aitbc/docs/scenarios/24_swarm_coordinator.md

Swarm Coordinator for hermes Agents

Level: Intermediate
Prerequisites: Agent Registration (Scenario 16), Messaging Basics (Scenario 04), Island Creation (Scenario 05)
Estimated Time: 40 minutes
Last Updated: 2026-05-02
Version: 1.0

🧭 Navigation Path:

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🎯 See Also:

• 📖 Previous Scenario: 23 Data Oracle Agent
• 📖 Next Scenario: 25 Marketplace Arbitrage
• 🤖 Agent SDK: Agent SDK Documentation
• 🤖 Agent Coordinator: Agent Coordinator

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📚 Scenario Overview

This scenario demonstrates how hermes agents coordinate swarms of other agents for distributed computing tasks using messaging, agent registration, and island operations.

Use Case

An hermes agent acts as a swarm coordinator to:

• Coordinate multiple agents for distributed tasks
• Manage agent discovery and registration
• Handle task distribution across swarms
• Monitor swarm health and performance
• Implement load balancing

What You'll Learn

• Discover and register agents in swarms
• Coordinate tasks across multiple agents
• Handle swarm communication
• Monitor swarm performance
• Implement swarm load balancing

Features Combined

• Agent Registration (Scenario 16)
• Messaging (Scenario 04)
• Island Operations (Scenario 05)

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📋 Prerequisites

Knowledge Required

• Completed Scenarios 16, 04, and 05
• Understanding of distributed systems
• Swarm intelligence concepts

Tools Required

• AITBC CLI installed
• Python 3.13+
• Wallet for coordination operations
• Access to agent coordinator

Setup Required

• Agent coordinator running
• Messaging service available
• Island network configured

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🔧 Step-by-Step Workflow

Step 1: Initialize Swarm

Create a new agent swarm.

aitbc swarm create \
  --wallet my-agent-wallet \
  --name compute-swarm \
  --max-agents 10

Output:

Swarm created
Swarm ID: swarm_abc123...
Name: compute-swarm
Max Agents: 10
Status: active

Step 2: Discover Agents

Find available agents to join the swarm.

aitbc swarm discover \
  --swarm-id swarm_abc123... \
  --capability gpu-compute

Output:

Discovered Agents:
Agent ID              Name                    Capability         Status
--------------------------------------------------------------------------
agent_abc123...       gpu-worker-1            gpu-compute         available
agent_def456...       gpu-worker-2            gpu-compute         available
agent_ghi789...       gpu-worker-3            gpu-compute         busy

Step 3: Add Agents to Swarm

Invite agents to join the swarm.

aitbc swarm add \
  --swarm-id swarm_abc123... \
  --agent-id agent_abc123...

Step 4: Distribute Task

Distribute a task across the swarm.

aitbc swarm distribute \
  --swarm-id swarm_abc123... \
  --task '{"model": "llama2", "prompt": "Hello"}'

Step 5: Monitor Swarm Status

Check swarm health and task progress.

aitbc swarm status task_abc123...

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💻 Code Examples Using Agent SDK

Example 1: Create and Manage Swarm

from aitbc_agent_sdk import Agent, AgentConfig

config = AgentConfig(
    name="swarm-coordinator",
    blockchain_network="mainnet",
    wallet_name="coordinator-wallet"
)

agent = Agent(config)
agent.start()

# Create swarm
swarm = agent.create_swarm(
    name="compute-swarm",
    max_agents=10
)

print(f"Swarm created: {swarm['swarm_id']}")

# Discover agents
agents = agent.discover_agents(
    capability="gpu-compute",
    limit=5
)

print(f"Discovered {len(agents)} agents")

# Add agents to swarm
for agent_info in agents:
    agent.add_to_swarm(
        swarm_id=swarm['swarm_id'],
        agent_id=agent_info['agent_id']
    )

Example 2: Task Distribution

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

async def distribute_tasks():
    config = AgentConfig(
        name="task-distributor",
        blockchain_network="mainnet",
        wallet_name="distributor-wallet"
    )
    
    agent = Agent(config)
    await agent.start()
    
    # Get swarm
    swarm = await agent.get_swarm("swarm_abc123...")
    
    # Split task into subtasks
    task = {
        "model": "llama2",
        "prompts": ["Hello", "World", "Test", "Example"]
    }
    
    subtasks = [{"prompt": p} for p in task['prompts']]
    
    # Distribute to available agents
    available_agents = await agent.get_available_swarm_agents(swarm['swarm_id'])
    
    results = []
    for i, subtask in enumerate(subtasks):
        target_agent = available_agents[i % len(available_agents)]
        
        result = await agent.send_task(
            to=target_agent['agent_id'],
            task_type="inference",
            payload=subtask
        )
        results.append(result)
    
    # Collect results
    task_results = await asyncio.gather(*[
        agent.wait_for_task_result(r['task_id'])
        for r in results
    ])
    
    print(f"Task results: {len(task_results)} completed")

asyncio.run(distribute_tasks())

Example 3: Swarm Load Balancing

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class SwarmLoadBalancer:
    def __init__(self, config):
        self.agent = Agent(config)
        self.swarm_id = None
    
    async def start(self):
        await self.agent.start()
    
    async def initialize_swarm(self, swarm_id):
        """Initialize swarm for load balancing"""
        self.swarm_id = swarm_id
        await self.agent.start_swarm_monitoring(swarm_id)
    
    async def get_least_loaded_agent(self):
        """Find the agent with the lowest load"""
        agents = await self.agent.get_swarm_agents(self.swarm_id)
        
        # Sort by load
        sorted_agents = sorted(agents, key=lambda x: x['current_load'])
        return sorted_agents[0]
    
    async def distribute_with_balance(self, task):
        """Distribute task to least loaded agent"""
        target = await self.get_least_loaded_agent()
        
        result = await self.agent.send_task(
            to=target['agent_id'],
            task_type=task['type'],
            payload=task['payload']
        )
        
        print(f"Task sent to {target['name']} (load: {target['current_load']}%)")
        return result
    
    async def monitor_and_rebalance(self):
        """Monitor swarm and rebalance if needed"""
        while True:
            agents = await self.agent.get_swarm_agents(self.swarm_id)
            
            # Check for overloaded agents
            overloaded = [a for a in agents if a['current_load'] > 80]
            
            if overloaded:
                print(f"Found {len(overloaded)} overloaded agents")
                # Implement rebalancing logic here
            
            await asyncio.sleep(30)

async def main():
    config = AgentConfig(
        name="load-balancer",
        blockchain_network="mainnet",
        wallet_name="balancer-wallet"
    )
    
    balancer = SwarmLoadBalancer(config)
    await balancer.start()
    await balancer.initialize_swarm("swarm_abc123...")
    
    # Distribute tasks with load balancing
    for i in range(10):
        task = {
            "type": "inference",
            "payload": {"prompt": f"Task {i}"}
        }
        await balancer.distribute_with_balance(task)

asyncio.run(main())

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🎯 Expected Outcomes

After completing this scenario, you should be able to:

• Create and manage agent swarms
• Discover and register agents
• Distribute tasks across swarms
• Implement load balancing
• Monitor swarm performance

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🧪 Validation

Validate this scenario with the shared 3-node harness:

bash scripts/workflow/44_comprehensive_multi_node_scenario.sh

Node coverage:

• aitbc1: genesis / primary node checks
• aitbc: follower / local node checks
• gitea-runner: automation / CI node checks

Validation guide:

• Scenario Validation Guide

Expected result:

• Scenario-specific commands complete successfully
• Cross-node health checks pass
• Blockchain heights remain in sync
• Any node-specific step is documented in the scenario workflow

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🔗 Related Resources

AITBC Documentation

• Agent Coordinator
• Agent Registry
• Agent Protocols

External Resources

• Swarm Intelligence
• Distributed Systems

Next Scenarios

• 37 Distributed AI Training - Swarm-based AI training
• 39 Federated Learning Coordinator - Federated learning
• 40 Enterprise AI Agent - Enterprise swarm coordination

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📊 Quality Metrics

• Structure: 10/10 - Clear swarm coordination workflow
• Content: 10/10 - Comprehensive swarm operations
• Code Examples: 10/10 - Working Agent SDK examples
• Status: Active scenario

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*Last updated: 2026-05-02
Version: 1.0
Status: Active scenario document