Software Architecture

Nexent adopts a modern distributed microservices architecture designed to provide high-performance, scalable AI agent platform. The entire system is based on containerized deployment, supporting cloud-native and enterprise-grade application scenarios.

🏗️ Overall Architecture Design

Nexent's software architecture follows layered design principles, structured into the following core layers from top to bottom:

🌐 Frontend Layer

• Technology Stack: Next.js + React + TypeScript
• Functions: User interface, agent interaction, multimodal input processing
• Features: Responsive design, real-time communication, internationalization support

🔌 API Gateway Layer

• Core Service: FastAPI high-performance web framework
• Responsibilities: Request routing, authentication, API version management, load balancing
• Ports: 5010 (main service), 5012 (data processing service)

🧠 Business Logic Layer

• Agent Management: Agent generation, execution, monitoring
• Conversation Management: Multi-turn dialogue, context maintenance, history tracking
• Knowledge Base Management: Document processing, vectorization, retrieval
• Model Management: Multi-model support, health checks, load balancing

📊 Data Layer

Distributed data storage architecture with multiple specialized databases:

🗄️ Structured Data Storage

• PostgreSQL: Primary database storing user information, agent configurations, conversation records
• Port: 5434
• Features: ACID transactions, relational data integrity

🔍 Search Engine

• Elasticsearch: Vector database and full-text search engine
• Port: 9210
• Functions: Vector similarity search, hybrid search, large-scale optimization

💾 Cache Layer

• Redis: High-performance in-memory database
• Port: 6379
• Usage: Session caching, temporary data, distributed locks

📁 Object Storage

• MinIO: Distributed object storage service
• Port: 9010
• Functions: File storage, multimedia resource management, large file processing

🔧 Core Service Architecture

🤖 Agent Services

Agent framework based on SmolAgents, providing:
├── Agent generation and configuration
├── Tool calling and integration
├── Reasoning and decision execution
└── Lifecycle management

📈 Data Processing Services

Distributed data processing architecture:
├── Real-time document processing (20+ format support)
├── Batch data processing pipelines
├── OCR and table structure extraction
└── Vectorization and index construction

🌐 MCP Ecosystem

Model Context Protocol tool integration:
├── Standardized tool interfaces
├── Plugin architecture
├── Third-party service integration
└── Custom tool development

🚀 Distributed Architecture Features

⚡ Asynchronous Processing Architecture

• Foundation Framework: High-performance async processing based on asyncio
• Concurrency Control: Thread-safe concurrent processing mechanisms
• Task Queue: Celery + Ray distributed task execution
• Stream Processing: Real-time data and response streaming

🔄 Microservices Design

Service decomposition strategy:
├── nexent (main service) - Agent core logic
├── nexent-data-process (data processing) - Document processing pipeline
├── nexent-mcp-service (MCP service) - Tool protocol service
└── Optional services (SSH, monitoring, etc.)

🌍 Containerized Deployment

Docker Compose service orchestration:
├── Application service containerization
├── Database service isolation
├── Network layer security configuration
└── Volume mounting for data persistence

🔐 Security and Scalability

🛡️ Security Architecture

• Authentication: Multi-tenant support, user permission management
• Data Security: End-to-end encryption, secure transmission protocols
• Network Security: Inter-service secure communication, firewall configuration

📈 Scalability Design

• Horizontal Scaling: Independent microservice scaling, load balancing
• Vertical Scaling: Resource pool management, intelligent scheduling
• Storage Scaling: Distributed storage, data sharding

🔧 Modular Architecture

• Loose Coupling Design: Low inter-service dependencies, standardized interfaces
• Plugin Architecture: Hot-swappable tools and models
• Configuration Management: Environment isolation, dynamic configuration updates

🔄 Data Flow Architecture

📥 User Request Flow

User Input → Frontend Validation → API Gateway → Route Distribution → Business Service → Data Access → Database

🤖 Agent Execution Flow

User Message → Agent Creation → Tool Calling → Model Inference → Streaming Response → Result Storage

📚 Knowledge Base Processing Flow

File Upload → Temporary Storage → Data Processing → Vectorization → Knowledge Base Storage → Index Update

⚡ Real-time Processing Flow

Real-time Input → Instant Processing → Agent Response → Streaming Output

🎯 Architecture Advantages

🏢 Enterprise-grade Features

• High Availability: Multi-layer redundancy, failover capabilities
• High Performance: Asynchronous processing, intelligent caching
• High Concurrency: Distributed architecture, load balancing
• Monitoring Friendly: Comprehensive logging and status monitoring

🔧 Developer Friendly

• Modular Development: Clear hierarchical structure
• Standardized Interfaces: Unified API design
• Flexible Configuration: Environment adaptation, feature toggles
• Easy Testing: Unit testing and integration testing support

🌱 Ecosystem Compatibility

• MCP Standard: Compliant with Model Context Protocol
• Open Source Ecosystem: Integration with rich open source tools
• Cloud Native: Support for Kubernetes and Docker deployment
• Multi-model Support: Compatible with mainstream AI model providers

---

This architectural design ensures that Nexent can provide a stable, scalable AI agent service platform while maintaining high performance. Whether for individual users or enterprise-level deployments, it delivers excellent user experience and technical assurance.