Example: Multi-Agent Swarm Coordination

JuliaOS provides advanced capabilities for coordinating multiple agents in a swarm. This example demonstrates how to create a swarm of agents that collaborate to solve a complex task.

# Start the CLI
./scripts/run-cli.sh  # or node packages/cli/interactive.cjs

# Select "🧬 Swarm Intelligence" from the main menu
# Choose "Create Swarm"
# Enter a name for your swarm (e.g., "CoordinationSwarm")
# Select an algorithm (e.g., "PSO" for Particle Swarm Optimization)
# Enter swarm configuration as JSON (e.g., {"max_iterations": 100})

# Add agents to the swarm
# Select "👤 Agent Management" from the main menu
# Create several agents with different specializations
# For each agent, select "Add to Swarm" and choose your swarm

# Start the swarm
# Select "🧬 Swarm Intelligence" from the main menu
# Choose "Manage Swarms"
# Select your swarm and choose "Start Swarm"

# Allocate tasks to the swarm
# Select "Task Management" from the swarm menu
# Create a new task with parameters
# The swarm will automatically allocate the task to the most suitable agent

You can also use the Python wrapper to coordinate a swarm of agents:

import asyncio
from juliaos import JuliaOS

async def coordinate_swarm():
    # Initialize JuliaOS
    juliaos_client = JuliaOS(host="localhost", port=8052)
    await juliaos_client.connect()

    # Create a swarm
    swarm = await juliaos_client.swarms.create_swarm(
        name="CoordinationSwarm",
        algorithm="PSO",
        config={
            "max_iterations": 100,
            "coordination_mode": "hierarchical"
        }
    )
    swarm_id = swarm["id"]

    # Create agents with different specializations
    agents = []
    for i in range(5):
        agent = await juliaos_client.agents.create_agent(
            name=f"Agent-{i+1}",
            agent_type="specialized",
            config={
                "specialization": ["explorer", "analyzer", "optimizer", "executor", "monitor"][i % 5],
                "skill_level": 3 + i % 3
            }
        )
        agents.append(agent)

        # Add agent to swarm
        await juliaos_client.swarms.add_agent_to_swarm(swarm_id, agent["id"])

    # Start the swarm
    await juliaos_client.swarms.start_swarm(swarm_id)

    # Create a shared state for the swarm
    await juliaos_client.swarms.update_shared_state(swarm_id, "target_position", [10.0, 20.0, 30.0])
    await juliaos_client.swarms.update_shared_state(swarm_id, "obstacles", [[5.0, 5.0, 5.0], [15.0, 15.0, 15.0]])

    # Allocate a task to the swarm
    task = await juliaos_client.swarms.allocate_task(
        swarm_id=swarm_id,
        task={
            "type": "exploration",
            "parameters": {
                "area": [0, 0, 0, 100, 100, 100],
                "resolution": 5.0,
                "priority": "high"
            }
        }
    )

    # Monitor task progress
    for _ in range(10):
        await asyncio.sleep(1)
        status = await juliaos_client.swarms.get_task_status(swarm_id, task["task_id"])
        print(f"Task status: {status['status']}")
        print(f"Progress: {status['progress']}%")

        # Get swarm metrics
        metrics = await juliaos_client.swarms.get_swarm_metrics(swarm_id)
        print(f"Active agents: {metrics['active_agents']}")
        print(f"Completed subtasks: {metrics['completed_subtasks']}")

    # Elect a leader for the swarm
    leader = await juliaos_client.swarms.elect_leader(swarm_id)
    print(f"Elected leader: {leader['leader_id']}")

    # Stop the swarm
    await juliaos_client.swarms.stop_swarm(swarm_id)

    await juliaos_client.disconnect()

# Run the coordination example
asyncio.run(coordinate_swarm())

The swarm coordination features include:

• Multi-agent collaboration with different agent specializations

• Task allocation based on agent capabilities and current load

• Shared state management for coordination and information sharing

• Leader election for hierarchical coordination

• Fault tolerance with automatic recovery from agent failures

• Dynamic membership with agents joining and leaving the swarm

• Communication patterns for efficient information exchange

• Security features for authentication and authorization

• Metrics collection for monitoring swarm performance