codec-cortex

Compress agent state for LLM and SLM agents by encoding context, lessons, and working memory into a dense sigil format. This semantic compression significantly reduces token overhead during transmission and storage across AI agent networks.

Semantic state compression in AI agent communication distills context, objectives, and working memory into a dense sigil format. This layered protocol minimizes token overhead while enabling seamless state reconstruction between interacting agents.

Yes, you need the cortex library to encode and decode the agent state sigil format. You must initialize the workspace with cortex learn init and activate the codec-cortex skill to facilitate state transmission.

An AI agent learning engine distills specific lessons into general knowledge by applying second-order compression. This mechanism automatically compounds insights across sessions, reducing token overhead while preserving core semantic state.

The best way to share working memory between AI agents without exceeding token limits is using a semantic compression protocol. It encodes state into a dense sigil format, ensuring efficient transmission and reconstruction with minimal overhead.

Limitations of using a dense sigil format for AI agent state transmission include requiring specific Python libraries and the cortex dependency for encoding and decoding. This adds a setup layer before agents can communicate efficiently.