I evaluated file, vector, graph and RL based memory frameworks
An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience.
In plain words
This project evaluates different memory architectures for AI agents that need to retain information across sessions. It compares three approaches: curated markdown files, automatically-mined structured stores with graph linking, and trained experience embedded in model weights. The evaluation measures performance across fixed models, structured storage methods, and agentic benchmarks to determine which memory framework works best for persistent agent learning.
written from the facts on this page · September 2026
From the sources
An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience. I measured all of them: files against a structured store under one fixed model, a store-only head-to-head across the structured lineages, and an experience bank on the agentic benchmarks where the state of the art trains memory into the weights. Three side-by-side memory shapes: a file-based index of markdown lines, a structured store of embedded units linked by a graph, and trajectories of agent experience with one successful episode ringed. An agent that only remembers within one conversation is a stranger with excellent manners: it greets you…from pinglin.tw
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