Merge pull request #866 from harshalmore31/improve/conversation_integration
Feature: Enhanced Conversation Support with Multi-Database Integrationpull/882/head
Kye Gomez
3 weeks ago
committed by
GitHub
commit
2a00911430
@ -0,0 +1,415 @@
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"""
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Swarms Conversation Supabase Backend Example
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This example demonstrates using Supabase as the conversation storage backend
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with real Swarms agents. It shows how to:
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1. Set up agents with different roles and capabilities
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2. Use Supabase for persistent conversation storage
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3. Aggregate multi-agent responses
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4. Handle real-world agent workflows
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Prerequisites:
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- Supabase project with SUPABASE_URL and SUPABASE_ANON_KEY environment variables
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- LLM API keys (OpenAI, Anthropic, etc.)
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- pip install supabase swarms
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"""
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import os
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from typing import List, Callable
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from swarms.structs.agent import Agent
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from swarms.structs.conversation import Conversation
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from swarms.structs.multi_agent_exec import run_agents_concurrently
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from swarms.utils.history_output_formatter import (
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history_output_formatter,
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HistoryOutputType,
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)
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def aggregator_agent_task_prompt(
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task: str, workers: List[Agent], conversation: Conversation
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):
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"""Create a comprehensive prompt for the aggregator agent."""
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return f"""
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As an expert analysis aggregator, please synthesize the following multi-agent conversation
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into a comprehensive and actionable report.
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Original Task: {task}
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Number of Participating Agents: {len(workers)}
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Agent Roles: {', '.join([agent.agent_name for agent in workers])}
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Conversation Content:
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{conversation.get_str()}
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Please provide a detailed synthesis that includes:
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1. Executive Summary
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2. Key Insights from each agent
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3. Conflicting viewpoints (if any)
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4. Recommended actions
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5. Next steps
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Format your response as a professional report.
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"""
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def create_research_agents() -> List[Agent]:
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"""Create a team of specialized research agents."""
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# Data Analyst Agent
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data_analyst = Agent(
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agent_name="DataAnalyst",
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agent_description="Expert in data analysis, statistics, and market research",
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system_prompt="""You are a senior data analyst with expertise in:
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- Statistical analysis and data interpretation
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- Market research and trend analysis
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- Data visualization insights
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- Quantitative research methods
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Provide data-driven insights with specific metrics, trends, and statistical evidence.
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Always cite data sources and provide confidence levels for your analysis.""",
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model_name="gpt-4o-mini",
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max_loops=1,
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verbose=True,
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output_type="string",
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)
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# Research Specialist Agent
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researcher = Agent(
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agent_name="ResearchSpecialist",
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agent_description="Expert in academic research, industry analysis, and information synthesis",
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system_prompt="""You are a research specialist with expertise in:
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- Academic research and literature review
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- Industry analysis and competitive intelligence
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- Information synthesis and validation
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- Research methodology and best practices
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Provide well-researched, evidence-based insights with proper citations.
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Focus on credible sources and peer-reviewed information when possible.""",
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model_name="gpt-4o-mini",
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max_loops=1,
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verbose=True,
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output_type="string",
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)
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# Strategic Advisor Agent
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strategist = Agent(
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agent_name="StrategicAdvisor",
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agent_description="Expert in strategic planning, business strategy, and decision-making",
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system_prompt="""You are a strategic advisor with expertise in:
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- Strategic planning and business strategy
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- Risk assessment and mitigation
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- Decision-making frameworks
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- Competitive analysis and positioning
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Provide strategic recommendations with clear rationale.
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Focus on actionable insights and long-term implications.""",
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model_name="gpt-4o-mini",
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max_loops=1,
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verbose=True,
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output_type="string",
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)
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return [data_analyst, researcher, strategist]
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def create_aggregator_agent() -> Agent:
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"""Create an aggregator agent to synthesize multi-agent responses."""
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return Agent(
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agent_name="SynthesisAggregator",
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agent_description="Expert in analyzing and synthesizing multi-agent conversations into comprehensive reports",
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system_prompt="""You are an expert synthesis aggregator specializing in:
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- Multi-perspective analysis and integration
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- Comprehensive report generation
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- Conflict resolution and consensus building
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- Strategic insight extraction
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Your role is to analyze conversations between multiple expert agents and create
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a unified, actionable report that captures the best insights from all participants.
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Always structure your reports professionally with:
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- Executive Summary
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- Detailed Analysis
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- Key Recommendations
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- Implementation Steps""",
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model_name="gpt-4o",
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max_loops=1,
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verbose=True,
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output_type="string",
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)
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def aggregate_with_supabase(
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workers: List[Agent],
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task: str = None,
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type: HistoryOutputType = "all",
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aggregator_model_name: str = "gpt-4o",
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# Backend parameters for conversation storage
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backend: str = "supabase",
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supabase_url: str = None,
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supabase_key: str = None,
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):
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"""
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Aggregate agent responses using Supabase for conversation storage.
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Args:
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workers: List of Agent instances
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task: The task to execute
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type: Output type for history formatting
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aggregator_model_name: Model name for the aggregator agent
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backend: Storage backend (default: "supabase")
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supabase_url: Supabase project URL
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supabase_key: Supabase API key
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"""
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if task is None:
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raise ValueError("Task is required for agent aggregation")
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if not workers:
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raise ValueError("At least one worker agent is required")
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if not all(isinstance(worker, Agent) for worker in workers):
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raise ValueError("All workers must be Agent instances")
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# Set up Supabase conversation storage
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conversation_kwargs = {}
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if backend == "supabase":
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url = supabase_url or os.getenv("SUPABASE_URL")
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key = supabase_key or os.getenv("SUPABASE_ANON_KEY")
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if not url or not key:
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raise ValueError(
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"Supabase backend requires SUPABASE_URL and SUPABASE_ANON_KEY "
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"environment variables or explicit parameters"
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)
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conversation_kwargs.update({
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"supabase_url": url,
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"supabase_key": key,
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})
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try:
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# Create conversation with Supabase backend
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conversation = Conversation(
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backend=backend,
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**conversation_kwargs,
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system_prompt="Multi-agent collaboration session with persistent storage",
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time_enabled=True,
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)
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print(f"✅ Successfully initialized {backend} backend for conversation storage")
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# Add initial task to conversation
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conversation.add("system", f"Task: {task}")
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except ImportError as e:
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print(f"❌ Backend initialization failed: {e}")
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print(f"💡 Falling back to in-memory storage")
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conversation = Conversation(backend="in-memory")
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# Create aggregator agent
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aggregator_agent = create_aggregator_agent()
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print(f"🚀 Starting multi-agent execution with {len(workers)} agents...")
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# Run agents concurrently
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results = run_agents_concurrently(agents=workers, task=task)
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# Store individual agent responses in conversation
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for result, agent in zip(results, workers):
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conversation.add(content=result, role=agent.agent_name)
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print(f"📝 Stored response from {agent.agent_name}")
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print("🔄 Running aggregation analysis...")
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# Generate aggregated analysis
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final_result = aggregator_agent.run(
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task=aggregator_agent_task_prompt(task, workers, conversation)
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)
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# Store aggregated result
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conversation.add(
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content=final_result,
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role=aggregator_agent.agent_name
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)
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print("✅ Aggregation complete!")
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# Return formatted history
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return history_output_formatter(
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conversation=conversation, type=type
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)
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# Example usage with real Swarms agents
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if __name__ == "__main__":
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print("🧪 Testing Swarms Multi-Agent System with Supabase Backend")
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print("="*70)
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# Check environment setup
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print("\n⚙️ Environment Setup Check")
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print("-" * 40)
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supabase_url = os.getenv("SUPABASE_URL")
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supabase_key = os.getenv("SUPABASE_ANON_KEY")
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openai_key = os.getenv("OPENAI_API_KEY")
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print(f"SUPABASE_URL: {'✅ Set' if supabase_url else '❌ Not set'}")
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print(f"SUPABASE_ANON_KEY: {'✅ Set' if supabase_key else '❌ Not set'}")
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print(f"OPENAI_API_KEY: {'✅ Set' if openai_key else '❌ Not set'}")
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if not (supabase_url and supabase_key):
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print("\n⚠️ Missing Supabase configuration!")
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print("Please set the following environment variables:")
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print("export SUPABASE_URL=https://your-project.supabase.co")
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print("export SUPABASE_ANON_KEY=your-anon-key")
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print("\nFalling back to demonstration with mock data...")
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if not openai_key:
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print("\n⚠️ Missing OpenAI API key!")
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print("Please set: export OPENAI_API_KEY=your-api-key")
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print("You can also use other LLM providers (Anthropic, Google, etc.)")
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# Example 1: Basic Multi-Agent Research Task
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print("\n📦 Example 1: Multi-Agent Market Research")
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print("-" * 50)
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try:
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# Create research team
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research_team = create_research_agents()
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# Define research task
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research_task = """
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Analyze the current state and future prospects of artificial intelligence
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in healthcare. Consider market trends, technological developments,
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regulatory challenges, and investment opportunities. Provide insights
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on key players, emerging technologies, and potential risks.
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"""
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print(f"📋 Task: {research_task.strip()}")
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print(f"👥 Team: {[agent.agent_name for agent in research_team]}")
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if supabase_url and supabase_key and openai_key:
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# Run with real agents and Supabase storage
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result = aggregate_with_supabase(
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workers=research_team,
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task=research_task,
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type="final",
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backend="supabase",
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supabase_url=supabase_url,
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supabase_key=supabase_key,
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)
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print("\n📊 Research Results:")
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print("=" * 50)
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print(result)
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else:
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print("❌ Skipping real agent execution due to missing configuration")
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except Exception as e:
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print(f"❌ Error in multi-agent research: {e}")
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# Example 2: Simple Conversation Storage Test
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print("\n📦 Example 2: Direct Conversation Storage Test")
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print("-" * 50)
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try:
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if supabase_url and supabase_key:
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# Test direct conversation with Supabase
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conv = Conversation(
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backend="supabase",
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supabase_url=supabase_url,
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supabase_key=supabase_key,
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time_enabled=True,
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)
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print("✅ Supabase conversation created successfully")
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# Add sample conversation
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conv.add("user", "What are the latest trends in AI technology?")
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conv.add("assistant", "Based on current developments, key AI trends include:")
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conv.add("assistant", "1. Large Language Models (LLMs) advancing rapidly")
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conv.add("assistant", "2. Multimodal AI combining text, image, and video")
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conv.add("assistant", "3. AI agents becoming more autonomous and capable")
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conv.add("user", "How do these trends affect businesses?")
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conv.add("assistant", "These trends are transforming businesses through automation, enhanced decision-making, and new product capabilities.")
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# Test conversation operations
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print(f"📊 Message count: {len(conv.to_dict())}")
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print(f"🔍 Search results for 'AI': {len(conv.search('AI'))}")
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print(f"📈 Role distribution: {conv.count_messages_by_role()}")
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# Export conversation
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conv.export_conversation("supabase_ai_conversation.json")
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print("💾 Conversation exported to supabase_ai_conversation.json")
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else:
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print("❌ Skipping Supabase test due to missing configuration")
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except Exception as e:
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print(f"❌ Error in conversation storage test: {e}")
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# Example 3: Agent Creation and Configuration Demo
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print("\n📦 Example 3: Agent Configuration Demo")
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print("-" * 50)
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try:
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if openai_key:
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# Create a simple agent for demonstration
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demo_agent = Agent(
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agent_name="DemoAnalyst",
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agent_description="Demonstration agent for testing",
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system_prompt="You are a helpful AI assistant specializing in analysis and insights.",
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model_name="gpt-4o-mini",
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max_loops=1,
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verbose=False,
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)
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print("✅ Demo agent created successfully")
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print(f"Agent: {demo_agent.agent_name}")
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print(f"Description: {demo_agent.agent_description}")
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# Test simple agent run
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simple_task = "Explain the benefits of using persistent conversation storage in AI applications."
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response = demo_agent.run(simple_task)
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print(f"\n📝 Agent Response:")
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print("-" * 30)
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print(response[:500] + "..." if len(response) > 500 else response)
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else:
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print("❌ Skipping agent demo due to missing OpenAI API key")
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except Exception as e:
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print(f"❌ Error in agent demo: {e}")
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# Summary and Next Steps
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print("\n" + "="*70)
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print("🏁 Demo Summary")
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print("="*70)
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print("\n✨ What was demonstrated:")
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print("1. 🏗️ Real Swarms agent creation with specialized roles")
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print("2. 🗄️ Supabase backend integration for persistent storage")
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print("3. 🤝 Multi-agent collaboration and response aggregation")
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print("4. 💾 Conversation export and search capabilities")
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print("5. ⚙️ Proper error handling and graceful fallbacks")
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print("\n🚀 Next Steps to get started:")
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print("1. Set up Supabase project: https://supabase.com")
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print("2. Configure environment variables")
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print("3. Install dependencies: pip install swarms supabase")
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print("4. Customize agents for your specific use cases")
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print("5. Scale to larger agent teams and complex workflows")
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print("\n🔗 Resources:")
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print("- Swarms Documentation: https://docs.swarms.world")
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print("- Supabase Python Docs: https://supabase.com/docs/reference/python/")
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print("- GitHub Repository: https://github.com/kyegomez/swarms")
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print(f"\n⚙️ Final Configuration Status:")
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print(f" SUPABASE_URL: {'✅ Set' if supabase_url else '❌ Not set'}")
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print(f" SUPABASE_ANON_KEY: {'✅ Set' if supabase_key else '❌ Not set'}")
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print(f" OPENAI_API_KEY: {'✅ Set' if openai_key else '❌ Not set'}")
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if supabase_url and supabase_key and openai_key:
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print("\n🎉 All systems ready! You can run the full demo.")
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else:
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print("\n⚠️ Set missing environment variables to run the full demo.")
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