Architecture

Learn about TrustGraph’s system architecture and design principles for building intelligent AI agent platforms.

System Overview

High-Level Architecture

TrustGraph follows a modular, microservices-based architecture that enables scalable knowledge graph construction and AI agent deployment. The platform is designed to integrate with existing enterprise infrastructure while providing advanced knowledge processing capabilities.

Core Components

• Knowledge Graph Builder: Extracts entities and relationships from enterprise data
• Vector Embedding Engine: Creates semantic embeddings for knowledge elements
• GraphRAG Processor: Combines graph and vector search for contextual retrieval
• AI Agent Runtime: Executes intelligent agents with access to knowledge graphs
• Integration Layer: Connects with external LLMs, databases, and enterprise systems

Data Flow

1. Ingestion: Raw data from various sources (documents, databases, APIs)
2. Processing: Entity extraction, relationship identification, and graph construction
3. Embedding: Vector representation of knowledge elements
4. Storage: Persistent storage in graph and vector databases
5. Query: AI agents query knowledge graphs for contextual information
6. Response: Contextually-aware responses based on relationship understanding

Storage Layer

Knowledge Graph Storage

Supports multiple graph database backends including Neo4j, ArangoDB, and others. Stores entities, relationships, and metadata in optimized graph structures.

Vector Database Integration

Integrates with popular vector databases like Pinecone, Weaviate, and Chroma for semantic similarity search and hybrid retrieval.

Knowledge Packages

Combines graph and vector storage into unified “Knowledge Packages” that provide both structured relationships and semantic search capabilities.

Processing Layer

Entity Extraction Engine

Uses advanced NLP techniques to identify entities, relationships, and concepts from unstructured data sources.

Relationship Mapping

Builds sophisticated relationship maps that understand how different entities connect and influence each other.

GraphRAG Processing

Implements Graph Retrieval-Augmented Generation that leverages both graph structure and vector similarity for enhanced context retrieval.

AI Agent Orchestration

Manages the execution of multiple AI agents with access to shared knowledge graphs and contextual information.

Integration Layer

LLM Integration

Supports multiple Large Language Models through standardized interfaces, enabling organizations to use their preferred models.

Enterprise Data Connectors

Built-in connectors for common enterprise systems including databases, document management systems, and APIs.

API Gateway

Provides unified access to all TrustGraph capabilities through REST APIs, GraphQL, and WebSocket connections.

Deployment Architecture

Containerized Deployment

Fully containerized using Docker with Kubernetes orchestration for scalable, cloud-native deployments.

Microservices Design

Modular architecture allows independent scaling of different components based on workload requirements.

Multi-Cloud Support

Designed to run on any cloud platform or on-premises infrastructure with consistent performance and capabilities.

Security & Compliance

Built-in security features including data encryption, access controls, and audit logging to meet enterprise security requirements.

Scalability & Performance

Horizontal Scaling

Components can be scaled independently based on demand, from knowledge graph construction to AI agent execution.

Distributed Processing

Supports distributed processing for large-scale knowledge graph construction and complex query processing.

Caching & Optimization

Intelligent caching strategies and query optimization ensure fast response times even with large knowledge graphs.