[IMPROVEMENT][AgentRearrange][Integrate concurrent agent execution in agent rearrange through the comma type] [AgentRearrange][Docs]
Kye Gomez
3 weeks ago
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0813594074
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db8437f52c
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from swarms import Agent, MultiAgentRouter
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from swarms.structs.agent import Agent
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from swarms.structs.multi_agent_router import MultiAgentRouter
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# Example usage:
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# Example usage:
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if __name__ == "__main__":
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agents = [
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# Define some example agents
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Agent(
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agents = [
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agent_name="ResearchAgent",
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Agent(
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agent_description="Specializes in researching topics and providing detailed, factual information",
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agent_name="ResearchAgent",
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system_prompt="You are a research specialist. Provide detailed, well-researched information about any topic, citing sources when possible.",
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description="Specializes in researching topics and providing detailed, factual information",
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),
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system_prompt="You are a research specialist. Provide detailed, well-researched information about any topic, citing sources when possible.",
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Agent(
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model_name="openai/gpt-4o",
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agent_name="CodeExpertAgent",
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),
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agent_description="Expert in writing, reviewing, and explaining code across multiple programming languages",
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Agent(
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system_prompt="You are a coding expert. Write, review, and explain code with a focus on best practices and clean code principles.",
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agent_name="CodeExpertAgent",
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),
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description="Expert in writing, reviewing, and explaining code across multiple programming languages",
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Agent(
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system_prompt="You are a coding expert. Write, review, and explain code with a focus on best practices and clean code principles.",
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agent_name="WritingAgent",
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model_name="openai/gpt-4o",
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agent_description="Skilled in creative and technical writing, content creation, and editing",
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),
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system_prompt="You are a writing specialist. Create, edit, and improve written content while maintaining appropriate tone and style.",
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Agent(
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),
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agent_name="WritingAgent",
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]
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description="Skilled in creative and technical writing, content creation, and editing",
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system_prompt="You are a writing specialist. Create, edit, and improve written content while maintaining appropriate tone and style.",
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model_name="openai/gpt-4o",
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),
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]
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# Initialize routers with different configurations
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# Initialize routers with different configurations
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router_execute = MultiAgentRouter(
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router_execute = MultiAgentRouter(
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agents=agents, execute_task=True
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agents=agents, temperature=0.5, model="claude-sonnet-4-20250514"
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)
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)
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# Example task
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# Example task: Remake the Fibonacci task
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task = "Write a Python function to calculate fibonacci numbers"
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task = "Use all the agents available to you to remake the Fibonacci function in Python, providing both an explanation and code."
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result_execute = router_execute.run(task)
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try:
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print(result_execute)
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# Process the task with execution
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print("\nWith task execution:")
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result_execute = router_execute.route_task(task)
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print(result_execute)
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except Exception as e:
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print(f"Error occurred: {str(e)}")
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from swarms import Agent
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from swarms import Agent
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from swarms.prompts.finance_agent_sys_prompt import (
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from swarms_tools.finance.okx_tool import okx_api_tool
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from swarms_tools import yahoo_finance_api
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# Initialize the agent
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# Initialize the agent
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agent = Agent(
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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agent_name="Financial-Analysis-Agent",
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agent_description="Personal finance advisor agent",
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agent_description="Personal finance advisor agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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max_loops=1,
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model_name="gpt-4o-mini",
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model_name="gpt-4o-mini",
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tools=[yahoo_finance_api],
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tools=[okx_api_tool],
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dynamic_temperature_enabled=True,
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dynamic_temperature_enabled=True,
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)
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)
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agent.run(
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agent.run("fetch the current price of bitcoin with okx")
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"Fetch the data for nvidia and tesla both with the yahoo finance api"
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)
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@ -0,0 +1,63 @@
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from swarms.structs.agent import Agent
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# Agent 1: Risk Metrics Calculator
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risk_metrics_agent = Agent(
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agent_name="Risk-Metrics-Calculator",
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agent_description="Calculates key risk metrics like VaR, Sharpe ratio, and volatility",
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system_prompt="""You are a risk metrics specialist. Calculate and explain:
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- Value at Risk (VaR)
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- Sharpe ratio
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- Volatility
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- Maximum drawdown
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- Beta coefficient
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Provide clear, numerical results with brief explanations.""",
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max_loops=1,
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model_name="gpt-4o-mini",
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dynamic_temperature_enabled=True,
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)
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# Agent 3: Market Risk Monitor
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market_risk_agent = Agent(
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agent_name="Market-Risk-Monitor",
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agent_description="Monitors market conditions and identifies risk factors",
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system_prompt="""You are a market risk monitor. Identify and assess:
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- Market volatility trends
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- Economic risk factors
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- Geopolitical risks
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- Interest rate risks
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- Currency risks
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Provide current risk alerts and trends.""",
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max_loops=1,
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dynamic_temperature_enabled=True,
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)
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# Agent 2: Portfolio Risk Analyzer
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portfolio_risk_agent = Agent(
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agent_name="Portfolio-Risk-Analyzer",
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agent_description="Analyzes portfolio diversification and concentration risk",
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system_prompt="""You are a portfolio risk analyst. Focus on:
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- Portfolio diversification analysis
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- Concentration risk assessment
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- Correlation analysis
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- Sector/asset allocation risk
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- Liquidity risk evaluation
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Provide actionable insights for risk reduction.""",
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max_loops=1,
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dynamic_temperature_enabled=True,
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handoffs=[
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risk_metrics_agent,
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market_risk_agent,
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],
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)
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out = portfolio_risk_agent.run(
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"Calculate VaR and Sharpe ratio for a portfolio with 15% annual return and 20% volatility using the risk metrics agent and market risk agent"
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)
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print(out)
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@ -0,0 +1,86 @@
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from swarms import Agent, AgentRearrange
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# Define all prompts as strings before agent initializations
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boss_agent_prompt = """
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You are the BossAgent responsible for managing and overseeing a swarm of agents analyzing company expenses.
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Your job is to dynamically assign tasks, prioritize their execution, and ensure that all agents collaborate efficiently.
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After receiving a report on the company's expenses, you will break down the work into smaller tasks,
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assigning specific tasks to each agent, such as detecting recurring high costs, categorizing expenditures,
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and identifying unnecessary transactions. Ensure the results are communicated back in a structured way
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so the finance team can take actionable steps to cut off unproductive spending. You also monitor and
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dynamically adapt the swarm to optimize their performance. Finally, you summarize their findings
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into a coherent report.
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"""
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expense_analyzer_prompt = """
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Your task is to carefully analyze the company's expense data provided to you.
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You will focus on identifying high-cost recurring transactions, categorizing expenditures
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(e.g., marketing, operations, utilities, etc.), and flagging areas where there seems to be excessive spending.
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You will provide a detailed breakdown of each category, along with specific recommendations for cost-cutting.
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Pay close attention to monthly recurring subscriptions, office supplies, and non-essential expenditures.
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"""
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summary_generator_prompt = """
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After receiving the detailed breakdown from the ExpenseAnalyzer,
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your task is to create a concise summary of the findings. You will focus on the most actionable insights,
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such as highlighting the specific transactions that can be immediately cut off and summarizing the areas
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where the company is overspending. Your summary will be used by the BossAgent to generate the final report.
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Be clear and to the point, emphasizing the urgency of cutting unnecessary expenses.
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"""
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# Swarm-Level Prompt (Collaboration Prompt)
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swarm_prompt = """
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As a swarm, your collective goal is to analyze the company's expenses and identify transactions that should be cut off.
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You will work collaboratively to break down the entire process of expense analysis into manageable steps.
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The BossAgent will direct the flow and assign tasks dynamically to the agents. The ExpenseAnalyzer will first
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focus on breaking down the expense report, identifying high-cost recurring transactions, categorizing them,
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and providing recommendations for potential cost reduction. After the analysis, the SummaryGenerator will then
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consolidate all the findings into an actionable summary that the finance team can use to immediately cut off unnecessary expenses.
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Together, your collaboration is essential to streamlining and improving the company's financial health.
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"""
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# Initialize the boss agent (Director)
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boss_agent = Agent(
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agent_name="BossAgent",
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system_prompt=boss_agent_prompt,
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max_loops=1,
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)
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# Initialize worker 1: Expense Analyzer
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worker1 = Agent(
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agent_name="ExpenseAnalyzer",
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system_prompt=expense_analyzer_prompt,
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max_loops=1,
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dashboard=False,
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)
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# Initialize worker 2: Summary Generator
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worker2 = Agent(
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agent_name="SummaryGenerator",
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system_prompt=summary_generator_prompt,
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max_loops=1,
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)
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# Create a list of agents
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agents = [boss_agent, worker1, worker2]
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# Define the flow pattern for the swarm
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flow = "BossAgent -> ExpenseAnalyzer, SummaryGenerator"
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# Using AgentRearrange class to manage the swarm
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agent_system = AgentRearrange(agents=agents, flow=flow)
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# Input task for the swarm
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task = f"""
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{swarm_prompt}
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The company has been facing a rising number of unnecessary expenses, and the finance team needs a detailed
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analysis of recent transactions to identify which expenses can be cut off to improve profitability.
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Analyze the provided transaction data and create a detailed report on cost-cutting opportunities,
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focusing on recurring transactions and non-essential expenditures.
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"""
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# Run the swarm system with the task
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output = agent_system.run(task)
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print(output)
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