Introducing Recursive Memory Harness: RLM For Agentic Memory

Applying recursive architecture to persistent AI memory. Based on Recursive Language Models (MIT CSAIL, 2025). https://arxiv.org/abs/2512.24601 https://github.com/aayoawoyemi/Ori-Mnemos By empowering LLMs to decompose, navigate, and reassemble information instead of brute-forcing it into memory, Recursive Language Models allow AI to process inputs up to a hundred times beyond what a single context window can hold. We applied that same recursive architecture to persistent memory and found that retrieval quality compounds, matching memory systems built on Redis and Qdrant cloud infrastructure with zero databases, zero cloud. Just local markdown files. Recursive Memory Harness brings us one step closer to active memory recall instead of sequential retrieval. What is RLM In December 2025, researchers at MIT CSAIL posited a simple inversion. Instead of cramming everything into a sequential, linear context window that gets loaded fresh for every single query, you treat the data as an environ