-## The Knowledge Platform for AI
+## The Sovereign Universal AI Adapter
[](https://pypi.org/project/trustgraph/) [](https://discord.gg/sQMwkRz5GX)
-📑 [Full Docs](https://docs.trustgraph.ai/docs/TrustGraph) 📺 [YouTube](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) 🔧 [Configuration Builder](https://config-ui.demo.trustgraph.ai/) ⚙️ [API Docs](docs/apis/README.md) 🧑💻 [CLI Docs](docs/cli/README.md) 💬 [Discord](https://discord.gg/sQMwkRz5GX) 📖 [Blog](https://blog.trustgraph.ai/subscribe)
+[Full Docs](https://docs.trustgraph.ai/docs/TrustGraph) | [YouTube](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) | [Configuration Builder](https://config-ui.demo.trustgraph.ai/) | [API Docs](docs/apis/README.md) | [CLI Docs](docs/cli/README.md) | [Discord](https://discord.gg/sQMwkRz5GX) | [Blog](https://blog.trustgraph.ai/subscribe)
-**TrustGraph streamlines the delivery and management of knowledge to AI, acting as a comprehensive knowledge platform for your containerized AI tools, pipelines, and integrations.**
-
-Deploying state-of-the-art AI requires managing a complex web of models, frameworks, data pipelines, and monitoring tools. TrustGraph simplifies this complexity by providing a unified, open-source platform to **configure, build, and ship a complete knowledge solution** anywhere you need it – from cloud, on-prem, or edge devices.
+Take control of your data and AI future with **TrustGraph**. Universal connectors can call the latest LLMs or deploy models on your hardware. **TrustGraph** future-proofs your AI strategy with graph driven intelligence that can deploy in any environment.
---
@@ -21,41 +19,38 @@ Deploying state-of-the-art AI requires managing a complex web of models, framewo
Table of Contents
-- 🎯 [**Why TrustGraph?**](#-why-trustgraph)
-- 🚀 [**Getting Started**](#-getting-started)
-- 🔧 [**Configuration Builder**](#-configuration-builder)
-- 🔎 [**TrustRAG**](#-trustrag)
-- 🧠 [**Knowledge Cores**](#-knowledge-cores)
-- 📐 [**Architecture**](#-architecture)
-- 🧩 [**Integrations**](#-integrations)
-- 📊 [**Observability & Telemetry**](#-observability--telemetry)
-- 🤝 [**Contributing**](#-contributing)
-- 📄 [**License**](#-license)
-- 📞 [**Support & Community**](#-support--community)
+- [**Why TrustGraph?**](#why-trustgraph)
+- [**Getting Started**](#getting-started)
+- [**Configuration Builder**](#configuration-builder)
+- [**GraphRAG**](#graphrag)
+- [**Knowledge Packages**](#knowledge-packages)
+- [**Architecture**](#architecture)
+- [**Integrations**](#integrations)
+- [**Observability & Telemetry**](#observability--telemetry)
+- [**Contributing**](#contributing)
+- [**License**](#license)
+- [**Support & Community**](#support--community)
---
-## 🎯 Why TrustGraph?
+## Why TrustGraph?
-* **Unified Knowledge:** Define and deploy complete knowledge environments, including models, dependencies, and tooling, as a single, manageable unit.
-* **No-code TrustRAG Pipelines:** Deploy full end-to-end RAG pipelines using unique TrustGraph algorithms leveraging both Knowledge graphs and VectorDBs.
-* **Environment-Agnostic Deployment:** Provision consistently across diverse infrastructures (Cloud, On-Prem, Edge, Dev environments). Build once, provision anywhere.
-* **Trusted & Secure Delivery:** Focuses on providing a secure supply chain for AI components.
-* **Simplified Operations:** Radically reduce the complexity and time required to stand up and manage sophisticated AI stacks. Get operational faster.
-* **Open Source & Extensible:** Built with transparency and community collaboration in mind. Easily inspect, modify, and extend the platform to meet your specific provisioning needs.
-* **Component Flexibility:** Avoid component lock-in. TrustGraph integrates multiple options for all system components.
+If you want to build powerful, intelligent AI applications without getting bogged down by complex infrastructure, brittle data pipelines, or opaque "black box" systems, TrustGraph is the platform that accelerates your AI transformation by solving these core problems.
-## 🚀 Getting Started
+- **Go Beyond Basic RAG with GraphRAG**: Stop building agents that just retrieve text snippets. TrustGraph provides the tooling to automatically build and query Knowledge Graphs combined with Vector Embeddings, enabling you to create applications with deep contextual reasoning and higher accuracy.
+- **Decouple Your App from the AI Stack**: Our modular, containerized architecture lets you deploy anywhere (Docker, K8s, bare-metal) and swap out components (LLMs, vector DBs, graph DBs) without re-architecting your core application. Write your app once, knowing the underlying AI stack can evolve.
+- **Automate the Knowledge Pipeline**: Focus on building your application's logic, not on writing ETL scripts for AI. TrustGraph provides a unified platform to ingest data from silos, transform it into structured Knowledge Packages, and deliver it to your AI – streamlining the entire "knowledge supply chain."
+- **Enjoy Full Transparency & Control**: As an open-source platform, you get complete visibility into the system's inner workings. Debug more effectively, customize components to your needs, and maintain total control over your application's data flow and security, eliminating vendor lock-in.
-This is a very-quickstart. See [other installation options](docs/README.md).
+## Getting Started
+This is a very-quickstart. See [other installation options](docs/README.md#ways-to-deploy).
+
+- [Configuration Builder](#configuration-builder)
- [Install the CLI](#install-the-trustgraph-cli)
-- [Configuration Builder](#-configuration-builder)
-- [Platform Restarts](#platform-restarts)
- [Test Suite](#test-suite)
-- [Example Notebooks](#example-trustgraph-notebooks)
### Developer APIs and CLI
@@ -64,15 +59,6 @@ This is a very-quickstart. See [other installation options](docs/README.md).
- [**Python SDK**](https://trustgraph.ai/docs/api/apistarted)
- [**TrustGraph CLI**](https://trustgraph.ai/docs/running/cli)
-See the [API Developer's Guide](#api-documentation) for more information.
-
-For users, **TrustGraph** has the following interfaces:
-
-- [**Configuration Builder**](#-configuration-builder)
-- [**Test Suite**](#test-suite)
-
-The `trustgraph-cli` installs the commands for interacting with TrustGraph while running along with the Python SDK. The **Configuration Builder** enables customization of TrustGraph deployments prior to launching. The **REST API** can be accessed through port `8088` of the TrustGraph host machine with JSON request and response bodies.
-
### Install the TrustGraph CLI
```
@@ -82,145 +68,144 @@ pip3 install trustgraph-cli==
> [!CAUTION]
> The `trustgraph-cli` version *must* match the selected **TrustGraph** release version.
-## 🔧 Configuration Builder
+## Configuration Builder
-TrustGraph is endlessly customizable by editing the `YAML` resource files. The **Configuration Builder** provides a tool for building a custom configuration that deploys with your selected orchestration method in your target environment.
+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:
-- [**Configuration Builder** 🚀](https://config-ui.demo.trustgraph.ai/)
-
-The **Configuration Builder** has 5 important sections:
-
-- 🚢 **TrustGraph 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**: Customize the prompts for the LLM System, Data Extraction Agents, and Agent Flow
-- 🕵️ **Test Suite**: Add the **Test Suite** to the configuration available on port `8888`
-- 🚀 **Finish Deployment**: Download the launch `YAML` files with deployment instructions
-
-The **Configuration Builder** will generate the `YAML` files in `deploy.zip`. Once `deploy.zip` has been downloaded and unzipped, launching TrustGraph is as simple as navigating to the `deploy` directory and running:
-
-```
-docker compose up -d
-```
-
-> [!TIP]
-> Docker is the recommended container orchestration platform for first getting started with TrustGraph.
-
-When finished, shutting down TrustGraph is as simple as:
-```
-docker compose down -v
-```
-
-### Platform Restarts
-
-The `-v` flag will destroy all data on shut down. To restart the system, it's necessary to keep the volumes. To keep the volumes, shut down without the `-v` flag:
-```
-docker compose down
-```
-
-With the volumes preserved, restarting the system is as simple as:
-```
-docker compose up -d
-```
-
-All data previously in TrustGraph will be saved and usable on restart.
+- **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**: Customize the prompts for the LLM System, Data Extraction Agents, and Agent Flow
+- **Finish Deployment**: Download the launch `YAML` files with deployment instructions
### Test Suite
If added to the build in the **Configuration Builder**, the **Test Suite** will be available at port `8888`. The **Test Suite** has the following capabilities:
-- **Graph RAG Chat** 💬: Graph RAG queries in a chat interface
-- **Vector Search** 🔎: Semantic similarity search with cosine similarity scores
-- **Semantic Relationships** 🕵️: See semantic relationships in a list structure
-- **Graph Visualizer** 🌐: Visualize semantic relationships in **3D**
-- **Data Loader** 📂: Directly load `.pdf`, `.txt`, or `.md` into the system with document metadata
+- **GraphRAG Chat**: GraphRAG queries in a chat interface
+- **Vector Search**: Semantic similarity search with cosine similarity scores
+- **Semantic Relationships**: See semantic relationships in a list structure
+- **Graph Visualizer**: Visualize semantic relationships in **3D**
+- **Data Loader**: Directly load `.pdf`, `.txt`, or `.md` into the system with document metadata
-### Example TrustGraph Notebooks
+## GraphRAG
-- [**REST API Notebooks**](https://github.com/trustgraph-ai/example-notebooks/tree/master/api-examples)
-- [**Python SDK Notebooks**](https://github.com/trustgraph-ai/example-notebooks/tree/master/api-library)
+TrustGraph features an advanced GraphRAG approach that automatically constructs Knowledge Graphs with mapped Vector Embeddings to provide richer and more accurate context to LLMs for trustworthy agents.
-TrustGraph is fully containerized and is launched with a `YAML` configuration file. Unzipping the `deploy.zip` will add the `deploy` directory with the following subdirectories:
+**How TrustGraph's GraphRAG Works:**
-- `docker-compose`
-- `minikube-k8s`
-- `gcp-k8s`
+- **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.
+- **Hybrid Retrieval:** 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.
+- **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.
-> [!NOTE]
-> As more integrations have been added, the number of possible combinations of configurations has become quite large. It is recommended to use the `Configuration Builder` to build your deployment configuration. Each directory contains `YAML` configuration files for the default component selections.
+## Knowledge Packages
-**Docker**:
-```
-docker compose -f up -d
-```
+One of the biggest challenges currently facing RAG architectures is the ability to quickly reuse and integrate knowledge sets. **TrustGraph** solves this problem by storing the results of the data ingestion process in reusable Knowledge Packages. Being able to store and reuse the Knowledge Packages means the data transformation process has to be run only once. These reusable Knowledge Packages can be loaded back into **TrustGraph** and used for GraphRAG.
-**Kubernetes**:
-```
-kubectl apply -f
-```
-
-TrustGraph is designed to be modular to support as many LLMs and environments as possible. A natural fit for a modular architecture is to decompose functions into a set of modules connected through a pub/sub backbone. [Apache Pulsar](https://github.com/apache/pulsar/) serves as this pub/sub backbone. Pulsar acts as the data broker managing data processing queues connected to procesing modules.
-
-## 🔎 TrustRAG
-
-TrustGraph incorporates **TrustRAG**, an advanced RAG approach that leverages automatically constructed Knowledge Graphs to provide richer and more accurate context to LLMs. Instead of relying solely on unstructured text chunks, TrustRAG understands and utilizes the relationships *between* pieces of information.
-
-**How TrustRAG Works:**
-
-1. **Automated Knowledge Graph Construction:**
- * TrustGraph processes source data to automatically **extract key entities, topics, and the relationships** connecting them.
- * It then maps these extracted **semantic relationships and concepts to high-dimensional vector embeddings**, capturing the nuanced meaning beyond simple keyword matching.
-
-2. **Hybrid Retrieval Process:**
- * When a query is received, TrustRAG 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.
-
-3. **Context Generation via Subgraph Traversal:**
- * Based on the ranked results from the similarity search, TrustRAG dynamically **generates relevant subgraphs**.
- * It starts from the identified entry points and traverses the connections within the Knowledge Graph. Users can configure the **number of 'hops'** (relationship traversals) to expand the contextual window, gathering interconnected information.
- * 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. **TrustGraph** solves this problem by storing the results of the document ingestion process in reusable Knowledge Cores. Being able to store and reuse the Knowledge Cores means the process has to be run only once for a set of documents. These reusable Knowledge Cores can be loaded back into **TrustGraph** and used for TrustRAG.
-
-A Knowledge Core has two components:
+A Knowledge Package 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.
+When a Knowledge Package is loaded into TrustGraph, the corresponding graph edges and vector embeddings are queued and loaded into the chosen graph and vector stores.
-## 📐 Architecture
+## Architecture
-As a full-stack platform, TrustGraph provides all the stack layers needed to connect the data layer to the app layer for autonomous operations.
+The platform contains the services, stores, control plane, and API gateway needed to connect your data to intelligent agents.
-
+
-## 🧩 Integrations
-TrustGraph seamlessly integrates API services, data stores, observability, telemetry, and control flow for a unified platform experience.
+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**.
-- LLM Providers: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **LM Studio**, **Mistral**, **Ollama**, and **OpenAI**
-- Vector Databases: **Qdrant**, **Pinecone**, and **Milvus**
-- Knowledge Graphs: **Memgraph**, **Neo4j**, and **FalkorDB**
-- Data Stores: **Apache Cassandra**
-- Observability: **Prometheus** and **Grafana**
-- Control Flow: **Apache Pulsar**
+Within the **TrustGraph** Platform, the services are grouped as follows:
-### Pulsar Control Flows
+- **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 Packages* – 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).
+- **Intelligence 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.
+- **Model Orchestration:** 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).
-- For control flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module.
+## 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
+- Scaleway
+
+
+
+### Pulsar Control Plane
+
+- For flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module.
- For services such as LLMs and embeddings, Pulsar provides a client/server model. A Pulsar queue is used as the input to the service. When processed, the output is then delivered to a separate queue where a client subscriber can request that output.
-### Document Extraction Agents
-
-TrustGraph extracts knowledge documents to an ultra-dense knowledge graph using 3 automonous data extraction agents. These agents focus on individual elements needed to build the knowledge graph. The agents are:
-
-- Topic Extraction Agent
-- Entity Extraction Agent
-- Relationship Extraction Agent
-
-The agent prompts are built through templates, enabling customized data extraction agents for a specific use case. The data extraction agents are launched automatically with the loader commands.
-
PDF file:
```
tg-load-pdf
@@ -231,7 +216,7 @@ Text or Markdown file:
tg-load-text
```
-### Graph RAG Queries
+### GraphRAG Queries
Once the knowledge graph and embeddings have been built or a cognitive core has been loaded, RAG queries are launched with a single line:
@@ -250,7 +235,7 @@ tg-invoke-agent -v -q "Write a blog post on the top 3 takeaways from the documen
> [!TIP]
> Adding `-v` to the agent request will return all of the agent manager's thoughts and observations that led to the final response.
-## 📊 Observability & Telemetry
+## Observability & Telemetry
Once the platform is running, access the Grafana dashboard at:
@@ -280,11 +265,11 @@ The default Grafana dashboard tracks the following:
- Token Throughput (Tokens/second)
- Cost Throughput (Cost/second)
-## 🤝 Contributing
+## Contributing
[Developing for TrustGraph](docs/README.development.md)
-## 📄 License
+## License
**TrustGraph** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
@@ -302,7 +287,7 @@ The default Grafana dashboard tracks the following:
See the License for the specific language governing permissions and
limitations under the License.
-## 📞 Support & Community
+## Support & Community
- Bug Reports & Feature Requests: [Discord](https://discord.gg/sQMwkRz5GX)
- Discussions & Questions: [Discord](https://discord.gg/sQMwkRz5GX)
- Documentation: [Docs](https://docs.trustgraph.ai/docs/getstarted)
diff --git a/TG-future-agents.svg b/TG-future-agents.svg
deleted file mode 100644
index d4342402..00000000
--- a/TG-future-agents.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/TG-future-horizon.svg b/TG-future-horizon.svg
deleted file mode 100644
index ddc00acf..00000000
--- a/TG-future-horizon.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/TG-horizon-repo.svg b/TG-horizon-repo.svg
deleted file mode 100644
index 751e3ea5..00000000
--- a/TG-horizon-repo.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/TG-layer-diagram.svg b/TG-layer-diagram.svg
deleted file mode 100644
index 912be783..00000000
--- a/TG-layer-diagram.svg
+++ /dev/null
@@ -1,2 +0,0 @@
-
\ No newline at end of file
diff --git a/TG-platform-diagram.svg b/TG-platform-diagram.svg
new file mode 100644
index 00000000..2d4f31e0
--- /dev/null
+++ b/TG-platform-diagram.svg
@@ -0,0 +1,4 @@
+
+
+
\ No newline at end of file
diff --git a/TG_layer_diagram.svg b/TG_layer_diagram.svg
deleted file mode 100644
index 22b89ab3..00000000
--- a/TG_layer_diagram.svg
+++ /dev/null
@@ -1,2 +0,0 @@
-
\ No newline at end of file
diff --git a/tg-adapter.png b/tg-adapter.png
new file mode 100644
index 00000000..e3a8ad84
Binary files /dev/null and b/tg-adapter.png differ
diff --git a/tg-arch-diagram.svg b/tg-arch-diagram.svg
deleted file mode 100644
index b9b14cf6..00000000
--- a/tg-arch-diagram.svg
+++ /dev/null
@@ -1,29 +0,0 @@
-
\ No newline at end of file
+