Agent Swarms (🦠)

Agent swarms in HeatMap are coordinated teams of AI agents that work together to analyze, track, and trade meme coins.

Core Principle

Agent swarms combine specialized expertise to provide comprehensive market analysis and trading execution. Each agent focuses on specific aspects while collaborating to generate high-quality trading signals.

Research Swarm Example

Here's an example of a research-focused agent swarm:

import heatmap as hm

# Research agents
research_agent = hm.Agent(
    name="Research Agent",
    instructions="""
    Analyze token fundamentals and provide detailed reports on:
    - Contract security
    - Holder distribution
    - Trading patterns
    - Liquidity metrics
    """,
    tools=[contract_analyzer, holder_tracker, volume_analyzer],
)

social_agent = hm.Agent(
    name="Social Agent",
    instructions="""
    Monitor social signals and community sentiment:
    - Track viral content
    - Analyze community growth
    - Monitor influencer activity
    """,
    tools=[social_tracker, sentiment_analyzer, trend_detector],
)

trading_agent = hm.Agent(
    name="Trading Agent",
    instructions="""
    Execute trades based on analysis and signals:
    - Validate trading signals
    - Manage risk parameters
    - Place and monitor orders
    """,
    tools=[order_manager, risk_controller, position_tracker],
)

Result Types

class ResearchResult(BaseModel):
    report: str
    confidence: float
    signals: List[Signal]

class TradeDecision(BaseModel):
    action: str  # buy/sell
    size: float
    entry_price: float
    stop_loss: float

class TradeExecution(BaseModel):
    success: bool
    order_id: str
    details: Dict[str, Any]

Best Practices

When working with agent swarms:

Define clear agent responsibilities
Configure proper communication channels
Set up error handling and recovery
Monitor swarm performance
Maintain audit trails

Next Steps

Learn about Trading Strategies
Setup Risk Management
Configure Notifications