AI Agent in Axion Framework

The AI Agent is the core component of the Axion Framework, embodying modularity, autonomy, and versatility. Agents handle diverse tasks, integrate with decentralized systems, and collaborate seamlessly with other agents.

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Key Features of the AI Agent

1. Multi-Modal Task Execution

   • Handles text, image, and audio processing efficiently.

2. Knowledge Management

   • Builds, queries, and visualizes a dynamic knowledge graph.

3. Distributed Task Management

   • Leverages Redis-backed task queues for workload distribution.

4. Collaboration Framework

   • Facilitates inter-agent communication and task delegation.

5. Blockchain Integration

   • Interacts with decentralized systems like Ethereum and Solana.

6. IPFS Integration

   • Supports file storage and retrieval on decentralized platforms.

7. Reinforcement Learning

   • Optimizes task execution through adaptive self-learning.

8. Swarm Decision-Making

   • Participates in swarm-level consensus and voting processes.

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How It Works

Each AI Agent is initialized with a unique agent_id and a specific role. The agent interacts with its environment, collaborates with peers, and integrates with decentralized systems to perform tasks efficiently.

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Key Methods and Examples

1. Multi-Modal Task Execution

# Execute a text-based task
agent.execute_text_task("Summarize the document content.")

# Process an image with text prompts
agent.execute_image_task("path/to/image.png", "Describe the scene in detail.")

# Handle an audio input
agent.execute_audio_task("path/to/audio.mp3")

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1. Knowledge Management

# Add a concept to the knowledge graph
agent.add_knowledge("Artificial Intelligence", {"field": "Computer Science"})

# Link concepts
agent.add_knowledge_relationship("Artificial Intelligence", "Machine Learning", "includes")

# Query the graph
knowledge = agent.query_knowledge("Machine Learning")
print(knowledge)

# Visualize the graph
agent.visualize_knowledge_graph("output/knowledge_graph.png")

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1. Distributed Task Queue

# Push a task to the queue
agent.push_task_to_queue("Analyze market trends")

# Pull and process a task
task = agent.pull_task_from_queue()
print(f"Processing task: {task}")

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1. Collaboration Framework

# Send a message to another agent
agent.send_message(recipient_id=2, message="Initiate data preprocessing.")

# Retrieve messages
messages = agent.receive_messages()
for msg in messages:
    print(f"Message from Agent {msg['sender_id']}: {msg['message']}")

# Delegate a task
agent.delegate_task(recipient_id=3, task_description="Train the machine learning model.")

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1. Blockchain Integration

# Check Solana wallet balance
balance = agent.get_sol_balance()
print(f"Solana balance: {balance}")

# Transfer SOL
agent.send_sol(recipient_pubkey="RecipientPubKey", amount=1.5)

# Check Ethereum wallet balance
eth_balance = agent.get_eth_balance("0xRecipientAddress")
print(f"Ethereum balance: {eth_balance}")

# Transfer ETH
agent.send_eth(sender_key="PrivateKey", recipient_address="0xRecipientAddress", amount_ether=0.25)

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1. IPFS Integration

# Upload a file to IPFS
cid = agent.upload_to_ipfs("path/to/file.txt")
print(f"File uploaded to IPFS with CID: {cid}")

# Download a file from IPFS
agent.download_from_ipfs(cid="QmCID", output_path="path/to/downloaded_file.txt")

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1. Self-Optimization with Reinforcement Learning

# Optimize task execution
state = agent.get_environment_state()
reward = agent.execute_action("action_name")
agent.optimize_task_execution(state)

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1. Swarm Decision-Making

# Propose a task to the swarm
agent.propose_task_to_swarm("Conduct AI-powered data analysis")

# Vote on a task
agent.vote_on_task(proposal_id="proposal-123")

# Check if consensus has been reached
consensus = agent.check_consensus()
print(f"Consensus reached: {consensus}")

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Full Workflow Example

from axion.agents.ai_agent import AIAgent

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

# Add knowledge and relationships
agent.add_knowledge("Blockchain", {"field": "Decentralized Systems"})
agent.add_knowledge_relationship("Blockchain", "Ethereum", "example_of")

# Propose a task and vote
agent.propose_task_to_swarm("Optimize transaction speed")
agent.vote_on_task("proposal-456")

# Push and process tasks
agent.push_task_to_queue("Generate market insights")
task = agent.pull_task_from_queue()
print(f"Processing: {task}")

# Send and retrieve messages
agent.send_message(2, "Start analyzing recent trends.")
messages = agent.receive_messages()
for msg in messages:
    print(f"Message: {msg}")

# Integrate with blockchain and IPFS
eth_balance = agent.get_eth_balance("0xYourAddress")
print(f"Ethereum Balance: {eth_balance}")

file_cid = agent.upload_to_ipfs("path/to/document.pdf")
print(f"File stored on IPFS: {file_cid}")

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Best Practices

1. Define Roles Clearly

   • Assign roles to agents based on their strengths and system requirements.

2. Monitor and Audit Tasks

   • Track task progress and ensure proper logging for accountability.

3. Optimize Resource Usage

   • Balance workloads and prevent overloading individual agents.

4. Leverage Swarm Collaboration

   • Use swarm decision-making for complex tasks requiring consensus.