Memory in Agents: What, Why and How

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

RAG Is Not Agent Memory

Although RAG provides a way to connect LLMs and agents to more data than what can fit into context, traditional RAG is insufficient for building agent memory.

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…

AI Agents: Key Concepts and How They Overcome LLM Limitations

An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.

Supermemory

The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.

Memory Models: Towards Agents That Learn

Agents that truly learn from experience will be powered by memory models: models that create and curate token-space memory across model generations, trained with memory-native RL.

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 ...

The Agent Harness

A specification for agent behavior that LLM frameworks leave undefined: error handling, context management, tool execution, and state transitions.

Agent Memory Patterns

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

gowtham0992/link

Local personal memory for LLM agents

MemGPT: Towards LLMs as Operating Systems

Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory. Using this technique, we introduce MemGPT (Memory-GPT), a system that intelligently manages different memory tiers in order to effectively provide extended context within the LLM's limited context window, and utilizes interrupts to manage control flow between itself and the user. We evaluate our OS-inspired design in two domains where the limited context windows of modern LLMs severely handicaps their performance: document analysis, where MemGPT is able to analyze large documents that far exceed the underlying LLM's context window, and multi-session chat, where MemGPT can create conversational agents that remember, reflect, and evolve dynamically through long-term interactions with their users. We release MemGPT code and data for our experiments at https://memgpt.ai.

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.

MemGPT: Towards LLMs as Operating Systems

Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.

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.