High-performance open-source in-memory graph database for GraphRAG, AI memory, agentic AI, and real-time graph analytics. Cypher-compatible, built in C++.

The context development platform. Store, enrich, and retrieve structured knowledge with graph-native infrastructure, semantic retrieval, and portable context cores.

Semantica 🧠 — A framework for building semantic layers, context graphs, and decision intelligence systems with explainability and provenance.

AI agents with graph based reasoning memory, scaffolded in seconds

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.

Compass is a context engine that builds a knowledge graph of your organization's metadata, capturing entities, relationships, and lineage across systems and time, making it discoverable and queryable for both humans and AI agents.

This is the code for the paper 'RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network'.

Graph native short-term, long-term, and reasoning memory to make your claw more powerful and efficient powered by neo4j-agent-memory

privacy-first context graph engine for AI agents and human teams.

RoboSystems is a financial intelligence platform that unifies structured data, document search, and AI memory to transform complex financial data into actionable intelligence. Fork-ready with full GitHub Actions CI/CD for deploying CloudFormation infrastructure to your AWS account.

Open-source organizational memory. Self-hosted. AI-powered. Your code, your data, your brain.

A bitemporal, graph-backed memory system for AI coding agents.

The Context Operating System for AI

📥 Fetch SEC EDGAR filings and convert them into CMD+RVL Fabric command JSON for clear, provenance-first event processing.

Open source compliance infrastructure — reads your codebase, maps it to GDPR, EU AI Act and ISO 27001, generates pre-filled documents. Your source code never leaves your network.

A minimal demo that shows how a context graph (backed by Neo4j) can be used to improve LLM responses for a student-facing co-pilot.

Episodic memory and semantic cache proxy for LLM APIs with ~40% token savings

A TypeScript CLI and Codex-friendly reference implementation for ingesting EDGAR disclosures into an append-only CQRS context layer. Focused on auditability, replayability, and strict schema discipline.

Build a context infrastructure blueprint for AI agents with clear data flow, memory growth, and setup guides for personalized behavior

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