# 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.

***

#### **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.

***

#### **Example: Task Delegation**

```python
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"
)
```

***

#### **Example: Messaging**

```python
# 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']}")
```

***

#### **Example: Distributed Task Queue**

```python
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}")
```

***

#### **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.

***

