Graph-Native Infrastructure for Context and Accountable AI Systems

A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.

Lightning-fast data access platform designed specifically for AI agents

Temporal knowledge graph for AI coding agents. 28 MCP tools, 10 runtime adapters (Claude Code, Cursor, Codex, Hermes, Continue, OpenClaw, pi, Copilot Chat, Cline, Windsurf), 682 tests, 100% adversarial verification benchmark. Prevents hallucinations, repeated mistakes, and regressions.

Native agent-graph runtime written in C++20

TrustGraph's web UI, built with React 19, TypeScript, and Vite - includes context graph UX

A simple method for keeping your context and decisions in one place when working with AI. Markdown files. Works with any model.

Thesis: The Software Collapse Has Already Happened

Agentic Air Logistics Control Plane ingests real disruption signals (FAA NAS status, METAR/TAF, NWS alerts, OpenSky ADS‑B) for airports, builds a bi-temporal context graph, and runs a deterministic (12-FSM) multi-agent state machine to emit a governed decision packet with a gateway posture

Recursive learning framework, give any AI agent a self-improvement loop with memory. No fine-tuning, just API calls

Deploy TrustGraph in an OVHcloud Kubernetes cluster using Pulumi