Operationalizing AI requires more than a vector database. TrustGraph provides the complete Context Operating System service layer. Flexible Flows for real-time ingestion, isolated Context Cores for secure multi-tenancy, and comprehensive observability. Treat your context like infrastructure — predictable, scalable, and fully under your control.

Deploy anywhere. Own your data. Control your context.

• Build Context Graphs — Automatically extract entities, relationships, and knowledge structures from your enterprise data
• Manage Context — Organize, version, and govern your context graphs with enterprise-grade tools and custom ontologies
• Deploy Intelligent Agents — Run AI agents grounded in your own precise context with full visibility and control
• Maintain Full Sovereignty — Keep your data and AI stack entirely under your control, deployed on-prem, in the cloud, or on bare metal

• Automated Entity & Relationship Extraction — AI-powered agents automatically identify key concepts and connections in your data
• Ontology-Driven Graphs — Define what should be extracted, not just what can be extracted, for consistent, controlled knowledge representation
• Multi-Format Data Support — Process PDFs, documents, databases, APIs, and structured data sources simultaneously
• Vector Embedding Integration — Automatic semantic embeddings mapped to graph relationships for hybrid retrieval

• Context Cores — Package and version your processed context for reuse across projects and deployments
• Collections — Organize knowledge by domain, project, or dataset with enterprise governance controls
• Flow Configuration — Design flexible data processing pipelines with runtime control and prompt management
• Observability & Telemetry — Monitor processing status, costs, performance, and agent behavior in real-time

• GraphRAG Queries — Intelligent retrieval combining graph structure and semantic search for deep contextual understanding
• Agentic Workflows — Build sophisticated agents that understand relationships, perform reasoning, and make decisions based on your knowledge
• Model Context Protocol (MCP) — Connect agents to external tools, APIs, and services while maintaining grounded context
• Multi-Model Support — Deploy local open-source models or connect to Anthropic, OpenAI, Google, Mistral, and other LLM providers

• Production Deployment — Kubernetes-native, fully containerized, ready for enterprise scale
• Cost Observability — Real-time tracking of token usage, inference costs, and resource consumption
• Access Controls & Secrets Management — Enterprise security with fine-grained permissions and credential handling
• Flexible Storage — Graph databases (Neo4j, Cassandra, Memgraph), vector stores (Qdrant, Pinecone, Milvus), and support for structured data

Anthropic Claude • OpenAI • Google AI Studio • Google VertexAI • Mistral • Cohere • AWS Bedrock • Azure OpenAI

Ollama • LM Studio • vLLM • Hugging Face TGI • Llamafiles

Qdrant • Pinecone • Milvus

Neo4j • Apache Cassandra • Memgraph • FalkorDB

AWS • Azure • Google Cloud • OVHcloud • Scaleway

Prometheus • Grafana

Model Context Protocol (MCP) for seamless agent integration with external APIs and tools

• Have Questions? Join our Discord
• Found a Bug? Open an issue
• Need Help? Check the documentation
• Ready to Contribute? See the contributing guide