## The Infrastructure for Trustworthy AI --- [![PyPI version](https://img.shields.io/pypi/v/trustgraph.svg)](https://pypi.org/project/trustgraph/) ![E2E Tests](https://github.com/trustgraph-ai/trustgraph/actions/workflows/release.yaml/badge.svg) [![Discord](https://img.shields.io/discord/1251652173201149994 )](https://discord.gg/sQMwkRz5GX) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/trustgraph-ai/trustgraph) [**Docs**](https://docs.trustgraph.ai) | [**YouTube**](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) | [**Configuration Builder**](https://config-ui.demo.trustgraph.ai/) | [**Discord**](https://discord.gg/sQMwkRz5GX) | [**Blog**](https://blog.trustgraph.ai/subscribe)
> “Your bottleneck isn’t moving data — it’s understanding it.” TrustGraph is the open-source foundation for agentic AI you can trust. It fuses knowledge graphs, data streaming, and vector intelligence into a unified architecture — delivering context precision, provenance, and verifiable reasoning across any domain. Unlike closed systems that trap your data and obscure your logic, TrustGraph is built for transparency, portability, and proof. Every answer is traceable. Every insight, explainable. Every deployment, yours.
Table of Contents
- [**Key Features**](#key-features)
- [**Why TrustGraph?**](#why-trustgraph)
- [**Agentic MCP Demo**](#agentic-mcp-demo)
- [**Getting Started**](#getting-started)
- [**Configuration Builder**](#configuration-builder)
- [**Context Engineering**](#context-engineering)
- [**Knowledge Cores**](#knowledge-cores)
- [**Platform Architecture**](#platform-architecture)
- [**Integrations**](#integrations)
- [**Observability & Telemetry**](#observability--telemetry)
- [**Contributing**](#contributing)
- [**License**](#license)
- [**Support & Community**](#support--community)
## Key Features To meet the demands of enterprises, a platform needs to enable multi-tenancy, user and agentic access controls, data management, and total data privacy. TrustGraph enables these capabilities with: - **Flows and Flow Classes -> Multi-tenancy**. *Flow classes are sets of processing components that can be combined into logically separate flows for both users and agents.* - **Collections -> User/agent access controls and data management**. *Collections enable grouping data with custom labels that can be used for limiting data access to both users and agents. Collections can be added, deleted, and listed.* - **Tool Groups -> Multi-agent**. *Create groups for agent tools for multi-agent flows within a single deployment.* - **Knowledge Cores -> Data management and data privacy**. *Knowledge cores are modular and reusable components of knowledge graphs and vector embeddings that can serve as "long-term memory".* - **Fully Containerized Platform with Private Model Serving -> Total data privacy**. *The entire TrustGraph platform can be deployed in any environment while managing the deployment of private LLMs for total data sovereignty.* - **No-LLM Knowledge Graph Retrieval -> Deterministic Natural Language Graph Retrieval**. *TrustGraph does *not* use LLMs for knowledge graph retrieval. Natural language queries use semantic similarity search as the basis for building graph queries without LLMs enabling true graph enhanced agentic flows.* ## Why TrustGraph? [![Why TrustGraph?](https://img.youtube.com/vi/Norboj8YP2M/maxresdefault.jpg)](https://www.youtube.com/watch?v=Norboj8YP2M) ## Agentic MCP Demo [![Agentic MCP Demo](https://img.youtube.com/vi/mUCL1b1lmbA/maxresdefault.jpg)](https://www.youtube.com/watch?v=mUCL1b1lmbA) ## Getting Started - [**Quickstart Guide**](https://docs.trustgraph.ai/getting-started/) - [**Configuration Builder**](#configuration-builder) - [**Workbench**](#workbench) - [**Developer APIs and CLI**](https://docs.trustgraph.ai/reference/) - [**Example Notebooks**](https://github.com/trustgraph-ai/example-notebooks) - [**Deployment Guide**](https://docs.trustgraph.ai/deployment/) ### Watch TrustGraph 101 [![TrustGraph 101](https://img.youtube.com/vi/rWYl_yhKCng/maxresdefault.jpg)](https://www.youtube.com/watch?v=rWYl_yhKCng) ## Configuration Builder 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: - **Version**: Select the version of TrustGraph you'd like to deploy - **Component Selection**: Choose from the available deployment platforms, LLMs, graph store, VectorDB, chunking algorithm, chunking parameters, and LLM parameters - **Customization**: Enable OCR pipelines and custom embeddings models - **Finish Deployment**: Download the launch `YAML` files with deployment instructions ## Workbench 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: - **Agentic, GraphRAG and LLM Chat**: Chat interface for agentic flows, GraphRAG queries, or directly interfacing with a LLM - **Semantic Discovery**: Analyze semantic relationships with vector search, knowledge graph relationships, and 3D graph visualization - **Data Management**: Load data into the **Librarian** for processing, create and upload **Knowledge Packages** - **Flow Management**: Create and delete processing flow patterns - **Prompt Management**: Edit all LLM prompts used in the platform during runtime - **Agent Tools**: Define tools used by the Agent Flow including MCP tools - **MCP Tools**: Connect to MCP servers ## Context Engineering 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. ## Knowledge Cores 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). A Knowledge Core has two components: - Set of Graph Edges - Set of mapped Vector Embeddings 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. ## Platform Architecture 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). ## Integrations TrustGraph provides maximum flexibility so your agents are always powered by the latest and greatest components.
LLM APIs
- Anthropic
- AWS Bedrock
- AzureAI
- AzureOpenAI
- Cohere
- Google AI Studio
- Google VertexAI
- Mistral
- OpenAI
LLM Orchestration
- LM Studio
- Llamafiles
- Ollama
- TGI
- vLLM
VectorDBs
- Qdrant (default)
- Pinecone
- Milvus
Graph Storage
- Apache Cassandra (default)
- Memgraph
- Neo4j
- FalkorDB
Observability
- Prometheus
- Grafana
Control Plane
- Apache Pulsar
Clouds
- AWS
- Azure
- Google Cloud
- Intel Tiber Cloud
- OVHcloud
- Scaleway
## Observability & Telemetry Once the platform is running, access the Grafana dashboard at: ``` http://localhost:3000 ``` Default credentials are: ``` user: admin password: admin ``` The default Grafana dashboard tracks the following:
Telemetry
- LLM Latency
- Error Rate
- Service Request Rates
- Queue Backlogs
- Chunking Histogram
- Error Source by Service
- Rate Limit Events
- CPU usage by Service
- Memory usage by Service
- Models Deployed
- Token Throughput (Tokens/second)
- Cost Throughput (Cost/second)
## Contributing [Developer's Guide](https://docs.trustgraph.ai/community/developer.html) ## License **TrustGraph** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). Copyright 2024-2025 TrustGraph Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ## Support & Community - Bug Reports & Feature Requests: [Discord](https://discord.gg/sQMwkRz5GX) - Discussions & Questions: [Discord](https://discord.gg/sQMwkRz5GX) - Documentation: [Docs](https://docs.trustgraph.ai/)