Stay up to date with the latest TrustGraph announcements, product releases, and insights from our team. Follow our journey as we transform enterprise AI.
TrustGraph 2.10 introduces SHACL-AF policy-based query filtering, guided GraphRAG traversal, cloze model classification, Laya decision scoring, embedding search attributes, and model listing across all LLM backends — alongside a VOWL ontology visualiser, upgrade notifications, and RDF type search filtering in the Workbench.
TrustGraph 2.9 ships a fully config-driven Workbench with intent-based navigation, a DCAT welcome page, embedded semantic search, and IAM admin plugins — alongside a LangChain-free core, named graph bulk APIs, and pre-loaded container models.
TrustGraph 2.8 delivers a foundational platform upgrade for teams deploying knowledge-driven AI systems at enterprise scale: fully asynchronous messaging infrastructure, hybrid Document RAG retrieval, native LLM structured output, image understanding, comprehensive auditability, and a redesigned extensible UI platform.
TrustGraph 2.6 introduces FlashRank cross-encoder reranking for RAG, OpenAI-compatible LLM variants, portable workspace bundles, fine-grained IAM, and a massive UI update with interactive domain explorers.
TrustGraph announces the release of TrustGraph 2.5, adding Alibaba Cloud as a supported deployment target, shipping interactive SPARQL and GraphQL workbenches in the UI, delivering comprehensive SPARQL 1.1 function support in the query engine, and closing a significant authentication gap in the MCP server. This release continues TrustGraph's push toward production-grade enterprise deployments across a broader range of cloud infrastructure.
TrustGraph announces the release of 2.4, the most significant update to the open-source context graph platform since its founding. This release ships a comprehensive web UI for the first time, introduces a full identity and access management (IAM) layer with JWT authentication, and replaces the previous user-based isolation model with a workspace-scoped multi-tenancy architecture.
This release delivers significant infrastructure improvements that reduce memory overhead by up to 3.5 GB per deployment, expands messaging fabric choices to include RabbitMQ and experimental Kafka support, and introduces deeper agent explainability tooling — making TrustGraph faster to deploy, easier to operate at scale, and more transparent in how its agents reason.
The release marks a significant step forward in TrustGraph's mission to provide deterministic, explainable AI infrastructure for enterprise and open-source deployments alike.
TrustGraph establishes itself as the leading context graph technology, democratizing knowledge through open source.
Decisions are about more than just context and tracing the steps between input A and output B. Decisions are about uncertainty, risk, intent, goals, and even governance.
Don't build many context graphs. Build one. A single source of truth that provides AI systems the precision and accuracy they demand.
Until agentic systems make context a first-class citizen, they will continue to struggle. Even then, how will enteprises guarantee that bad outcomes won't occur?
Context Graphs are much more than a system of records. They provide the foundation for solving the problem of competence, performance, and scope in AI systems.
TrustGraph releases version bringing comprehensive API documentation and a new plugin system for messaging.
Accepting the probabilistic nature of AI means rethinking what determinism means for creating trustworthy AI systems.
Graph reification unlocks the potential for closing the loop on context graph driven AI systems to create an auditable system of records.
What are Context Graphs? A deep dive into the history of knowledge graphs, the evolution of RAG, and how context graphs could be the next big thing in AI.
TrustGraph 1.7 delivers multi-tenant infrastructure, comprehensive Python API streaming support, and enhanced knowledge extraction capabilities for enterprise deployments
TrustGraph Release 1.6 introduces streaming responses for ontology-driven AI, enhancing real-time interaction capabilities while maintaining the platform's knowledge graph-centric architecture.
TrustGraph introduces automated ontology-driven graph building capabilities, revolutionizing how enterprises structure their knowledge for AI applications.
A deep dive into TrustGraph's integration with Qdrant vector database, powering enterprise-grade agentic AI with semantic search and knowledge graph capabilities.
Version 1.4.22 introduces revolutionary flow configuration capabilities and enhanced collection management, making it easier than ever to build and deploy sophisticated AI agents.
Version 1.3.20 introduces groundbreaking natural language database querying capabilities, allowing users to interact with their data using plain English.
An in-depth look at how TrustGraph leverages Apache Pulsar for real-time event streaming, enabling scalable and reliable AI agent infrastructure.
Exploring how TrustGraph and European cloud providers are democratizing enterprise AI, offering sovereignty and choice beyond AWS, Azure, and Google Cloud.
Join us at Data Streaming Summit 2025 where we'll showcase how TrustGraph combines event streaming with knowledge graphs for next-generation AI applications.
Version 1.2 introduces AI agents that automatically construct and maintain knowledge graphs from unstructured data, dramatically reducing setup time and improving accuracy.
How TrustGraph enables organizations to maintain data sovereignty, avoid vendor lock-in, and build AI infrastructure that stands the test of time.
Version 1.1 introduces comprehensive support for Anthropic's Model Context Protocol, enabling seamless integration between AI agents and external tools, data sources, and services.
TrustGraph officially launches version 1.0, introducing a sovereign, universal AI adapter designed for enterprise innovation and contextual intelligence.