Memorix: Managing Memory Selections for AI Agents

Abstract

AI agents now handle a growing share of tasks across sessions within a persistent project. Their behavior depends not only on the model, but heavily on the persistent state they read from the environment. Existing systems assemble this memory at use time, splicing selected files into the prompt as a one-off payload. As a result, per-task assembly is costly and can still miss critical material, failures cannot be traced or replayed, and agents can read documentation written for the wrong version. Our key insight is that what an agent should read is mostly determined by its project, so the selection can be computed once, ahead of time, and reused across tasks. We propose Memorix, an agent memory system that realizes this insight. Memorix achieves this via three key mechanisms: (1) an immutable and versioned Store pins every source by identity and reproduces historical layouts cheaply, (2) a View that splits selection (Derivations) from composition (Environment), making the result reproducible and shareable, and (3) Quests and Profiles keep a delivered view refreshable and available inside the project as sources evolve. We implement Memorix and evaluate it on SWE-ContextBench and LibEvolutionEval against mainstream agent memory systems. Precomputed Environments raise resolved rate by up to 1.5X and deliver the correct library version on every version-conditioned task, while cutting amortized cost by up to 100X and build time by up to 38X.