Integrations
Overview
The Aether Framework provides modular integration capabilities to connect with a wide range of tools, platforms, and services. This flexibility enables developers to create robust systems that leverage the best technologies in decentralized computing, AI, and blockchain.
Supported Integrations
1. Blockchain Networks
Aether integrates seamlessly with Ethereum, Solana, and other blockchain networks for secure, trustless operations.
Key Features:
Smart Contract Deployment: Deploy and interact with smart contracts for on-chain task verification.
On-Chain Logging: Log task results and decision-making on the blockchain for transparency.
Multi-Chain Compatibility: Operate across different blockchains for diverse use cases.
Example:
from src.utils.blockchain_manager import BlockchainManager blockchain = BlockchainManager() # Deploy a contract contract_address = blockchain.deploy_contract(abi, bytecode) print(f"Contract deployed at: {contract_address}") # Call a contract function result = blockchain.call_contract_function(contract_address, abi, "getValue") print(f"Contract result: {result}")
2. Decentralized Storage (IPFS)
IPFS provides decentralized, immutable storage for data, enabling agents to share files without relying on centralized systems.
Key Features:
Decentralized file storage and retrieval.
Immutable data for tamper-proof records.
Distributed access across nodes.
Example:
from src.utils.ipfs_client import IPFSClient ipfs_client = IPFSClient() # Upload a file cid = ipfs_client.upload_file("data/report.pdf") print(f"File uploaded to IPFS with CID: {cid}") # Retrieve the file ipfs_client.retrieve_file(cid, output_path="downloaded_report.pdf") print(f"File downloaded to: downloaded_report.pdf")
3. Redis for Task Management
Redis is used for managing distributed task queues and enabling high-speed operations in agent swarms.
Key Features:
Distributed task queue for task prioritization.
High-speed voting and consensus mechanisms using Lua scripts.
Scalable operations for large swarms.
Example:
from src.utils.redis_task_queue import RedisTaskQueue redis_queue = RedisTaskQueue() # Push a task to the queue redis_queue.push_task({"task": "Analyze data trends"}) # Pop a task from the queue task = redis_queue.pop_task() print(f"Task popped from queue: {task}")
4. Knowledge Graphs
Aether integrates with a Knowledge Graph module to store, query, and visualize structured data.
Key Features:
Add concepts and relationships for advanced reasoning.
Query structured data for decision-making.
Visualize knowledge graphs to debug and analyze connections.
Example:
from src.utils.knowledge_graph import KnowledgeGraph knowledge_graph = KnowledgeGraph() # Add a concept knowledge_graph.add_concept("AI Agent", {"role": "worker"}) # Add a relationship knowledge_graph.add_relationship("AI Agent", "Swarm", "belongs_to") # Visualize the graph knowledge_graph.visualize_graph(output_path="graph.png")
5. Cloud Services
Aether can integrate with cloud platforms like AWS, GCP, and Azure for hybrid deployments.
Key Features:
Store large datasets in cloud storage for scalability.
Use cloud compute resources for training or simulations.
Hybrid solutions combining IPFS for decentralized access and cloud for centralized redundancy.
6. External APIs
Agents can interact with external APIs for diverse functionalities such as fetching data, triggering workflows, or sending notifications.
Key Features:
Dynamic integration with REST APIs.
Webhook support for real-time event-driven workflows.
Secure API key management.
Example:
import requests response = requests.get("https://api.example.com/data") print(f"Fetched data: {response.json()}")
Benefits of Integrations
Modularity: Easily plug in or replace components to fit specific use cases.
Scalability: Scale across blockchains, storage networks, and cloud services.
Interoperability: Build systems that combine decentralized and centralized technologies.
Future-Proofing: Adapt to emerging technologies and integrate them seamlessly.
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