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<div align="center">
## Agentic Orchestration Platform
## Data-to-AI, Simplified.
[![PyPI version](https://img.shields.io/pypi/v/trustgraph.svg)](https://pypi.org/project/trustgraph/) [![Discord](https://img.shields.io/discord/1251652173201149994
)](https://discord.gg/sQMwkRz5GX)
🚀 [Getting Started](https://trustgraph.ai/docs/getstarted) 📺 [YouTube](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) 🧠 [Cognitive Cores](https://github.com/trustgraph-ai/catalog/tree/master/v3) ⚙️ [API Docs](docs/apis/README.md) 🧑‍💻 [CLI Docs](https://trustgraph.ai/docs/running/cli) 💬 [Discord](https://discord.gg/sQMwkRz5GX) 📖 [Blog](https://blog.trustgraph.ai/subscribe)
🚀 [Getting Started](https://trustgraph.ai/docs/getstarted) 📺 [YouTube](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) 🧠 [Knowledge Cores](https://github.com/trustgraph-ai/catalog/tree/master/v3) ⚙️ [API Docs](docs/apis/README.md) 🧑‍💻 [CLI Docs](https://trustgraph.ai/docs/running/cli) 💬 [Discord](https://discord.gg/sQMwkRz5GX) 📖 [Blog](https://blog.trustgraph.ai/subscribe)
</div>
TrustGraph empowers you to deploy reasoning AI Agents in minutes. Our Agentic Graph RAG platform allows you to leverage modular cognitive cores for complex reasoning and information retrieval, all within a scalable and reliable infrastructure. Forget lengthy development cycles – TrustGraph delivers instant reasoning.
## The AI App Problem: Everything in Between
## Key Features
Building enterprise AI applications is *hard*. You're not just connecting APIs with a protocol - you're wrangling a complex ecosystem:
- 📄 **Document Extraction**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md`
* **Data Silos:** Connecting to and managing data from various sources (databases, APIs, files) is a nightmare.
* **LLM Integration:** Choosing, integrating, and managing different LLMs adds another layer of complexity.
* **Deployment Headaches:** Deploying, scaling, and monitoring your AI application is a constant challenge.
* **Knowledge Graph Construction:** Taking raw knowledge and structuring it so it can be efficiently retrieved.
* **Vector Database Juggling:** Setting up and optimizing a vector database for efficient data retrieval is crucial but complex.
* **Data Pipelines:** Building robust ETL pipelines to prepare and transform your data is time-consuming.
* **Data Management:** As your app grows, so does the data meaning storage and retreival becomes much more complex.
* **Prompt Engineering:** Building, testing, and deploying prompts for specific use cases.
* **Reliability:** With every new connection, the complexity ramps up meaning any simple error can bring the entire system crashing down.
## What is TrustGraph?
**TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences.
![architecture](TG-layer-diagram.svg)
## The Stack Layers
- 📄 **Data Ingest**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md`
- 🪓 **Adjustable Chunking**: Choose your chunking algorithm and parameters
- 🔁 **No-code LLM Integration**: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **Ollama**, and **OpenAI**
- ☁️ **Cloud Deployments**: **AWS** and **Google Cloud**
- 📖 **Entity, Topic, and Relationship Knowledge Graphs**
- 🔢 **Mapped Vector Embeddings**
- ❔**No-code Graph RAG Queries**: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
- 🧠 **Cognitive Cores**: Modular data sets with semantic relationships that can saved and quickly loaded on demand
- 🤖 **Agent Flow**: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform Graph RAG requests
- 📖 **Automated Knowledge Graph Building**: No need for complex ontologies and manual graph building
- 🔢 **Knoweldge 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 **Pinecone**, **Qdrant**, or **Milvus**
- 🎛️ **Production-Grade** reliability, scalability, and accuracy
- 🔍 **Observability**: get insights into system performance with Prometheus and Grafana
- 🧮 **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** and **Google Cloud**
- 🪴 **Customizable and Extensible**: Tailor for your data and use cases
- 🖥️ **Configuration Portal**: Build the `YAML` configuration with drop down menus and selectable parameters
- 🕵️ **Data Workbench**: Explore your data with a 3D semantic visualizer
## Why Use TrustGraph?
* **Accelerate Development:** TrustGraph instantly connects your data and app, keeping you laser focused on your users.
* **Reduce Complexity:** Eliminate the pain of integrating disparate tools and technologies.
* **Focus on Innovation:** Spend your time building your core AI logic, not managing infrastructure.
* **Improve Data Relevance:** Ensure your LLM has access to the *right* data, at the *right* time.
* **Scale with Confidence:** Deploy and scale your AI applications reliably and efficiently.
* **Full RAG Solution:** Focus on optimizing your respones not building RAG pipelines.
## Quickstart Guide 🚀
- [Install the CLI](#install-the-trustgraph-cli)
- [Configuration Portal](#configuration-portal)
@ -149,10 +178,6 @@ docker compose -f <launch-file.yaml> up -d
kubectl apply -f <launch-file.yaml>
```
## Architecture
![architecture](TG-layer-diagram.svg)
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.
### Pulsar Workflows