Basic Memory

Titans + MIRAS: Helping AI have long-term memory

Ali Behrouz, Student Researcher, Meisam Razaviyayn, Staff Researcher, and Vahab Mirrokni, VP and Google Fellow, Google Research

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.

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%.

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.

Growth-Kinetics/DiffMem

Git Based Memory Storage for Conversational AI Agent

What does good AI memory feel like? - Cameron

Thoughts about co-3, my thinking partner

DREAM — Dynamic Retention Episodic Architecture for Memory

Modern AI systems lack persistent, user-specific episodic memory. Existing approaches rely on short-term context windows, shallow preference storage, or static conversation logs that do not scale and cannot preserve meaningful long-term continuity. This paper introduces DREAM (Dynamic Retention Episodic Architecture for Memory), a scalable, opt-in, episodic memory framework designed to work with current LLM and agent architectures. DREAM integrates episodic summarization, user-controlled opt-in memory, semantic retrieval via per-user vector indexes, an adaptive retention mechanism that expands TTL based on user engagement, and horizontal sharding of orchestrators and storage for large-scale deployments. The paper details the architecture, components, data flows, and implementation examples, and argues that DREAM provides a practical path toward AI systems capable of consistent, privacy-aligned long-term reasoning. This project has been extended with a conceptual analysis and simulation of a "DREAM-as-a-Support" (DaaS) hybrid layer. This extension demonstrates DREAM's architectural extensibility, reframing it from a standalone framework into a foundational platform component. The DaaS model provides core memory governance such as adaptive retention (ARM) and user-centric opt-in as an on-demand service to complementary cognitive systems, validating the original four-pillar design through a scalable, internal API. Reference Implementation A reference implementation of the DREAM architecture is available as an open-source Python framework:Official Reference Implementation This implementation is intended for experimentation and architectural validation and does not represent a production-ready system DREAM Architecture — Official GitHub Repository

Supermemory

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

LettaBot - Your personal AI that remembers

Your personal AI with living memory. Remembers everything. Runs on your computer. Grows with you.

Claude carries memory into work - Sensemaker

Chat and cloud Cowork now share remembered topics; continuity improves, but so can the reach of a mistake.

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.

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…

Letta

Making machines that learn. Create stateful agents that remember everything, learn continuously, and improve themselves over time.

Use Claude’s chat search and memory to build on previous context | Anthropic Help Center

You can prompt Claude to search through your previous conversations to find and reference relevant information in new chats. Claude can also remember context from your chats and carry it into new conversations and Cowork tasks. This article explains how chat search and memory work, what Claude does and doesn't remember, how to review and edit what's saved, and how to turn these features on or off.

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.