Unlock the potential of AI Agents with the Graph Powered Context Harness.
Ingest your data into a unified context graph, enrich that graph with ontologies, and serve that graph as structured context to your agents and applications. Instead of letting LLMs guess from flat text, TrustGraph harnesses your graph so every response, tool call, and decision is driven by connected context.
Deploy the Graph Powered Context Harness
Launch the agentic context graph infrastructure with a single command
Context as a First Class Citizen
Put context front and center in your agent architecture. Context graphs provide the semantic reasoning layer for agents you can trust.
Observe Decisions
Capture agent behavior behind business processes and connect that behavior to the original context. Know exactly where your agent answers came from.
Enterprise Ready Platform
Pre-built, containerized infrastructure. Modular, transparent design with end-to-end context management and MCP integration.
How It Works
Three simple steps to build intelligent AI agents
Connect Your Data
Integrate enterprise data sources through our containerized platform. Transform fragmented silos into unified context.
Build Context Graphs
Our platform automatically constructs interconnected Context Graphs with semantic relationships and contextual grounding.
Deploy AI Agents
Build and deploy sophisticated AI agents with superior reasoning, reduced hallucinations, and transparent decision-making.
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
Watch TrustGraph in action
See how Context Graphs unlock the potential of AI
