Components
This document provides a detailed overview of Equilink's core components and their interactions.

1. Agent Core (equilink.core)
The central orchestration system that manages all component interactions and decision-making processes.
Key Modules
Features
Autonomous decision making
System state management
Component lifecycle management
Configuration handling
Error recovery and resilience
2. AI Engine (equilink.ai)
Manages AI model interactions and cognitive processes.
Architecture
Features
Multi-model orchestration
Dynamic prompt management
Context awareness
Memory systems
Learning capabilities
3. Blockchain Integration (equilink.blockchain)
Handles all blockchain interactions and transaction management.
Components
Features
Multi-chain support
Transaction management
Smart contract interaction
Gas optimization
Wallet management
4. DeFi Integration (equilink.defi)
Manages interactions with DeFi protocols and strategies.
Structure
Features
Protocol integration
Strategy execution
Position management
Yield optimization
Risk assessment
5. Market Analysis (equilink.analysis)
Handles market data analysis and trading signals.
Components
Features
Price analysis
Volume analysis
Technical indicators
Pattern recognition
Sentiment analysis
6. Social Integration (equilink.social)
Manages social media integration and community engagement.
Structure
Features
Platform integration
Sentiment analysis
Trend detection
Community management
Automated engagement
7. Data Management (equilink.data)
Handles data storage, processing, and analytics.
Components
Features
Data storage
Caching
Processing pipelines
Analytics
Reporting
8. Security (equilink.security)
Manages system security and access control.
Structure
Features
Key management
Access control
Security monitoring
Audit logging
Rate limiting
Data Flow
Communication Patterns
Event-Based Communication
Component Coordination
Component Configuration
Integration Points
External Systems
Blockchain nodes
AI API endpoints
Social platforms
Data providers
Internal Services
Message queue
Cache layer
Database
Metrics collection
Health Checks
Performance Metrics
Response times
Resource usage
Success rates
Error rates
Recovery Procedures
Fallback Mechanisms
Secondary AI models
Backup data sources
Alternative execution paths
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