Multi-Agent Collaboration

Agents in the Aether Framework 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

1. Messaging Agents exchange messages to share insights or instructions.

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

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

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Example: Task Delegation

from 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"
)

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Example: Messaging

# 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']}")

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Example: Distributed Task Queue

from 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

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

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

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

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