Python scraper based on AI
A modern platform for visual, flexible, and extensible graph-based investigations. For cybersecurity analysts and investigators.
Cross-platform, customizable ML solutions for live and streaming media.
A modular graph-based Retrieval-Augmented Generation (RAG) system
Graph-Based Captioning as one KIR program on the Togeari dataflow machine, with its engines
LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with DeepSeek Harness, Claude Code, OpenClaw, and any agent runtime.
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.
Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation
Graph-based Exploration Planner for Subterranean Environments
Multi-Agent + Automation Harness: Graph-based Workflows, MCP, Self-Improving Agents. NextJs+React
links to conference publications in graph-based deep learning
Infrahub is a graph-based data management platform with built-in version control, CI workflows, peer review, and API access. It’s purpose-built to power reliable infrastructure automation at scale.
Graph-based N-1 resilience analysis for small power networks.