[IMPROVE][InteractiveGroupchat][Improve the prompting so the agents acknowledge eachother + speaker functions][DOCS][Improvement]
Kye Gomez
2 days ago
parent
21b4e89499
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6b04ec671e
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"""
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Enhanced Collaborative InteractiveGroupChat Example
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This example demonstrates the improved collaborative behavior where agents:
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1. Read and understand all previous responses
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2. Acknowledge what other agents have said
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3. Build upon their insights rather than repeating information
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4. Synthesize multiple perspectives
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5. Delegate appropriately using @mentions
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The enhanced prompts ensure agents work as a true collaborative team.
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"""
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from swarms import Agent
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from swarms.structs.interactive_groupchat import (
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InteractiveGroupChat,
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round_robin_speaker,
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)
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def create_collaborative_agents():
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"""Create agents designed for enhanced collaboration."""
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# Data Analyst - focuses on data insights and trends
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analyst = Agent(
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agent_name="analyst",
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system_prompt="""You are a senior data analyst with expertise in business intelligence, statistical analysis, and data visualization. You excel at:
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- Analyzing complex datasets and identifying trends
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- Creating actionable insights from data
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- Providing quantitative evidence for business decisions
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- Identifying patterns and correlations in data
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When collaborating, always reference specific data points and build upon others' insights with quantitative support.""",
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llm="gpt-3.5-turbo",
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)
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# Market Researcher - focuses on market trends and customer insights
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researcher = Agent(
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agent_name="researcher",
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system_prompt="""You are a market research specialist with deep expertise in consumer behavior, competitive analysis, and market trends. You excel at:
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- Understanding customer needs and preferences
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- Analyzing competitive landscapes
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- Identifying market opportunities and threats
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- Providing qualitative insights that complement data analysis
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When collaborating, always connect market insights to business implications and build upon data analysis with market context.""",
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llm="gpt-3.5-turbo",
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)
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# Strategy Consultant - focuses on strategic recommendations
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strategist = Agent(
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agent_name="strategist",
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system_prompt="""You are a strategic consultant with expertise in business strategy, competitive positioning, and strategic planning. You excel at:
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- Developing comprehensive business strategies
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- Identifying competitive advantages
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- Creating actionable strategic recommendations
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- Synthesizing multiple perspectives into coherent strategies
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When collaborating, always synthesize insights from all team members and provide strategic recommendations that leverage the collective expertise.""",
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llm="gpt-3.5-turbo",
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)
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return [analyst, researcher, strategist]
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def example_comprehensive_analysis():
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"""Example of comprehensive collaborative analysis."""
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print("=== Enhanced Collaborative Analysis Example ===\n")
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agents = create_collaborative_agents()
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# Create group chat with round robin speaker function
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group_chat = InteractiveGroupChat(
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name="Strategic Analysis Team",
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description="A collaborative team for comprehensive business analysis",
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agents=agents,
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speaker_function=round_robin_speaker,
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interactive=False,
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)
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# Complex task that requires collaboration
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task = """Analyze our company's performance in the e-commerce market.
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We have the following data:
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- Q3 revenue: $2.5M (up 15% from Q2)
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- Customer acquisition cost: $45 (down 8% from Q2)
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- Customer lifetime value: $180 (up 12% from Q2)
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- Market share: 3.2% (up 0.5% from Q2)
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- Competitor analysis shows 3 major players with 60% market share combined
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@analyst @researcher @strategist please provide a comprehensive analysis and strategic recommendations."""
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print(f"Task: {task}\n")
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print("Expected collaborative behavior:")
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print(
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"1. Analyst: Analyzes the data trends and provides quantitative insights"
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)
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print(
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"2. Researcher: Builds on data with market context and competitive analysis"
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)
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print(
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"3. Strategist: Synthesizes both perspectives into strategic recommendations"
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)
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print("\n" + "=" * 80 + "\n")
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response = group_chat.run(task)
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print(f"Collaborative Response:\n{response}")
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def example_problem_solving():
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"""Example of collaborative problem solving."""
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print("\n" + "=" * 80)
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print("=== Collaborative Problem Solving Example ===\n")
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agents = create_collaborative_agents()
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group_chat = InteractiveGroupChat(
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name="Problem Solving Team",
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description="A team that collaborates to solve complex business problems",
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agents=agents,
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speaker_function=round_robin_speaker,
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interactive=False,
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)
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# Problem-solving task
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task = """We're experiencing declining customer retention rates (down 20% in the last 6 months).
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Our customer satisfaction scores are also dropping (from 8.5 to 7.2).
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@analyst please analyze the retention data, @researcher investigate customer feedback and market trends,
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and @strategist develop a comprehensive solution strategy."""
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print(f"Task: {task}\n")
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print("Expected collaborative behavior:")
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print("1. Analyst: Identifies patterns in retention data")
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print(
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"2. Researcher: Explores customer feedback and market factors"
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)
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print(
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"3. Strategist: Combines insights to create actionable solutions"
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)
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print("\n" + "=" * 80 + "\n")
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response = group_chat.run(task)
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print(f"Collaborative Response:\n{response}")
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def example_agent_delegation():
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"""Example showing how agents delegate to each other."""
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print("\n" + "=" * 80)
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print("=== Agent Delegation Example ===\n")
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agents = create_collaborative_agents()
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group_chat = InteractiveGroupChat(
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name="Delegation Team",
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description="A team that demonstrates effective delegation and collaboration",
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agents=agents,
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speaker_function=round_robin_speaker,
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interactive=False,
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)
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# Task that encourages delegation
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task = """We need to evaluate a potential new market entry opportunity in Southeast Asia.
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The initial data shows promising growth potential, but we need a comprehensive assessment.
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@analyst start with the market data analysis, then delegate to @researcher for market research,
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and finally @strategist should provide strategic recommendations."""
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print(f"Task: {task}\n")
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print("Expected behavior:")
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print(
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"1. Analyst: Analyzes data and delegates to researcher for deeper market insights"
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)
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print(
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"2. Researcher: Builds on data analysis and delegates to strategist for recommendations"
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)
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print(
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"3. Strategist: Synthesizes all insights into strategic recommendations"
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)
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print("\n" + "=" * 80 + "\n")
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response = group_chat.run(task)
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print(f"Collaborative Response:\n{response}")
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def example_synthesis_and_integration():
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"""Example showing synthesis of multiple perspectives."""
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print("\n" + "=" * 80)
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print("=== Synthesis and Integration Example ===\n")
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agents = create_collaborative_agents()
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group_chat = InteractiveGroupChat(
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name="Synthesis Team",
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description="A team that excels at integrating multiple perspectives",
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agents=agents,
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speaker_function=round_robin_speaker,
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interactive=False,
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)
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# Task requiring synthesis
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task = """We have conflicting information about our product's market position:
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- Sales data shows strong growth (25% increase)
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- Customer surveys indicate declining satisfaction
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- Competitor analysis shows we're losing market share
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- Internal metrics show improved operational efficiency
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@analyst @researcher @strategist please analyze these conflicting signals and provide
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an integrated assessment of our true market position."""
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print(f"Task: {task}\n")
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print("Expected behavior:")
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print(
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"1. Analyst: Clarifies the data discrepancies and identifies patterns"
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)
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print(
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"2. Researcher: Provides market context to explain the contradictions"
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)
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print(
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"3. Strategist: Synthesizes all perspectives into a coherent market assessment"
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)
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print("\n" + "=" * 80 + "\n")
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response = group_chat.run(task)
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print(f"Collaborative Response:\n{response}")
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def main():
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"""Run all enhanced collaboration examples."""
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print("Enhanced Collaborative InteractiveGroupChat Examples")
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print("=" * 80)
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print("This demonstrates improved agent collaboration with:")
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print("- Acknowledgment of other agents' contributions")
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print("- Building upon previous insights")
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print("- Synthesis of multiple perspectives")
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print("- Appropriate delegation using @mentions")
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print("- Comprehensive understanding of conversation history")
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print("=" * 80 + "\n")
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# Run examples
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example_comprehensive_analysis()
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example_problem_solving()
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example_agent_delegation()
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example_synthesis_and_integration()
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print("\n" + "=" * 80)
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print("All enhanced collaboration examples completed!")
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print("Notice how agents now:")
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print("✓ Acknowledge each other's contributions")
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print("✓ Build upon previous insights")
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print("✓ Synthesize multiple perspectives")
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print("✓ Delegate appropriately")
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print("✓ Provide more cohesive and comprehensive responses")
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if __name__ == "__main__":
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main()
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@ -0,0 +1,13 @@
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from swarms import Agent
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# Initialize a new agent
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agent = Agent(
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model_name="gpt-4o-mini", # Specify the LLM
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max_loops=1, # Set the number of interactions
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interactive=True, # Enable interactive mode for real-time feedback
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streaming_on=True,
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print_on=False,
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)
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# Run the agent with a task
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agent.run("What are the key benefits of using a multi-agent system?")
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@ -0,0 +1,72 @@
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"""
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InteractiveGroupChat Speaker Function Examples
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This example demonstrates how to use different speaker functions in the InteractiveGroupChat:
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- Round Robin: Agents speak in a fixed order, cycling through the list
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- Random: Agents speak in random order
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- Priority: Agents speak based on priority weights
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- Custom: User-defined speaker functions
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The example also shows how agents can mention each other using @agent_name syntax.
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"""
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from swarms import Agent
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from swarms.structs.interactive_groupchat import (
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InteractiveGroupChat,
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random_speaker,
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)
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def create_example_agents():
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"""Create example agents for demonstration."""
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# Create agents with different expertise
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analyst = Agent(
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agent_name="analyst",
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system_prompt="You are a data analyst. You excel at analyzing data, creating charts, and providing insights.",
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model_name="gpt-4.1",
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streaming_on=True,
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print_on=True,
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)
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researcher = Agent(
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agent_name="researcher",
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system_prompt="You are a research specialist. You are great at gathering information, fact-checking, and providing detailed research.",
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model_name="gpt-4.1",
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streaming_on=True,
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print_on=True,
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)
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writer = Agent(
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agent_name="writer",
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system_prompt="You are a content writer. You excel at writing clear, engaging content and summarizing information.",
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model_name="gpt-4.1",
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streaming_on=True,
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print_on=True,
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)
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return [analyst, researcher, writer]
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def example_random():
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agents = create_example_agents()
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# Create group chat with random speaker function
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group_chat = InteractiveGroupChat(
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name="Random Team",
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description="A team that speaks in random order",
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agents=agents,
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speaker_function=random_speaker,
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interactive=False,
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)
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# Test the random behavior
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task = "Let's create a marketing strategy. @analyst @researcher @writer please contribute."
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response = group_chat.run(task)
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print(f"Response:\n{response}\n")
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if __name__ == "__main__":
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# example_round_robin()
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example_random()
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