RAG Is Not Agent Memory

Memory in Agents: What, Why and How

LLM memory gives language models persistent context across sessions. Learn how it works, how it differs from RAG and context windows, and how to add LLM memory to your agents with Mem0.

Shlok Khemani on Twitter / X

It's telling that neither ChatGPT nor Claude uses RAG for their memory systems, despite having:- the most data (oldest services, highest usage, general purpose)- strong incentives for memory (user lock-in)- A+ talent and alpha on model trajectoriesMaybe RAG just doesn't…— Shlok Khemani (@shloked) September 15, 2025

mem-agent: Equipping LLM Agents with Memory Using RL

The insights and the technical report behind Mem-Agent, our 4B model for persistent memory in LLMs

Agent Memory Patterns

A short HOW TO guide for agent memory systems. Especially the difference between blocks, files and skills.

Ambient Associative Memory

Most agent memory waits to be queried. Ambient memory runs on every tool call — past lessons surface on their own, no rules list required.

The Shape of Memory Benchmarks

Why the familiar memory benchmarks are outdated, how the agent-native work looks today and why design your own.

Wiki Memory

Memory for agents is still early, with little to no standards. “Memory” means something different to everyone. But one common pattern is emerging: wiki memory.

gowtham0992/link

Local personal memory for LLM agents

I Benchmarked OpenAI Memory vs LangMem vs Letta (MemGPT) vs Mem0 for Long-Term Memory: Here’s How They Stacked Up

145 votes, 53 comments. Lately, I’ve been testing memory systems to handle long conversations in agent setups, optimizing for: Factual consistency…

Karpathy's LLM Wiki as Agent Memory - Agentic AI Foundation (AAIF)

At work, I’m building agents to handle various operational tasks and have found Karpathy’s LLM Wiki design to be an excellent solution for implementing most ty…

Memora scales agent memory to boost long-horizon productivity

AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this with a scalable memory system separating what’s stored from how it's retrieved.

Deep Agents

Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are “shallow” and fail to plan and act over longer, more complex tasks. Applications like “Deep Research”, “Manus”, and “Claude Code” have gotten around this limitation by

The rebel alliance

This blog is co-authored with Zoe Weinberg and Matt Hawes at ex/ante, and is a follow-up to our first blog post on the topic, 'You don't own your memory.' We need an open architecture that puts us in control of our memories while making their exploitation technically impossible. But how will this shift happen? In order to discover possible implementations, we must understand how our data informs LLMs. The three predominant context engineering techniques are prompt design, retrieval-augmented ...

Pieces | Infinite Artificial Memory for your Digital Workers and Agents

Pieces is your AI companion that captures live context from browsers to IDEs and collaboration tools, manages snippets and supports multiple llms - all while processing data locally for maximum control.

Utkarsh on Twitter / X

Another reason why you want an ambient memory agent is TIME. Your other coding agents understand time differently depending on who or which company writes them. Your machine however should live and breathe with you in your timeline. With Claude, I often find myself begging… pic.twitter.com/XVAvzciplY— Utkarsh (@saxenauts) May 4, 2026