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The context backend for AI agents

Durable agent memory you can trust. Build, version, and retrieve grounded context from a context graph.

• Give agents memory that persists across sessions and deployments.
• Reduce hallucinations with grounded context retrieval
• Ship reusable, portable Context Cores (packaged context you can move between projects/environments).

The context backend:

• Multi-model and multimodal database system
  • Tabular/relational, key-value
  • Document, graph, and vectors
  • Images, video, and audio
• Automated data ingest and loading
  • Quick ingest with semantic similarity retrieval
  • Ontology structuring for precision retrieval
• Out-of-the-box RAG pipelines
  • DocumentRAG
  • GraphRAG
  • OntologyRAG
• 3D GraphViz for exploring context
• Fully Agentic System
  • Single Agent
  • Multi Agent
  • MCP integration
• Run anywhere
  • Deploy locally with Docker
  • Deploy in cloud with Kubernetes
• Support for all major LLMs
  • API support for Anthropic, Cohere, Gemini, Mistral, OpenAI, and others
  • Model inferencing with vLLM, Ollama, TGI, LM Studio, and Llamafiles
• Developer friendly
  • REST API Docs
  • Websocket API Docs
  • Python API Docs
  • CLI Docs

Quickstart

npx @trustgraph/config

TrustGraph downloads as Docker containers and can be run locally with Docker, Podman, or Minikube. The config tool will generate:

• deploy.zip with either a docker-compose.yaml file for a Docker/Podman deploy or resources.yaml for Kubernetes
• Deployment instructions as INSTALLATION.md

Quickstart.mp4

Table of Contents

• What is a Context Graph?
  
• Why TrustGraph?
  
• Getting Started
  
• Context Cores
  
• Tech Stack
  
• Observability & Telemetry
  
• Contributing
  
• License
  
• Support & Community
  

What is a Context Graph?

Why TrustGraph?

Getting Started with TrustGraph

• Getting Started Guides
• Using the Workbench
• Developer APIs and CLI
• Deployment Guides

Watch TrustGraph 101

Workbench

The Workbench provides tools for all major features of TrustGraph. The Workbench is on port 8888 by default.

• Vector Search: Search the installed knowledge bases
• Agentic, GraphRAG and LLM Chat: Chat interface for agents, GraphRAG queries, or direct to LLMs
• Relationships: Analyze deep relationships in the installed knowledge bases
• Graph Visualizer: 3D GraphViz of the installed knowledge bases
• Library: Staging area for installing knowledge bases
• Flow Classes: Workflow preset configurations
• Flows: Create custom workflows and adjust LLM parameters during runtime
• Knowledge Cores: Manage resuable knowledge bases
• Prompts: Manage and adjust prompts during runtime
• Schemas: Define custom schemas for structured data knowledge bases
• Ontologies: Define custom ontologies for unstructured data knowledge bases
• Agent Tools: Define tools with collections, knowledge cores, MCP connections, and tool groups
• MCP Tools: Connect to MCP servers

TypeScript Library for UIs

There are 3 libraries for quick UI integration of TrustGraph services.

• @trustgraph/client
• @trustgraph/react-state
• @trustgraph/react-provider

Context Cores

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.

What’s inside a Context Core

A Context Core typically includes:

• Ontology (your domain schema) and mappings
• Context Graph (entities, relationships, supporting evidence)
• Embeddings / vector indexes for fast semantic entry-point lookup
• Source manifests + provenance (where facts came from, when, and how they were derived)
• Retrieval policies (traversal rules, freshness, authority ranking)

Tech Stack

TrustGraph provides component flexibility to optimize agent workflows.

LLM APIs

• Anthropic
  
• AWS Bedrock
  
• AzureAI
  
• AzureOpenAI
  
• Cohere
  
• Google AI Studio
  
• Google VertexAI
  
• Mistral
  
• OpenAI
  

LLM Orchestration

• LM Studio
  
• Llamafiles
  
• Ollama
  
• TGI
  
• vLLM
  

Graph Storage

• Apache Cassandra (default)
  
• Neo4j
  
• Memgraph
  
• FalkorDB
  

VectorDBs

• Qdrant (default)
  
• Pinecone
  
• Milvus
  

File and Object Storage

• Garage (default)
  
• MinIO
  

Observability

• Prometheus
  
• Grafana
  

Data Streaming

• Apache Pulsar
  

Clouds

• AWS
  
• Azure
  
• Google Cloud
  
• OVHcloud
  
• Scaleway
  

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:

Telemetry

• 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

Developer's Guide

License

TrustGraph is licensed under Apache 2.0.

Copyright 2024-2025 TrustGraph

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Support & Community

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