    max_loops=1,
)

# Generate multiple versions simultaneously
variations = content_workflow.run(
    "Create a product description for our new AI-powered project management tool"
)

print(variations)

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Multi-Language Translation

Translate content into multiple languages simultaneously: 
from swarms import Agent, ConcurrentWorkflow

# Create translation agents for different languages
spanish_translator = Agent(
    agent_name="Spanish-Translator",
    system_prompt="Translate the given text into Spanish while maintaining tone and context.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

french_translator = Agent(
    agent_name="French-Translator",
    system_prompt="Translate the given text into French while maintaining tone and context.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

german_translator = Agent(
    agent_name="German-Translator",
    system_prompt="Translate the given text into German while maintaining tone and context.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

japanese_translator = Agent(
    agent_name="Japanese-Translator",
    system_prompt="Translate the given text into Japanese while maintaining tone and context.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Create translation workflow
translation_workflow = ConcurrentWorkflow(
    agents=[spanish_translator, french_translator, german_translator, japanese_translator],
    max_loops=1,
)

# Translate simultaneously into all languages
translations = translation_workflow.run(
    "Welcome to our platform! We're excited to help you achieve your goals."
)

print(translations)

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Competitive Product Analysis

Analyze multiple competitors simultaneously: 
from swarms import Agent, ConcurrentWorkflow

# Create competitor analysis agents
product_analyzer = Agent(
    agent_name="Product-Analyzer",
    system_prompt="Analyze the product features, capabilities, and user experience.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

pricing_analyzer = Agent(
    agent_name="Pricing-Analyzer",
    system_prompt="Analyze pricing strategies, plans, and value proposition.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

marketing_analyzer = Agent(
    agent_name="Marketing-Analyzer",
    system_prompt="Analyze marketing strategies, messaging, and positioning.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

customer_analyzer = Agent(
    agent_name="Customer-Analyzer",
    system_prompt="Analyze customer reviews, satisfaction, and feedback patterns.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Create competitive analysis workflow
competitive_workflow = ConcurrentWorkflow(
    agents=[product_analyzer, pricing_analyzer, marketing_analyzer, customer_analyzer],
    max_loops=1,
)

# Analyze competitor from all angles
competitor_analysis = competitive_workflow.run(
    "Analyze Notion as a competitor in the productivity software space"
)

print(competitor_analysis)

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Performance Benefits

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Time Savings Comparison

Sequential Execution (3 agents, 10 seconds each):
  • Agent 1: 10 seconds
  • Agent 2: 10 seconds
  • Agent 3: 10 seconds
  • Total: 30 seconds
Concurrent Execution (3 agents, 10 seconds each):
  • All agents run simultaneously
  • Total: ~10 seconds
Result: 3x faster execution

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Best Practices

  1. Independent Tasks: Use for tasks that don’t depend on each other’s outputs
  2. Similar Complexity: Agents should have roughly similar execution times for optimal efficiency
  3. Resource Management: Consider system resources when running many agents concurrently
  4. Error Handling: One agent’s failure shouldn’t block others from completing
  5. Result Processing: Plan how to aggregate and synthesize multiple outputs

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Limitations and Considerations

  • Resource Intensive: Running multiple agents simultaneously requires more computational resources
  • No Dependencies: Not suitable when agents need outputs from other agents
  • Result Management: More complex to process multiple simultaneous outputs
  • Cost: May incur higher API costs when using paid LLM services

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Combining with Other Patterns

ConcurrentWorkflow works well with: 
from swarms import Agent, ConcurrentWorkflow, MixtureOfAgents

# Run agents concurrently, then aggregate results
experts = [market_analyst, financial_analyst, risk_analyst]

# Use MoA to synthesize concurrent results
aggregator = Agent(
    agent_name="Synthesizer",
    system_prompt="Combine all analyses into a comprehensive recommendation.",
    model_name="gpt-4o-mini",
)

moa = MixtureOfAgents(
    agents=experts,
    aggregator_agent=aggregator,
)

final_recommendation = moa.run("Should we invest in renewable energy stocks?")
print(final_recommendation)

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