> For the complete documentation index, see [llms.txt](https://aether-framework.gitbook.io/aetherframework/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aether-framework.gitbook.io/aetherframework/ai-agent.md).

# AI Agent

The **AI Agent** is the core component of the Aether Framework. Each agent is modular, autonomous, and capable of handling various tasks, integrating with decentralized systems, and collaborating with other agents.

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#### **Features**

1. **Multi-Modal Task Execution**: Support for text, image, and audio processing.
2. **Knowledge Management**: Build, query, and visualize a knowledge graph.
3. **Distributed Task Management**: Use Redis-backed task queues to manage workloads.
4. **Collaboration Framework**: Enable inter-agent communication and task delegation.
5. **Blockchain Integration**: Interact with Solana and Ethereum for decentralized transactions and logging.
6. **IPFS Integration**: Store and retrieve files using decentralized storage.
7. **Reinforcement Learning**: Optimize task execution through self-learning.
8. **Swarm Decision-Making**: Participate in swarm-level consensus and voting.

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#### **How It Works**

Each AI Agent is initialized with a unique **agent\_id** and a specific **role**. Agents can interact with their environment, other agents, or decentralized systems to complete tasks effectively.

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#### **Key Methods**

**1. Multi-Modal Task Execution**

* `execute_text_task(task_description)`: Processes text-based tasks.
* `execute_image_task(image_path, text_prompts)`: Handles image-related tasks.
* `execute_audio_task(audio_path)`: Processes audio inputs.

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**2. Knowledge Management**

* `add_knowledge(concept, attributes)`: Adds a concept to the knowledge graph.
* `add_knowledge_relationship(concept1, concept2, relationship_type)`: Links concepts.
* `query_knowledge(concept)`: Queries the knowledge graph.
* `visualize_knowledge_graph(output_path)`: Visualizes the graph.

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**3. Distributed Task Queue**

* `push_task_to_queue(task_description)`: Adds a task to the distributed queue.
* `pull_task_from_queue()`: Pulls and processes a task from the queue.

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**4. Collaboration Framework**

* `send_message(recipient_id, message)`: Sends a message to another agent.
* `receive_messages()`: Retrieves messages for the agent.
* `delegate_task(recipient_id, task_description)`: Delegates a task to another agent.

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**5. Blockchain Integration**

* `get_sol_balance()`: Retrieves the agent’s Solana wallet balance.
* `send_sol(recipient_pubkey, amount)`: Sends SOL to a recipient.
* `get_eth_balance(address)`: Checks an Ethereum wallet’s balance.
* `send_eth(sender_key, recipient_address, amount_ether)`: Transfers ETH.

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**6. IPFS Integration**

* `upload_to_ipfs(file_path)`: Uploads a file to IPFS.
* `download_from_ipfs(cid, output_path)`: Retrieves a file using its IPFS hash.

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**7. Self-Optimization (Reinforcement Learning)**

* `optimize_task_execution(state)`: Optimizes task execution based on rewards.
* `execute_action(action)`: Executes a specific action and returns a reward.
* `get_environment_state()`: Retrieves the agent’s current state.

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**8. Swarm Decision-Making**

* `propose_task_to_swarm(task_description)`: Proposes a task for swarm consensus.
* `vote_on_task(proposal_id)`: Votes on a proposed task.
* `check_consensus()`: Checks if consensus has been reached.

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#### **Example Code**

Here’s an example of setting up and using an AI Agent:

```python
from src.agents.ai_agent import AIAgent

# Initialize an AI Agent
agent = AIAgent(agent_id=1, role="data manager", provider="openai", base_url="https://api.openai.com")

# Add knowledge to the graph
agent.add_knowledge("Artificial Intelligence", {"field": "Computer Science"})
agent.add_knowledge_relationship("Artificial Intelligence", "Machine Learning", "includes")

# Propose a task to the swarm
agent.propose_task_to_swarm("Analyze market trends")

# Optimize task execution
state = agent.get_environment_state()
agent.optimize_task_execution(state)

# Send a message to another agent
agent.send_message(recipient_id=2, message="Please assist with data preprocessing.")
```