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Update README with new context and features (#987)
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Jack Colquitt
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README.md
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README.md
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@ -11,44 +11,71 @@
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<a href="https://trendshift.io/repositories/17291" target="_blank"><img src="https://trendshift.io/api/badge/repositories/17291" alt="trustgraph-ai%2Ftrustgraph | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
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# The semantic deployment platform
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# Write context once. Run agents anywhere.
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</div>
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TrustGraph is a comprehensive semantic infrastructure for agents built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control. The platform is the full stack for agentic systems: context graphs, memory, retrieval, orchestration, and inference for deterministic agent workloads.
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Stop rebuilding context from scratch. TrustGraph treats context as a holon: modular, independent Context Cores that power multi-tenant agent workflows, while naturally snapping together to form a unified, domain-wide intelligence layer. Version your context, share it across teams, and scale with full provenance aligned to semantic web standards like RDF, OWL, SKOS, and SHACL.
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The platform:
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- [x] Multi-model and multimodal database system
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- [x] Tabular/relational, key-value
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- [x] Document, graph, and vectors
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- [x] Images, video, and audio
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- [x] Context Graph engine
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- [x] Automated entity and relationship extraction
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- [x] Ontology-driven graph construction
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- [x] Graph-grounded retrieval for explainable outputs
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- [x] Automated data ingest and loading
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- [x] Quick ingest with semantic similarity retrieval
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- [x] Ontology structuring for precision retrieval
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- [x] Out-of-the-box RAG pipelines
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- [x] DocumentRAG
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- [x] GraphRAG
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- [x] OntologyRAG
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- [x] 3D GraphViz for exploring context
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- [x] Fully Agentic System
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- [x] Single or Multi Agent
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- [x] ReAct, Plan-then-Execute, and Supervisor patterns
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- [x] MCP integration
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- [x] Run anywhere
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- [x] Deploy locally with Docker
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- [x] Deploy in cloud with Kubernetes
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- [x] Support for all major LLMs
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- [x] API support for Anthropic, Cohere, Gemini, Mistral, OpenAI, and others
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- [x] Model inferencing with vLLM, Ollama, TGI, LM Studio, and Llamafiles
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- [x] Developer friendly
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- [x] REST API [Docs](https://docs.trustgraph.ai/reference/apis/rest.html)
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- [x] Websocket API [Docs](https://docs.trustgraph.ai/reference/apis/websocket.html)
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- [x] Python API [Docs](https://docs.trustgraph.ai/reference/apis/python)
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- [x] CLI [Docs](https://docs.trustgraph.ai/reference/cli/)
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<div align="center">
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## Context is a holon.
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</div>
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The philosopher Arthur Koestler coined the word holon to describe something that is simultaneously a whole in itself and a part of something larger. A fact is whole. It is also part of a domain. A domain is whole. It is also part of an organization's knowledge. An organization's knowledge is whole. It is also part of every decision an agent makes.
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AI agents break down because this structure is never built. Context gets shoved into flat text windows, scattered across vector stores, or hardwired into one-off prompts. Facts lose their relationships. Agents lose their grounding. Answers become hallucinated guesses.
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## The Problem
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When you build an AI agent today, you spend most of your time fighting context:
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- **RAG retrieves fragments, not meaning**. Chunks of text have no structure. Relationships between facts are invisible. Your agent guesses at the connections.
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- **Context is disposable**. What the agent learned in one session is gone in the next. There is no persistent, structured knowledge layer underneath.
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- **Answers aren't traceable**. You can't explain why the agent said what it said, which means you can't trust it in production.
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- **Knowledge can't be reused**. You rebuild the same context pipelines for every new project, every new agent, every new environment.
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These aren't retrieval problems. They are structural problems. Context needs to be organized, versioned, and composable — exactly the way software infrastructure is.
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## What TrustGraph Does
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TrustGraph provides the full infrastructure layer underneath your agents: knowledge ingestion, structured storage, graph-grounded retrieval, agent orchestration, and inference — all in a single private, sovereign deployment.
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At the core is a holonic system: a structured representation of your domain where entities, relationships, and evidence are first-class objects. Every agent query is grounded against these holons that marry symbolic graph structures and vector embeddings. Every answer carries provenance. Every fact is traceable.
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On top of that sits Context Cores — portable, versioned bundles of domain knowledge you can build once and ship anywhere. Treat knowledge the way you treat code: build it, test it, version it, promote it to production, and roll it back when something breaks.
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## Context Cores: Knowledge as a First-Class Artifact
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A Context Core is the deployable unit of knowledge in TrustGraph. It packages everything an agent needs to reason reliably over a domain into a single, portable artifact.
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What's inside a Context Core
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- Ontology — your domain schema and entity mappings
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- Holon — entities, relationships, and supporting evidence
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- Embeddings — vector indexes for fast semantic entry-point lookup
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- Provenance — where every fact came from, when, and how it was derived
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- Retrieval policies — traversal rules, freshness controls, authority ranking
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Context Cores decouple what agents know from how agents are deployed. Build once. Run in Docker locally, Kubernetes in production, or on any cloud. Pin a version. Roll back. Promote across environments. This is context engineering — and it works because knowledge is finally treated like the infrastructure it is.
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## The Full Stack
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TrustGraph is not a wrapper around a graph database. It is the complete backend for production agentic systems.
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- **Holonic engine**: automated entity and relationship extraction, ontology-driven graph construction, graph-grounded retrieval for explainable outputs
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- **Multi-model database**: tabular/relational, key-value, document, graph, vectors, images, video, and audio — all managed in Cassandra and S3-compatible Garage
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- **Out-of-the-box RAG pipelines**: DocumentRAG, GraphRAG, and OntologyRAG ready to deploy
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- **Fully agentic orchestration**: single or multi-agent, ReAct, Plan-then-Execute, Supervisor patterns, and MCP integration
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- **3D Knowledge Explorer**: interactive graph visualization with BFS neighborhood extraction and edge pulse animation
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- **Automated data ingest**: quick ingest with semantic similarity or ontology-structured precision retrieval
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- **Run anywhere**: Docker/Podman locally, Kubernetes in the cloud
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All major LLMs — Anthropic, Cohere, Gemini, Mistral, OpenAI, and more via API.
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vLLM, Ollama, TGI, LM Studio, and Llamafiles for fully local inferencing.
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## No API Keys Required
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@ -62,12 +89,12 @@ Everything else is included.
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- [x] Managed Multi-model storage in [Cassandra](https://cassandra.apache.org/_/index.html)
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- [x] Managed Vector embedding storage in [Qdrant](https://github.com/qdrant/qdrant)
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- [x] Managed File and Object storage in [Garage](https://github.com/deuxfleurs-org/garage) (S3 compatible)
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- [x] Managed High-speed Pub/Sub messaging fabric with [Pulsar](https://github.com/apache/pulsar)
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- [x] Managed High-speed Pub/Sub messaging fabric with [Pulsar](https://github.com/apache/pulsar) or [RabbitMQ](https://www.rabbitmq.com/)
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- [x] Complete LLM inferencing stack for open LLMs with [vLLM](https://github.com/vllm-project/vllm), [TGI](https://github.com/huggingface/text-generation-inference), [Ollama](https://github.com/ollama/ollama), [LM Studio](https://github.com/lmstudio-ai), and [Llamafiles](https://github.com/mozilla-ai/llamafile)
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## Quickstart
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There's no need to clone this repo, unless you want to build from source. TrustGraph is a fully containerized app that deploys as a set of Docker containers. To configure TrustGraph on the command line:
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No need to clone the repo unless you are building from source. TrustGraph deploys as a set of Docker containers. Configure it on the command line in one step:
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```
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npx @trustgraph/config
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@ -84,22 +111,21 @@ width="80%" controls></video>
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For a browser based configuration, try the [Configuration Terminal](https://config-ui.demo.trustgraph.ai/).
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## Watch What is a Context Graph?
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## Watch What is a Holon?
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[](https://www.youtube.com/watch?v=gZjlt5WcWB4)
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## Watch Context Graphs in Action
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## Watch Holons in Action
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[](https://www.youtube.com/watch?v=sWc7mkhITIo)
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## Getting Started with TrustGraph
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- [**Getting Started Guides**](https://docs.trustgraph.ai/getting-started)
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- [**Using the Workbench**](#workbench)
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- [**Developer APIs and CLI**](https://docs.trustgraph.ai/reference)
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- [**Deployment Guides**](https://docs.trustgraph.ai/deployment)
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## Context Graph UI
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## TrustGraph UI
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<img width="1389" height="961" alt="Image" src="https://github.com/user-attachments/assets/35c9250d-0f01-40cb-9294-1ee8fd9a1b56" />
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@ -123,134 +149,6 @@ There are 3 libraries for quick UI integration of TrustGraph services.
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- [@trustgraph/react-state](https://www.npmjs.com/package/@trustgraph/react-state)
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- [@trustgraph/react-provider](https://www.npmjs.com/package/@trustgraph/react-provider)
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## Context Cores
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Context Cores are how TrustGraph treats context like code. A Context Core is a **portable, versioned bundle of context** that you can ship between projects and environments, pin in production, and reuse across agents. It packages the “stuff agents need to know” (structured knowledge + embeddings + evidence + policies) into a single artifact, so you can treat context like code: build it, test it, version it, promote it, and roll it back. TrustGraph is built to support this kind of end-to-end context engineering and orchestration workflow.
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### What’s inside a Context Core
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A Context Core typically includes:
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- Ontology (your domain schema) and mappings
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- Context Graph (entities, relationships, supporting evidence)
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- Embeddings / vector indexes for fast semantic entry-point lookup
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- Source manifests + provenance (where facts came from, when, and how they were derived)
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- Retrieval policies (traversal rules, freshness, authority ranking)
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## Tech Stack
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TrustGraph provides component flexibility to optimize agent workflows.
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<details>
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<summary>LLM APIs</summary>
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<br>
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- Anthropic<br>
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- AWS Bedrock<br>
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- AzureAI<br>
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- AzureOpenAI<br>
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- Cohere<br>
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- Google AI Studio<br>
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- Google VertexAI<br>
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- Mistral<br>
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- OpenAI<br>
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</details>
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<details>
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<summary>LLM Orchestration</summary>
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<br>
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- LM Studio<br>
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- Llamafiles<br>
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- Ollama<br>
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- TGI<br>
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- vLLM<br>
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</details>
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<details>
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<summary>Multi-model storage</summary>
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<br>
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- Apache Cassandra<br>
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</details>
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<details>
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<summary>VectorDB</summary>
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<br>
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- Qdrant<br>
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</details>
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<details>
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<summary>File and Object Storage</summary>
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<br>
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- Garage<br>
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</details>
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<details>
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<summary>Observability</summary>
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<br>
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- Prometheus<br>
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- Grafana<br>
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- Loki<br>
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</details>
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<details>
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<summary>Data Streaming</summary>
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<br>
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- Apache Pulsar<br>
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- RabbitMQ<br>
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- Apache Kafka<br>
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</details>
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<details>
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<summary>Clouds</summary>
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<br>
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- AWS<br>
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- Azure<br>
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- Google Cloud<br>
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- OVHcloud<br>
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- Scaleway<br>
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</details>
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## Observability & Telemetry
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Once the platform is running, access the Grafana dashboard at:
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```
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http://localhost:3000
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```
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Default credentials are:
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```
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user: admin
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password: admin
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```
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The default Grafana dashboard tracks the following:
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<details>
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<summary>Telemetry</summary>
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<br>
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- LLM Latency<br>
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- Error Rate<br>
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- Service Request Rates<br>
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- Queue Backlogs<br>
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- Chunking Histogram<br>
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- Error Source by Service<br>
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- Rate Limit Events<br>
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- CPU usage by Service<br>
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- Memory usage by Service<br>
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- Models Deployed<br>
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- Token Throughput (Tokens/second)<br>
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- Cost Throughput (Cost/second)<br>
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</details>
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## Contributing
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[Developer's Guide](https://docs.trustgraph.ai/guides/building/introduction.html)
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@ -259,7 +157,7 @@ The default Grafana dashboard tracks the following:
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**TrustGraph** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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Copyright 2024-2025 TrustGraph
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Copyright 2024-2026 TrustGraph
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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