trustgraph/README.md

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# Eliminate AI Hallucinations with Private Knowledge Bases

Build accurate, trustworthy AI agents powered by your own private data. TrustGraph connects your documents, databases, tools, and agents into a unified knowledge graph with precise retrieval, full observability, and deploy-anywhere control.

<details>
<summary>Table of Contents</summary>
<br>

- [**Key Features**](#key-features)<br>
- [**Why TrustGraph?**](#why-trustgraph)<br>
- [**Agentic MCP Demo**](#agentic-mcp-demo)<br>
- [**Getting Started**](#getting-started)<br>
- [**Configuration Builder**](#configuration-builder)<br>
- [**Knowledge Cores**](#knowledge-cores)<br>
- [**Integrations**](#integrations)<br>
- [**Observability & Telemetry**](#observability--telemetry)<br>
- [**Contributing**](#contributing)<br>
- [**License**](#license)<br>
- [**Support & Community**](#support--community)<br>

</details>

## Key Features

- **Unify your Data for Smarter AI**
- **Ship Intelligent Agents Powered with Your Data**
- **Connect Your Agents with MCP**
- **Run Anywhere**
- **Observe Your Data**
- **Serve Models for Private LLM Inference**
- **Create Many Workflows**
- **Control Data Access**

## Why TrustGraph?

Ingests structured and unstructured data (PDFs, databases, OCR, custom schemas, and ontologies) into a single system. Define tools that can search your private knowledge bases and retrieve deep relationships to eliminate hallucinations from agent responses. Connect agents with the Model Context Protocol (MCP) to leverage external tools, services, and custom workflow. Deploy all of the services, datastores, and agents locally, on-premises, or in cloud. Visualize deep data relationships with 3D GraphViz and a full observability and telemetry stack. Deploy LLMs on your hardware for full control of your data. Flows and Flow Classes enable unique agent workflows. Manage user and agent access to data with collections and knowledge cores.

[![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/)
- [**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

## Knowledge Cores

A challenge facing RAG architectures is the ability to quickly reuse and remove datasets from pipelines. **TrustGraph** stores the results of the data ingestion process in reusable Knowledge Cores. Knowledge cores can be loaded and removed during runtime. Some sample knowledge cores are [here](https://github.com/trustgraph-ai/catalog/tree/master/v3).

A Knowledge Core has two components:

- Knowledge graph triples
- Vector embeddings mapped to the knowledge graph

## Integrations
TrustGraph provides maximum flexibility to avoid vendor lock-in.

<details>
<summary>LLM APIs</summary>
<br>

- Anthropic<br>
- AWS Bedrock<br>
- AzureAI<br>
- AzureOpenAI<br>
- Cohere<br>
- Google AI Studio<br>
- Google VertexAI<br>
- Mistral<br>
- OpenAI<br>

</details>
<details>
<summary>LLM Orchestration</summary>
<br>

- LM Studio<br>
- Llamafiles<br>
- Ollama<br>
- TGI<br>
- vLLM<br>

</details>
<details>
<summary>VectorDBs</summary>
<br>

- Qdrant (default)<br>
- Pinecone<br>
- Milvus<br>

</details>
<details>
<summary>Graph Storage</summary>
<br>

- Apache Cassandra (default)<br>
- Neo4j<br>
- Memgraph<br>
- FalkorDB<br>

</details>
<details>
<summary>Observability</summary>
<br>  

- Prometheus<br>
- Grafana<br>

</details>
<details>
<summary>Control Plane</summary>
<br>

- Apache Pulsar<br>

</details>
<details>
<summary>Clouds</summary>
<br>

- AWS<br>
- Azure<br>
- Google Cloud<br>
- OVHcloud<br>
- Scaleway<br>

</details>

## 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:

<details>
<summary>Telemetry</summary>
<br>

- LLM Latency<br>
- Error Rate<br>
- Service Request Rates<br>
- Queue Backlogs<br>
- Chunking Histogram<br>
- Error Source by Service<br>
- Rate Limit Events<br>
- CPU usage by Service<br>
- Memory usage by Service<br>
- Models Deployed<br>
- Token Throughput (Tokens/second)<br>
- Cost Throughput (Cost/second)<br>
   
</details>

## 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/)