Examples

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Overview

The Aether Framework offers a wide range of use cases to demonstrate its capabilities. These examples illustrate how to leverage the framework's core features, including swarm intelligence, task management, blockchain integration, IPFS storage, and more.

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1. Running a Swarm Simulation

Simulate a swarm of agents and observe their behavior over multiple iterations.

from src.swarm.advanced_swarm_behavior import Swarm

# Initialize a swarm with 10 agents
swarm = Swarm(10)

# Simulate the swarm for 5 iterations
swarm.simulate(5)

What Happens:

• Each agent executes tasks, communicates with others, and optimizes its role.

• The swarm reaches consensus on tasks and adapts dynamically to failures.

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2. Task Scheduling

Dynamically assign tasks to agents based on priority and availability.

from src.utils.task_scheduler import TaskScheduler
from src.swarm.advanced_swarm_behavior import Swarm

# Initialize a swarm and task scheduler
swarm = Swarm(10)
scheduler = TaskScheduler()

# Add tasks to the scheduler
scheduler.add_task(1, "Analyze market trends", priority=5)
scheduler.add_task(2, "Generate AI model", priority=8)

# Assign tasks to swarm agents
scheduler.assign_task(swarm.nodes)

What Happens:

• Tasks are distributed among the swarm based on priority.

• Agents with higher availability take on higher-priority tasks.

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3. Using IPFS for Decentralized Storage

Store and retrieve files securely on IPFS for decentralized collaboration.

from src.utils.ipfs_client import IPFSClient

# Initialize the IPFS client
ipfs_client = IPFSClient()

# Upload a file to IPFS
cid = ipfs_client.upload_file("data/report.pdf")
print(f"File uploaded to IPFS with CID: {cid}")

# Retrieve the file from IPFS
ipfs_client.retrieve_file(cid, output_path="retrieved_report.pdf")
print(f"File retrieved from IPFS and saved to: retrieved_report.pdf")

What Happens:

• The file is uploaded to the decentralized IPFS network and assigned a unique CID.

• The file can be retrieved globally using its CID.

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4. Blockchain Task Logging

Log tasks and results on a blockchain for secure, transparent tracking.

from src.utils.blockchain_manager import BlockchainManager

# Initialize the blockchain manager
blockchain = BlockchainManager()

# Log a task result on-chain
transaction_hash = blockchain.log_task(
    sender_keypair="path/to/solana_keypair.json",
    task_description="Analyze energy consumption data",
    task_result="Task completed successfully"
)
print(f"Task logged on blockchain. Transaction hash: {transaction_hash}")

What Happens:

• Task details are securely logged on-chain.

• The transaction can be verified on the blockchain network.

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5. Agent Collaboration

Enable agents to delegate tasks and share knowledge in real time.

from src.ai.ai_agent import AIAgent

# Initialize two agents
agent1 = AIAgent(agent_id=1, role="coordinator", provider="openai", base_url="https://api.openai.com")
agent2 = AIAgent(agent_id=2, role="worker", provider="anthropic", base_url="https://api.anthropic.com")

# Delegate a task from Agent 1 to Agent 2
agent1.delegate_task(recipient_id=2, task_description="Process financial data")

# Agent 2 receives and executes the task
agent2.process_next_task()

What Happens:

• Agents collaborate and share tasks based on their roles and capabilities.

• Delegation allows for efficient resource utilization across the swarm.

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6. Knowledge Graph Queries

Query structured data from the knowledge graph to make informed decisions.

from src.utils.knowledge_graph import KnowledgeGraph

# Initialize the knowledge graph
knowledge_graph = KnowledgeGraph()

# Add concepts and relationships
knowledge_graph.add_concept("AI Agent", {"role": "worker", "status": "active"})
knowledge_graph.add_relationship("AI Agent", "Swarm", "belongs_to")

# Query the knowledge graph
attributes = knowledge_graph.query_concept("AI Agent")
relationships = knowledge_graph.query_relationships("AI Agent")
print(f"Attributes of AI Agent: {attributes}")
print(f"Relationships of AI Agent: {relationships}")

# Visualize the knowledge graph
knowledge_graph.visualize_graph(output_path="knowledge_graph.png")

What Happens:

• Concepts and relationships are stored in the knowledge graph.

• Agents retrieve relevant information to make decisions or generate insights.

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7. Reinforcement Learning Optimization

Enable agents to optimize their behavior using reinforcement learning.

from src.utils.reinforcement_learning import QLearning

# Initialize a Q-Learning agent
state_size = 5
action_size = 3
rl_agent = QLearning(state_size, action_size)

# Simulate an environment
state = [0, 1, 0, 1, 0]  # Example state
action = rl_agent.choose_action(state)
print(f"Action chosen: {action}")

# Update the Q-table based on the reward
reward = 1  # Example reward
next_state = [1, 0, 1, 0, 1]
rl_agent.update_q_table(state, action, reward, next_state)

What Happens:

• The agent learns from its environment by updating its Q-table based on rewards.

• Actions become increasingly optimized over time.

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Key Takeaways

• These examples highlight the flexibility and power of the Aether Framework.

• Developers can combine multiple modules to create complex, decentralized systems.

• Each example serves as a building block for more advanced applications.