Modular Architecture

Aether Framework is built on a highly modular architecture, allowing developers to customize and extend its capabilities based on specific project needs. Each module is self-contained, interacts seamlessly with others, and can be replaced or upgraded without disrupting the core functionality.

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Core Principles

1. Flexibility Modules can be enabled or disabled depending on project requirements, making Aether suitable for both lightweight and complex applications.

2. Scalability The architecture supports adding new modules as the system grows, whether it’s for new databases, communication protocols, or AI models.

3. Interoperability Modules communicate through standardized interfaces, ensuring compatibility and easy integration with external tools and frameworks.

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Key Modules

1. Swarm Intelligence

• Purpose: Manage decentralized agent networks for collaborative decision-making.

• Components:

  • Swarm nodes

  • Task scheduler

  • Reinforcement learning

• Example:

  from src.swarm.advanced_swarm_behavior import Swarm
  
  swarm = Swarm(10)
  swarm.simulate(5)

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2. Blockchain Integration

• Purpose: Enable secure, on-chain operations and decentralized decision-making.

• Components:

  • Ethereum and Solana wallet managers

  • Smart contract deployment and interaction

• Example:

  from src.blockchain.blockchain_manager import BlockchainManager
  
  blockchain = BlockchainManager()
  contract_address = blockchain.deploy_contract(abi, bytecode)

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3. Multi-Modal Processing

• Purpose: Handle and process diverse data types, such as text, images, and audio.

• Components:

  • Text analysis

  • Image processing

  • Audio processing

• Example:

  from src.utils.multi_modal_handler import MultiModalHandler
  
  multi_modal = MultiModalHandler()
  result = multi_modal.process_text("Analyze this data")

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4. Knowledge Graph

• Purpose: Maintain relationships and knowledge across agents.

• Components:

  • Entity-relationship storage

  • Advanced querying

  • Graph visualization

• Example:

  from src.utils.knowledge_graph import KnowledgeGraph
  
  graph = KnowledgeGraph()
  graph.add_concept("Agent", {"role": "worker"})

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5. Decentralized Messaging (IPFS)

• Purpose: Enable agents to communicate in disconnected or decentralized environments.

• Components:

  • IPFS file sharing

  • Decentralized messaging protocols

• Example:

  from src.integrations.ipfs_communication import IPFSCommunication
  
  ipfs = IPFSCommunication()
  ipfs.send_message("Hello from Node 1")

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6. Reinforcement Learning

• Purpose: Train agents to optimize task execution using rewards and penalties.

• Components:

  • Q-Learning-based optimization

  • Multi-agent reinforcement learning (future milestone)

• Example:

  from src.utils.reinforcement_learning import QLearning
  
  rl_agent = QLearning(state_size=5, action_size=3)
  action = rl_agent.choose_action(state)

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Advantages of Modular Design

1. Ease of Development Developers can focus on individual modules without worrying about breaking the entire framework.

2. Customizability Swap out modules for alternatives (e.g., use Qdrant instead of Redis for vector storage).

3. Future-Ready Add new technologies (e.g., federated learning) without altering the existing structure.

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Customizing Aether

1. Add New Modules

   • Create a new module folder (e.g., src/custom_module/).

   • Define module-specific functionality.

   • Connect to the core using standardized interfaces.

2. Replace Existing Modules

   • Swap Redis for Qdrant, or replace knowledge graph storage with Neo4j.

3. Configure Modules

   • Use the config.yaml file to enable or disable specific modules.

     modules:
       redis: enabled
       neo4j: disabled
       ipfs: enabled