TrustGraph 2.8 is available now, with a major upgrade to the platform foundation for enterprise knowledge and AI systems.
This release focuses on a practical problem: AI applications must remain reliable when they grow beyond a single demo, a single document library, or a small number of workspaces. That means scalable messaging, dependable retrieval, typed model outputs, observable services, auditable decisions, and an interface that can adapt to different domains.
TrustGraph has completed its migration from thread-per-consumer pub/sub to an asynchronous architecture.
In the previous model, a deployment with many workspaces and flows could consume hundreds of threads. TrustGraph 2.8 replaces that design with configurable async receive and send pools.
Async support now covers:
pulsar.asyncio.Client
aio-pika
aiokafka
For operators, this means a much stronger basis for multi-workspace deployments. For custom processor developers, it also means migrating extensions to the async model. Document RAG now combines two complementary retrieval strategies:
TrustGraph merges the results with Reciprocal Rank Fusion (RRF).
The first keyword-index implementation uses SQLite FTS5 and scopes indexes by workspace and collection. If keyword retrieval is unavailable, TrustGraph degrades gracefully rather than blocking retrieval entirely.
Hybrid retrieval is especially useful for enterprise content, where a user might search semantically in one query and need an exact product code, legal term, technical error, or named entity in the next.
TrustGraph 2.8 carries JSON schemas from prompt definitions through the completion layer into provider-native structured-output APIs.
This reduces the need to parse loose natural-language responses after generation. A runtime compatibility checker determines whether a prompt can use schema-enforced output with the selected backend.
Supported providers include OpenAI, vLLM, Ollama, Claude, Mistral, Azure OpenAI, Vertex AI Gemini, Google AI Studio, Bedrock, TGI, Llamafile, and LM Studio.
For agent builders, this enables more dependable workflows such as:
TrustGraph now includes a pluggable image-to-text service with an OpenAI vision backend, gateway support, REST and WebSocket clients, IAM integration, and the tg-describe-image CLI.
The release also introduces a Docling-powered document decoder. The trustgraph-docling package supports PDF, DOCX, XLSX, PPTX, HTML, Markdown, and CSV, with page and hybrid chunking modes.
These services widen the kinds of enterprise information that can enter a knowledge pipeline.
TrustGraph 2.8 adds structured audit events for gateway activity and IAM decisions. Events include request and client-IP correlation, and they can be consumed into organization-managed storage and retention systems.
Metrics coverage now spans the platform from infrastructure services through embeddings, reranking, query paths, agent orchestration, gateway authentication, knowledge extraction, and IAM.
All platform metrics now use the tg_ prefix. The release also reduces metric-cardinality risk by removing workspace and flow labels from infrastructure histograms.
A generic gateway passthrough mechanism now supports enterprise services outside the core TrustGraph platform. It uses opaque JSON request and response payloads with dynamic service lookup.
This makes it possible to attach organization-specific services, such as attestation engines, without modifying TrustGraph core code.
TrustGraph also continues to improve RDF support:
FILTER(LANG()) SPARQL patterns are supported.
TrustGraph 2.8 includes Workbench 2.0.3, which moves the UI to a manifest-driven plugin architecture.
Explorers can now be dynamically loaded as plugin packages. Teams can use remote plugins, multi-component bundles, and customer-specific plugins.json configurations.
Other Workbench additions include:
GraphCanvas3D. template. configuration prefix.
TrustGraph 2.8 contains important breaking changes:
8888, replacing 8088. global region rather than us-central1.--api-url or --pulsar-url.
TrustGraph 2.8 is open source and available now: