An agentic memory system that enables AI assistants to store, search, and manage persistent memories with semantic understanding using natural language instructions.
Enables AI agents to automate real desktop applications across Windows, Linux, and macOS using incremental screen perception, accessibility trees, OCR, and window management, dramatically reducing token usage compared to screenshot-per-step approaches.
Enables AI agents to see, locate UI elements, and operate any Windows desktop app through natural language, using accessibility-tree matching with optional vision-model fallback, plus an autonomous visual loop with introspection and meta-learning.
Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.
Associative key-graph memory for LLM agents — recall facts by association (recall → read_key → read_memory) instead of vector similarity alone, with persistent cross-session memory and cross-lingual keys.