TrustGraph 1.7: Multi-Tenant Infrastructure and Streaming APIs for Enterprise AI Engineering

TrustGraph, the semantic operating system for AI, announces the release of version 1.7—a significant milestone bringing enterprise-grade multi-tenancy, comprehensive streaming API support, and advanced ontology-based knowledge extraction to production deployments.

This release transforms how teams deploy, scale, and operationalize AI context infrastructure at enterprise scale. With native multi-tenant support, 60x latency improvements in streaming operations, and enhanced knowledge graph construction, TrustGraph 1.7 empowers data engineers to build trustworthy, production-ready agentic AI systems with complete control over data and context.

Enterprise Multi-Tenancy: Isolated Deployments at Scale

TrustGraph 1.7 introduces foundational multi-tenant architecture, enabling teams to deploy isolated, multi-customer environments on shared infrastructure—a critical requirement for enterprise deployments and managed service providers.

The multi-tenant infrastructure overhaul includes:

• Collection Management Migration: Collection management has been migrated from the Librarian to the Config Service, enabling centralized, tenant-aware configuration management
• Tenant-Specific Queues and Configurations: Services can now utilize tenant-specific queue and configuration overrides, ensuring complete isolation between deployments
• Config Service Distribution: Collection storage now leverages the Config Service with push-based distribution, eliminating manual synchronization and reducing operational overhead
• Fixed Parameter Handling: AsyncProcessor and Config Service parameter handling has been corrected, eliminating configuration mismatches that previously plagued queue customization

These architectural improvements enable data engineers to operate multiple independent TrustGraph instances on a single deployment, drastically simplifying infrastructure management while maintaining strict data isolation—a fundamental requirement for regulated industries and enterprise customers.

Python API Refactor: Streaming-First Architecture with 60x Latency Gains

The comprehensive Python API client refactor delivers feature parity across all LLM services and introduces streaming-first architecture with dramatic latency improvements.

60x Latency Reduction in Streaming Operations: Token streaming now achieves first-token latency of 500ms, compared to the previous 30-second baseline—a transformative improvement for real-time agentic workflows and interactive AI applications.

Complete Streaming Interfaces across all core services:

• Agent streams for agentic reasoning and orchestration
• GraphRAG streams for hybrid knowledge retrieval workflows
• DocumentRAG streams for document-based retrieval augmented generation
• Text completion streams for foundational LLM interactions
• Prompt management streams for dynamic prompt engineering

WebSocket Transport: Persistent connections with native multiplexing enable efficient real-time communication, reducing overhead and enabling complex multi-step agentic reasoning patterns.

Async/Await Support: Full async/await compatibility across REST, WebSocket, bulk, and metrics APIs ensures efficient resource utilization in production deployments.

Bulk Operations: Import and export capabilities for triples, graph embeddings, and document embeddings streamline knowledge core management and enable seamless data migration workflows.

Type-Safe Interfaces: Pythonic type hints provide IDE autocomplete, early error detection, and improved developer experience—all while maintaining complete backward compatibility with existing deployments.

CLI Utilities: Command-line tools have been updated to leverage the new streaming API, making advanced context engineering accessible to data engineers working in terminal environments.

This refactor brings TrustGraph's Python API to parity with modern async-first application frameworks, enabling data engineers to build responsive, production-grade agentic applications.

Enhanced Ontology Extraction: Phase 2 Knowledge Engineering

TrustGraph 1.7 advances ontology-based knowledge extraction with targeted improvements to entity normalization, parsing, and schema adherence.

The Phase 2 ontology extraction enhancements include:

• Entity Normalizer: Ensures consistent entity naming across knowledge graphs, eliminating duplicate entities caused by naming variations and improving semantic consistency
• Simplified Parser: Reduced parsing complexity improves extraction accuracy and reduces hallucination in knowledge graph construction
• Triple Converter: Converts extracted entities and relationships into properly-formatted RDF triples that strictly adhere to defined ontology schemas
• Enhanced Prompt Engineering: Improved prompts guide LLMs toward more accurate ontology-aware extractions, reducing noise and improving knowledge graph quality

These improvements directly address a critical pain point in GraphRAG deployments: knowledge graph quality directly determines the quality of context delivered to agents. By improving ontology-based extraction, TrustGraph 1.7 ensures that the Knowledge Cores produced during data transformation are semantically rich, properly structured, and optimized for downstream agentic reasoning.

System Monitoring and Production-Grade Logging

TrustGraph 1.7 ships with enterprise-grade operational infrastructure:

System Startup Verification: The new tg-verify-system-status CLI tool provides immediate visibility into deployment health, enabling rapid verification that all services are correctly configured and operational.

Loki Integration: Centralized log aggregation via Loki eliminates the need for log scraping across distributed services, enabling real-time log exploration and historical analysis.

Structured Logging: Log entries now include Service IDs instead of module names, dramatically improving traceability in distributed deployments where services scale horizontally.

Production-Grade Logging Strategy: Enhanced logging infrastructure follows industry best practices for observability, making TrustGraph deployments transparent and auditable.

Cost Tracking and Resource Visibility

TrustGraph 1.7 introduces model-aware metrics collection, enabling precise cost tracking and resource consumption analysis. All metering metrics now include model information, allowing data engineers to:

• Track LLM API costs by model and service
• Identify cost optimization opportunities
• Understand resource consumption patterns
• Build accurate cost forecasts for production deployments

Gateway Configuration Flexibility

New gateway queue override capabilities enable flexible deployment topologies, allowing data engineers to optimize routing and queue distribution for their specific infrastructure requirements.

Testing and Quality Assurance

Comprehensive Python API client testing with streaming validation ensures reliability and correctness across all streaming pathways. This extensive test coverage provides confidence in production deployments while enabling future API evolution.

Comprehensive Technical Specifications

TrustGraph 1.7 documentation now includes detailed technical specifications covering:

• Multi-tenant support architecture and isolation guarantees
• Python API refactor design and streaming architecture
• Ontology extraction phase 2 design and implementation details
• Enhanced logging strategy and observability architecture

These specifications enable deep technical understanding and serve as the foundation for custom integrations and advanced deployments.

Availability

Version 1.7 is available immediately via GitHub at https://github.com/trustgraph-ai/trustgraph and through the Configuration Builder at https://config-ui.demo.trustgraph.ai/.

The release is fully open source under the Apache 2.0 license, enabling enterprises to deploy, modify, and operate the platform on their own infrastructure with complete transparency and control.

---

For more information:

• Documentation: https://docs.trustgraph.ai
• GitHub Repository: https://github.com/trustgraph-ai/trustgraph
• Discord Community: https://discord.gg/sQMwkRz5GX
• Website: https://trustgraph.ai