TrustGraph is a production-ready platform for building post-training agentic systems. Combine your domain data with LLMs through unified infrastructure: real-time data pipelines, knowledge graph construction, vector retrieval, and agent orchestration. Deploy locally or in any cloud. Complete data sovereignty. Built by engineers, for engineers.
TrustGraph is not just another AI framework but a comprehensive context stack that bridges the gap between raw data and intelligent, adaptable agent deployments in production environments.
- **Complete Agentic Context Stack**
- Combines all necessary layers: data streaming control plane, knowledge graphs, vector databases, LLM integrations, and data pipelines in a unified platform.
- Enables deployment of intelligent agents grounded in domain-specific knowledge.
- **Post-Training Infrastructure**
- Supports transforming raw and streaming data into knowledge representations for fine-tuning and in-context agent reasoning.
- Enables continuous learning and optimization of AI agents beyond base model training.
- **Containerized Single Deployment**
- Simplifies operations with a turnkey containerized solution.
- Eliminates the complexity of managing multiple, disparate components and dependencies.
- **Multi-Cloud and Local Run Support**
- Runs anywhere—locally, on-premises, or in any cloud environment (AWS, Azure, GCP, OVHcloud, Scaleway).
- Supports data sovereignty and flexible deployment architectures.
The [**Configuration Builder**](https://config-ui.demo.trustgraph.ai/) assembles all of the selected components and builds them into a deployable package. It has 4 sections:
- **Component Selection**: Choose from the available deployment platforms, LLMs, graph store, VectorDB, chunking algorithm, chunking parameters, and LLM parameters
The **Workbench** is a UI that provides tools for interacting with all major features of the platform. The **Workbench** is enabled by default in the **Configuration Builder** and is available at port `8888` on deployment. The **Workbench** has the following capabilities:
TrustGraph features a complete context engineering solution combinging the power of Knowledge Graphs and VectorDBs. Connect your data to automatically construct Knowledge Graphs with mapped Vector Embeddings to deliver richer and more accurate context to LLMs for trustworthy agents.
- **Automated Knowledge Graph Construction:** Data Transformation Agents processes source data to automatically **extract key entities, topics, and the relationships** connecting them. Vector emebeddings are then mapped to these semantic relationships for context retrieval.
- **Deterministic Graph Retrieval:** Semantic relationsips are retrieved from the knowledge graph *without* the use of LLMs. When an agent needs to perform deep research, it first performs a **cosine similarity search** on the vector embeddings to identify potentially relevant concepts and relationships within the knowledge graph. This initial vector search **pinpoints relevant entry points** within the structured Knowledge Graph which gets built into graph queries *without* LLMs that retrieve the relevant subgraphs.
- **Context Generation via Subgraph Traversal:** Based on the ranked results from the similarity search, agents are provided with only the relevant subgraphs for **deep context**. Users can configure the **number of 'hops'** (relationship traversals) to extend the depth of knowledge availabe to the agents. This structured **subgraph**, containing entities and their relationships, forms a highly relevant and context-aware input prompt for the LLM that is endlessly configurable with options for the number of entities, relationships, and overall subgraph size.
One of the biggest challenges currently facing RAG architectures is the ability to quickly reuse and integrate knowledge sets like long-term memory for LLMs. **TrustGraph** solves this problem by storing the results of the data ingestion process in reusable Knowledge Cores. Being able to store and reuse the Knowledge Cores means the data transformation process has to be run only once. These reusable Knowledge Cores can be loaded back into **TrustGraph** and used for GraphRAG. Some sample knowledge cores are available for download [here](https://github.com/trustgraph-ai/catalog/tree/master/v3).
When a Knowledge Core is loaded into TrustGraph, the corresponding graph edges and vector embeddings are queued and loaded into the chosen graph and vector stores.
The platform orchestrates a comprehensive suite of services to transform external data into intelligent, actionable outputs for AI agents and users. It interacts with external data sources and external services (like LLM APIs) via an **API Gateway**.
Within the **TrustGraph** Platform, the services are grouped as follows:
- **Data Orchestration:** This crucial set of services manages the entire lifecycle of ingesting and preparing data to become AI-ready knowledge. It includes **Data Ingest** capabilities for various data types, a *Data Librarian* for managing and cataloging this information, *Data Transformation* services to clean, structure, and refine raw data, and ultimately produces consumable *Knowledge Cores*– the structured, enriched knowledge artifacts for AI.
- **Data Storage:** The platform relies on a flexible storage layer designed to handle the diverse needs of AI applications. This includes dedicated storage for *Knowledge Graphs* (to represent interconnected relationships), *VectorDBs* (for efficient semantic similarity search on embeddings), and *Tabular Datastores* (for structured data).
- **Context Orchestration:** This is the core reasoning engine of the platform. It leverages the structured knowledge from the Storage layer to perform *Deep Knowledge Retrieval* (advanced search and context discovery beyond simple keyword matching) and facilitate *Agentic Thinking*, enabling AI agents to process information and form complex responses or action plans.
- **Agent Orchestration:** This group of services is dedicated to managing and empowering the AI agents themselves. The *Agent Manager* handles the lifecycle, configuration, and operation of agents, while *Agent Tools* provide a framework or library of capabilities that agents can utilize to perform actions or interact with other systems.
- **Private Model Serving:** This layer is responsible for the deployment, management, and operationalization of the various AI models TrustGraph uses or provides to agents. This includes *LLM Deployment*, *Embeddings Deployment*, and *OCR Deployment*. Crucially, it features *Cross Hardware Support*, indicating the platform's ability to run these models across diverse computing environments.
- **Prompt Management:** Effective interaction with AI, especially LLMs and agents, requires precise instruction. This service centralizes the management of all prompt types: *LLM System Prompts* (to define an LLM's persona or core instructions), *Data Transformation Prompts* (to guide AI in structuring data), **RAG Context** generation (providing relevant intelligence to LLMs), and *Agent Definitions* (the core instructions and goals for AI agents).
- **Platform Services:** These foundational services provide the essential operational backbone for the entire TrustGraph platform, ensuring it runs securely, reliably, and efficiently. This includes *Access Controls* (for security and permissions), *Secrets Management* (for handling sensitive credentials), *Logging* (for audit and diagnostics), *Observability* (for monitoring platform health and performance), *Realtime Cost Observability* (for tracking resource consumption expenses), and *Hardware Resource Management* (for optimizing the use of underlying compute).
