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.
- 🎯 Why TrustGraph?
- 🚀 Getting Started
- 🔧 Configuration Builder
- 🧠 Knowledge Cores
- 📐 Architecture
- 🧩 Integrations
- 📊 Observability & Telemetry
- 🤝 Contributing
- 📄 License
- 📞 Support & Community
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:
- Scalability & Reliability: Standalone agents don't scale or offer the robustness required for business-critical operations.
- 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.
- Integration Nightmare: Connecting agents to diverse systems, data sources, and various LLMs is difficult and time-consuming.
- 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.
See the API Developer's Guide for more information.
For users, TrustGraph has the following interfaces:
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.
pip3 install trustgraph-cli==0.21.17
Note
The TrustGraph CLI version must match the desired TrustGraph release version.
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.
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
YAMLfiles 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
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.
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.mdinto the system with document metadata
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-composeminikube-k8sgcp-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.
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.
As a full-stack platform, TrustGraph provides all the stack layers needed to connect the data layer to the app layer for autonomous operations.
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
- 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.
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>
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?"
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.
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)
TrustGraph is licensed under AGPL-3.0.