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Learn the Concepts
Foundational guides to context graphs, context backends, and building reliable AI
Holons, Context Graphs, and Ontologies
TrustGraph overcomes the limitations of traditional RAG by leveraging Semantic Web standards—such as OWL, SHACL, and RDF 1.2—to structure knowledge into formal ontologies, context-rich holons, and dynamic context graphs, enabling highly accurate and explainable AI generation.
How TrustGraph Makes Token Consumption Irrelevant
Learn how TrustGraph's self-hosting capabilities eliminate API costs and make token limits a thing of the past by deploying open-weight LLMs you fully control.
Why Can't I Do This in PostgreSQL?
Learn the fundamental differences between relational databases and TrustGraph's holonic context graph, and why relying on JOINs for complex, context-heavy AI data is an exercise in inefficiency and friction.
How It Works
Three steps to grounded, reliable AI agents
Ingest Your Data
Load documents, databases, and data sources into TrustGraph's multi-model store. Automated semantic indexing and ontology structuring prepare your data for precision retrieval.
Build a Holonic Context Graph
TrustGraph constructs a holonic context graph from your data, linking entities and relationships into structured, queryable knowledge your agents can trust.
Power Your Agents
Deploy AI agents backed by durable, grounded context. Every response, tool call, and decision is driven by verified, connected knowledge—not guesswork.
Everything You Need for Complete Agent Systems
Built-in features for production-ready AI agents—no assembly required
Context Management
Intelligent data structuring and retrieval
- Automated Context Graph ConstructionTransform raw data into interconnected context structures with ontology-driven context engineering
- Context CoresReusable, modular context bases that can be dynamically loaded and removed at runtime
- 3D GraphViz VisualizationInteractive three-dimensional exploration and analysis of graph relationships
- Relationship AnalysisDeep inspection of connections and dependencies within your context bases
AI Agent Architecture
Flexible single and multi-agent systems
- Single & Multi-Agent SystemsSupport for various agent configurations from simple single agents to complex multi-agent orchestration
- MCP InteroperabilityNative integration with Model Context Protocol for seamless external tool connections
- Custom WorkflowsRuntime-adjustable flow creation with dynamic parameter modification
- Intelligent Context RetrievalPrecision-grounded information extraction for highly accurate AI responses
Query & Retrieval
Advanced search and information retrieval
- Vector SearchSemantic similarity searching across your entire context base for contextually relevant results
- GraphRAG InterfaceGraph-enhanced retrieval-augmented generation combining context graphs with vector search
- Direct LLM ChatUnmediated language model interactions for flexible conversational experiences
- Multi-Modal QueriesSupport for text, structured data, and semantic queries across different data types
Data Processing
Enterprise-grade data ingestion and processing
- OCR Pipeline SupportBuilt-in optical character recognition for processing documents, images, and scanned materials
- High-Throughput StreamingEfficient large-scale data ingestion with Apache Pulsar for real-time processing
- Customizable ChunkingAdjustable document segmentation algorithms optimized for your data and use case
- Multi-Format SupportProcess PDFs, documents, structured databases, APIs, and unstructured data sources
Deployment Flexibility
Run anywhere, from local to enterprise cloud
- Multi-Cloud SupportDeploy on AWS, Azure, Google Cloud, OVHcloud, Scaleway, or any Kubernetes environment
- Fully ContainerizedComplete Docker-based deployment model for consistency across all environments
- Run Anywhere FlexibilityFrom local development to on-premise data centers to enterprise cloud deployments
- Single Command DeployLaunch your entire agent infrastructure with docker compose in seconds
Integrations & Storage
Connect to your existing stack seamlessly
- Multiple Graph StoresChoose from Neo4j, Apache Cassandra, Memgraph, or FalkorDB for your graph database
- Vector Database OptionsBuilt-in support for Qdrant (default), Pinecone, Milvus, and other vector stores
- Multi-LLM SupportIntegration with Anthropic, OpenAI, Google VertexAI, AWS Bedrock, and 40+ other providers
- Enterprise ConnectorsPre-built connectors for databases, APIs, cloud storage, and enterprise systems
Observability & Operations
Production-ready monitoring and operations
- Prometheus & Grafana IntegrationComprehensive monitoring dashboards with pre-configured metrics and alerts
- Telemetry TrackingTrack latency, error rates, throughput, and cost metrics across your entire system
- Audit LoggingComplete audit trails for compliance, debugging, and performance analysis
- Cost MonitoringReal-time tracking of LLM API costs, compute usage, and infrastructure expenses
Enterprise Security
Built for compliance and data sovereignty
- Data SovereigntyKeep your data in your chosen region or on-premise for regulatory compliance
- Native Multi-TenancyIsolated namespaces, separate resource allocation, individual quota management, and security boundary enforcement per tenant
- Access ControlFine-grained RBAC, SSO integration, and multi-factor authentication support
- Open Source TransparencyFull source code access for security audits and compliance verification
What is a Holonic Context Graph?
Learn how Context Graphs power smarter, more accurate AI
Watch TrustGraph in action
See how Holonic Context Graphs unlock the potential of AI