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ARCHITECTURE.md

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OpenSVM Architecture

This document outlines the architecture of the OpenSVM project, describing its components, data flow, and technical design.

System Overview

OpenSVM is built as a Next.js application with a client-server architecture. The application interacts with the Solana blockchain through RPC endpoints and provides a user interface for exploring blockchain data.

graph TD
    User[User] --> UI[Web UI]
    UI --> NextJS[Next.js Application]
    NextJS --> API[API Routes]
    NextJS --> Pages[Page Components]
    API --> SolanaLib[Solana Library]
    API --> AILib[AI Library]
    SolanaLib --> RPC[Solana RPC Endpoints]
    AILib --> LLM[LLM Services]
    API --> Cache[Caching Layer]
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Core Components

Frontend Layer

  • Page Components: React components for different views (blocks, transactions, accounts, etc.)
  • UI Components: Reusable UI elements like tables, charts, and visualizations
  • State Management: Client-side state management for UI interactions
  • Data Visualization: Components for rendering blockchain data visually

API Layer

  • API Routes: Next.js API routes for handling data requests
  • Solana Proxy: Middleware for Solana RPC requests
  • Data Processing: Services for processing and transforming blockchain data
  • AI Integration: Endpoints for AI-powered analysis and explanations

Data Layer

  • Solana Connection: Services for interacting with Solana RPC endpoints
  • Caching: In-memory and persistent caching for performance optimization
  • Data Models: TypeScript interfaces and classes for blockchain data

AI Components

  • Agent System: AI agent for handling natural language queries
  • Tools: Specialized tools for different blockchain operations
  • Knowledge Base: Structured knowledge about Solana and blockchain concepts
  • LLM Integration: Integration with language models for generating responses

Data Flow

  1. User Request: User interacts with the UI to request blockchain data
  2. API Processing: Next.js API routes handle the request
  3. Solana Interaction: Solana library fetches data from RPC endpoints
  4. Data Transformation: Raw blockchain data is transformed into application models
  5. Rendering: Processed data is rendered in the UI

Key Technologies

  • Next.js: React framework for server-rendered applications
  • TypeScript: Typed JavaScript for better developer experience
  • Solana Web3.js: Official Solana JavaScript SDK
  • Tailwind CSS: Utility-first CSS framework
  • D3.js/Cytoscape: Libraries for data visualization
  • XState: State machine library for complex workflows
  • Together AI: LLM provider for AI capabilities

Subsystems

Transaction Visualization

The transaction visualization system renders graphical representations of transaction data:

graph LR
    TX[Transaction Data] --> Parser[Transaction Parser]
    Parser --> Graph[Graph Generator]
    Graph --> Renderer[Visual Renderer]
    Renderer --> D3[D3.js Visualization]
    Renderer --> Cytoscape[Cytoscape Visualization]
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Wallet Path Finding

The wallet path finding system traces connections between wallets:

graph LR
    Input[Source/Target Wallets] --> Validator[Input Validator]
    Validator --> Cache[Path Cache]
    Cache -- Cache Miss --> BFS[BFS Algorithm]
    BFS --> SolanaAPI[Solana API]
    BFS --> Result[Path Result]
    Cache -- Cache Hit --> Result
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AI Assistant

The AI assistant system processes natural language queries:

graph LR
    Query[User Query] --> Agent[AI Agent]
    Agent --> Tools[Blockchain Tools]
    Tools --> SolanaAPI[Solana API]
    Agent --> LLM[Language Model]
    LLM --> Response[AI Response]
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Deployment Architecture

OpenSVM can be deployed in various environments:

graph TD
    Code[Source Code] --> Build[Build Process]
    Build --> Static[Static Assets]
    Build --> Server[Server Components]
    Static --> CDN[Content Delivery Network]
    Server --> Hosting[Application Hosting]
    Hosting --> Docker[Docker Container]
    Docker --> Cloud[Cloud Provider]
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Performance Considerations

  • Caching: Extensive caching of blockchain data to reduce RPC calls
  • Pagination: Paginated data loading for large datasets
  • Lazy Loading: Deferred loading of non-critical components
  • Optimized Queries: Efficient RPC queries to minimize data transfer
  • Edge Caching: CDN caching for static assets and API responses

Security Considerations

  • Input Validation: Thorough validation of user inputs
  • Rate Limiting: Protection against excessive API usage
  • Error Handling: Graceful handling of errors and edge cases
  • Environment Variables: Secure storage of sensitive configuration
  • Content Security Policy: Protection against XSS and other attacks