Examples

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.

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.

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.

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.

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.

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.

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.

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.

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.

PreviousDatabase and Storage IntegrationsNextBlockchain Smart Contract Interaction

Last updated