Autonomous Operations Platform

📑 Docs 📺 YouTube 🧠 Knowledge Cores ⚙️ API Docs 🧑‍💻 CLI Docs 💬 Discord 📖 Blog

Transform AI agents from experimental concepts into a new paradigm of continuous operations.

The TrustGraph platform provides a robust, scalable, and reliable AI infrastructure designed for complex environments, complete with a full observability and telemetry stack. TrustGraph automates the deployment of state-of-the-art RAG pipelines using both Knowledge Graphs and Vector Databases in local and cloud environments with a unified interface to all major LLM providers.

---

• ✨ Key Features
• 🎯 Why TrustGraph?
• 🚀 Getting Started
• 🔧 Configuration Builder
• 🧠 Knowledge Cores
• 📐 Architecture
• 🧩 Integrations
• 📊 Observability & Telemetry
• 🤝 Contributing
• 📄 License
• 📞 Support & Community

---

✨ Key Features

• 📄 Data Ingest: Bulk ingest documents such as .pdf,.txt, and .md
• 📃 OCR Pipelines: OCR documents with PDF decode, Tesseract, or Mistral OCR services
• 🪓 Adjustable Chunking: Choose your chunking algorithm and parameters
• 🔁 No-code LLM Integration: Anthropic, AWS Bedrock, AzureAI, AzureOpenAI, Cohere, Google AI Studio, Google VertexAI, Llamafiles, LM Studio, Mistral, Ollama, and OpenAI
• 📖 Automated Knowledge Graph Building: No need for complex ontologies and manual graph building
• 🔢 Knowledge Graph to Vector Embeddings Mappings: Connect knowledge graph enhanced data directly to vector embeddings
• ❔Natural Language Data Retrieval: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
• 🧠 Knowledge Cores: Modular data sets with semantic relationships that can saved and quickly loaded on demand
• 🤖 Agent Manager: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform Graph RAG requests
• 📚 Multiple Knowledge Graph Options: Full integration with Memgraph, FalkorDB, Neo4j, or Cassandra
• 🧮 Multiple VectorDB Options: Full integration with Qdrant, Pinecone, or Milvus
• 🎛️ Production-Grade Reliability, scalability, and accuracy
• 📊 Observability and Telemetry: Get insights into system performance with Prometheus and Grafana
• 🎻 Orchestration: Fully containerized with Docker or Kubernetes
• 🥞 Stack Manager: Control and scale the stack with confidence with Apache Pulsar
• ☁️ Cloud Deployments: AWS, Azure, Google Cloud, and Scaleway
• 🪴 Customizable and Extensible: Tailor for your data and use cases
• 🖥️ Configuration Builder: Build the YAML configuration with drop down menus and selectable parameters
• 🕵️ Test Suite: A simple UI to fully test TrustGraph performance

🎯 Why TrustGraph?

Traditional operations involve manual intervention, siloed tools, and reactive problem-solving. While AI agents show promise, integrating them into reliable, continuous operations presents significant challenges:

1. Scalability & Reliability: Standalone agents don't scale or offer the robustness required for business-critical operations.
2. Contextual Understanding: Agents need deep, relevant context (often locked in sensitive and protectec data) to perform complex tasks effectively. RAG is powerful but complex to deploy and manage.
3. Integration Nightmare: Connecting agents to diverse systems, data sources, and various LLMs is difficult and time-consuming.
4. Lack of Oversight: Monitoring, debugging, and understanding the behavior of multiple autonomous agents in production is critical but often overlooked.

TrustGraph addresses these challenges by providing:

• A platform, not just a library, for managing the lifecycle of autonomous operations.
• Automated, best-practice RAG deployments that combine the strengths of semantic vector search and structured knowledge graph traversal.
• A standardized layer for LLM interaction and enterprise system integration.
• Built-in observability to ensure you can trust and manage your autonomous systems.

🚀 Getting Started

• Install the CLI
• Configuration Builder
• Platform Restarts
• Test Suite
• Example Notebooks

Developer APIs and CLI

• REST API
• Websocket API
• Python SDK
• TrustGraph CLI

See the API Developer's Guide for more information.

For users, TrustGraph has the following interfaces:

• Configuration Builder
• 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

pip3 install trustgraph-cli==0.21.17

Note

The TrustGraph CLI version must match the desired TrustGraph release version.

🔧 Configuration Builder

TrustGraph is endlessly customizable by editing the YAML launch files. The Configuration Builder provides a quick and intuitive tool for building a custom configuration that deploys with Docker, Podman, Minikube, AWS, Azure, Google Cloud, or Scaleway. There is a Configuration Builder for the both the lastest and stable TrustGraph releases.

• Configuration Builder (Stable 0.21.17) 🚀
• Configuration Builder (Latest 0.22.5) 🚀

The Configuration Builder has 4 important sections:

• 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.

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

Example TrustGraph Notebooks

• REST API Notebooks
• Python SDK Notebooks

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:

• docker-compose
• minikube-k8s
• gcp-k8s

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.

Docker:

docker compose -f <launch-file.yaml> up -d

Kubernetes:

kubectl apply -f <launch-file.yaml>

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 serves as this pub/sub backbone. Pulsar acts as the data broker managing data processing queues connected to procesing modules.

🧠 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 RAG.

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.

📐 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.

🧩 Integrations

TrustGraph seamlessly integrates API services, data stores, observability, telemetry, and control flow for a unified platform experience.

• 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

Pulsar Control Flows

• For control 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 <document.pdf>

Text or Markdown file:

tg-load-text <document.txt>

Graph RAG 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:

tg-invoke-graph-rag -q "What are the top 3 takeaways from the document?"

Agent Flow

Invoking the Agent Flow will use a ReAct style approach the combines Graph RAG and text completion requests to think through a problem solution.

tg-invoke-agent -v -q "Write a blog post on the top 3 takeaways from the document."

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

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:

• 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

Developing for TrustGraph

📄 License

TrustGraph is licensed under AGPL-3.0.

📞 Support & Community

• Bug Reports & Feature Requests: Discord
• Discussions & Questions: Discord
• Documentation: Docs