After reading hundreds of papers on agentic memory and trying out every possible tool, I came to the simple conclusion that maybe we're looking at memory wrong.
Memory is just... learning. Learning about the user, the task at hand, learning insights and patterns, learning from decisions - good and bad, the feedback received. Learning from every interaction. Everything else is integration (how the agent uses these learnings) and curation (decay, pruning, deduplication).
So I built Learning Machines: A system that helps agents continuously learn from every interaction.
I started working on it dec 31, and got a basic working version yesterday. Here's the PR for those interested: [learning-machine-v0](https://github.com/agno-agi/agno/pull/5897)
Memory is just... learning. Learning about the user, the task at hand, learning insights and patterns, learning from decisions - good and bad, the feedback received. Learning from every interaction. Everything else is integration (how the agent uses these learnings) and curation (decay, pruning, deduplication).
So I built Learning Machines: A system that helps agents continuously learn from every interaction.
I started working on it dec 31, and got a basic working version yesterday. Here's the PR for those interested: [learning-machine-v0](https://github.com/agno-agi/agno/pull/5897)
This post digs into the technical details.