Multi-Agent Collaboration in SynaptiQ Systems

Multi-Agent Collaboration in SynaptiQ Systems

Agents in SynaptiQ Systems can dynamically interact to share knowledge, delegate tasks, and collaborate on complex goals. This functionality is crucial for large-scale, distributed systems where coordination between agents is key.

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Key Features

• Messaging: Agents exchange messages to share insights or instructions.

• Task Delegation: Agents assign tasks to one another based on their roles and capabilities.

• Distributed Task Queues: Use Redis to manage task distribution across multiple agents.

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Example Workflows

1. Task Delegation

pythonCopy codefrom src.utils.agent_collaboration import CollaborationFramework

# Initialize Collaboration Framework
collaboration = CollaborationFramework()

# Delegate a task
collaboration.delegate_task(
    sender_id=1,
    recipient_id=2,
    task_description="Analyze IPFS data and generate a report"
)

2. Messaging

pythonCopy code# Send a message
collaboration.send_message(sender_id=1, recipient_id=2, message="Start processing task.")

# Receive messages
messages = collaboration.receive_message(recipient_id=2)
for msg in messages:
    print(f"Received message: {msg['message']}")

3. Distributed Task Queue

pythonCopy codefrom src.utils.redis_task_queue import RedisTaskQueue

# Initialize Redis Task Queue
redis_queue = RedisTaskQueue()

# Push a task to the queue
redis_queue.push_task({
    "agent_id": 1,
    "task_description": "Perform sentiment analysis on dataset."
})

# Pop a task from the queue
task = redis_queue.pop_task()
print(f"Task popped from queue: {task}")

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Best Practices

• Role-Based Delegation: Assign tasks to agents best suited to handle them based on their roles (e.g., "worker", "manager").

• Message Logging: Maintain logs of all exchanged messages for debugging and tracking purposes.

• Scalability: Use the distributed task queue for scaling collaboration in larger swarms.