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ConcurrentWorkflow Examples

The ConcurrentWorkflow architecture enables parallel execution of multiple agents, allowing them to work simultaneously on different aspects of a task. This is particularly useful for complex tasks that can be broken down into independent subtasks.

Prerequisites

  • Python 3.7+
  • OpenAI API key or other supported LLM provider keys
  • Swarms library

Installation

pip3 install -U swarms

Environment Variables

WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY=""
ANTHROPIC_API_KEY=""
GROQ_API_KEY=""

Basic Usage

1. Initialize Specialized Agents

from swarms import Agent, ConcurrentWorkflow

# Initialize market research agent
market_researcher = Agent(
    agent_name="Market-Researcher",
    system_prompt="""You are a market research specialist. Your tasks include:
    1. Analyzing market trends and patterns
    2. Identifying market opportunities and threats
    3. Evaluating competitor strategies
    4. Assessing customer needs and preferences
    5. Providing actionable market insights""",
    model_name="gpt-4o",
    max_loops=1,
)

# Initialize financial analyst agent
financial_analyst = Agent(
    agent_name="Financial-Analyst",
    system_prompt="""You are a financial analysis expert. Your responsibilities include:
    1. Analyzing financial statements
    2. Evaluating investment opportunities
    3. Assessing risk factors
    4. Providing financial forecasts
    5. Recommending financial strategies""",
    model_name="gpt-4o",
    max_loops=1,
)

# Initialize technical analyst agent
technical_analyst = Agent(
    agent_name="Technical-Analyst",
    system_prompt="""You are a technical analysis specialist. Your focus areas include:
    1. Analyzing price patterns and trends
    2. Evaluating technical indicators
    3. Identifying support and resistance levels
    4. Assessing market momentum
    5. Providing trading recommendations""",
    model_name="gpt-4o",
    max_loops=1,
)

2. Create and Run ConcurrentWorkflow

# Create list of agents
agents = [market_researcher, financial_analyst, technical_analyst]

# Initialize the concurrent workflow
workflow = ConcurrentWorkflow(
    name="market-analysis-workflow",
    agents=agents,
    max_loops=1,
)

# Run the workflow
result = workflow.run(
    "Analyze Tesla (TSLA) stock from market, financial, and technical perspectives"
)

Advanced Usage

1. Custom Agent Configuration

from swarms import Agent, ConcurrentWorkflow

# Initialize agents with custom configurations
sentiment_analyzer = Agent(
    agent_name="Sentiment-Analyzer",
    system_prompt="You analyze social media sentiment...",
    model_name="gpt-4o",
    max_loops=1,
    temperature=0.7,
    streaming_on=True,
    verbose=True,
)

news_analyzer = Agent(
    agent_name="News-Analyzer",
    system_prompt="You analyze news articles and reports...",
    model_name="gpt-4o",
    max_loops=1,
    temperature=0.5,
    streaming_on=True,
    verbose=True,
)

# Create and run workflow
workflow = ConcurrentWorkflow(
    name="sentiment-analysis-workflow",
    agents=[sentiment_analyzer, news_analyzer],
    max_loops=1,
    verbose=True,
)

result = workflow.run(
    "Analyze the market sentiment for Bitcoin based on social media and news"
)

2. Error Handling and Logging

try:
    workflow = ConcurrentWorkflow(
        name="error-handled-workflow",
        agents=agents,
        max_loops=1,
        verbose=True,
    )

    result = workflow.run("Complex analysis task")

    # Process results
    for agent_result in result:
        print(f"Agent {agent_result['agent']}: {agent_result['output']}")

except Exception as e:
    print(f"Error in workflow: {str(e)}")

Best Practices

  1. Task Distribution:
  2. Break down complex tasks into independent subtasks
  3. Assign appropriate agents to each subtask

  4. Ensure tasks can be processed concurrently

  5. Agent Configuration:

  6. Use specialized agents for specific tasks
  7. Configure appropriate model parameters

  8. Set meaningful system prompts

  9. Resource Management:

  10. Monitor concurrent execution
  11. Handle rate limits appropriately

  12. Manage memory usage

  13. Error Handling:

  14. Implement proper error handling
  15. Log errors and exceptions
  16. Provide fallback mechanisms

Example Implementation

Here's a complete example showing how to use ConcurrentWorkflow for a comprehensive market analysis:

import os
from swarms import Agent, ConcurrentWorkflow

# Initialize specialized agents
market_analyst = Agent(
    agent_name="Market-Analyst",
    system_prompt="""You are a market analysis specialist focusing on:
    1. Market trends and patterns
    2. Competitive analysis
    3. Market opportunities
    4. Industry dynamics
    5. Growth potential""",
    model_name="gpt-4o",
    max_loops=1,
)

financial_analyst = Agent(
    agent_name="Financial-Analyst",
    system_prompt="""You are a financial analysis expert specializing in:
    1. Financial statements analysis
    2. Ratio analysis
    3. Cash flow analysis
    4. Valuation metrics
    5. Risk assessment""",
    model_name="gpt-4o",
    max_loops=1,
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="""You are a risk assessment specialist focusing on:
    1. Market risks
    2. Operational risks
    3. Financial risks
    4. Regulatory risks
    5. Strategic risks""",
    model_name="gpt-4o",
    max_loops=1,
)

# Create the concurrent workflow
workflow = ConcurrentWorkflow(
    name="comprehensive-analysis-workflow",
    agents=[market_analyst, financial_analyst, risk_analyst],
    max_loops=1,
    verbose=True,
)

# Run the analysis
try:
    result = workflow.run(
        """Provide a comprehensive analysis of Apple Inc. (AAPL) including:
        1. Market position and competitive analysis
        2. Financial performance and health
        3. Risk assessment and mitigation strategies"""
    )

    # Process and display results
    for agent_result in result:
        print(f"\nAnalysis from {agent_result['agent']}:")
        print(agent_result['output'])
        print("-" * 50)

except Exception as e:
    print(f"Error during analysis: {str(e)}")

This comprehensive guide demonstrates how to effectively use the ConcurrentWorkflow architecture for parallel processing of complex tasks using multiple specialized agents.