145 votes, 53 comments. Lately, I’ve been testing memory systems to handle long conversations in agent setups, optimizing for: Factual consistency…
Woah. Letta vs Mem0. (For AI memory nerds)
365 votes, 55 comments. I’m an absolute AI memory nerd, and have probably read every proposal made about memory, and demoed virtually all of the…
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.
Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues. We introduce Mem0, a scalable memory-centric architecture that addresses this issue by dynamically extracting, consolidating, and retrieving salient information from ongoing conversations. Building on this foundation, we further propose an enhanced variant that leverages graph-based memory representations to capture complex relational structures among conversational elements. Through comprehensive evaluations on LOCOMO benchmark, we systematically compare our approaches against six baseline categories: (i) established memory-augmented systems, (ii) retrieval-augmented generation (RAG) with varying chunk sizes and k-values, (iii) a full-context approach that processes the entire conversation history, (iv) an open-source memory solution, (v) a proprietary model system, and (vi) a dedicated memory management platform. Empirical results show that our methods consistently outperform all existing memory systems across four question categories: single-hop, temporal, multi-hop, and open-domain. Notably, Mem0 achieves 26% relative improvements in the LLM-as-a-Judge metric over OpenAI, while Mem0 with graph memory achieves around 2% higher overall score than the base configuration. Beyond accuracy gains, we also markedly reduce computational overhead compared to full-context method. In particular, Mem0 attains a 91% lower p95 latency and saves more than 90% token cost, offering a compelling balance between advanced reasoning capabilities and practical deployment constraints. Our findings highlight critical role of structured, persistent memory mechanisms for long-term conversational coherence, paving the way for more reliable and efficient LLM-driven AI agents.
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.
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
Letting an AI remember tripled its puzzle score - Sensemaker
OpenAI changed two conversation settings, not the model. The result shows why long-running AI tests depend on their memory setup.
The Shape of Memory Benchmarks
Why the familiar memory benchmarks are outdated, how the agent-native work looks today and why design your own.
OpenMemory - AI Memory MCP Server for Coding Agents | Mem0
With OpenMemory, add persistent, project-aware memory to Cursor, Windsurf, and VS Code agents. Store preferences, patterns, and context that get retrieved automatically.
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.
Mem0 Research Paper: Token-Efficient Memory Algorithm
Benchmarked across LoCoMo, LongMemEval, and BEAM, achieves competitive accuracy while using under 7,000 tokens per retrieval call. For comparison, full-context approaches on these benchmarks routinely consume 25,000+ tokens per query.
ChatGPT Memory and the Bitter Lesson
I reverse-engineered ChatGPT's memory system to understand what it stores, how it works (spoiler: not RAG!), and why OpenAI built it this way. Complete with prompts you can try yourself to explore your own ChatGPT memory.
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.
milla-jovovich/mempalace
The highest-scoring AI memory system ever benchmarked. And it's free.
Shlok Khemani on Twitter / X
Super interesting that eve doesn't ship with any long-term or cross-session memory (yet). The main context-management lever out of the box is conversation compaction. This is unlike OpenClaw and Hermes, each of which comes with a default opinionated memory implementation. https://t.co/DDIyc4rFtW— Shlok Khemani (@shloked) June 17, 2026
Why AI Coding Agents Forget — And How ArcticMem Fixes It
Explore ArcticMem, Snowflake’s persistent semantic memory system for AI coding agents. See how dual-tier memory improves benchmark pass rates to 73%.
