# 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.

***


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://aether-framework.gitbook.io/aetherframework/multi-agent-collaboration.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.