project-you-apps/vector-plus-studio

How to Fix OpenClaw's Memory Search with QMD | Jose Casanova

Upgrade OpenClaw's memory search from basic SQLite to QMD — a local hybrid search engine combining BM25, vector search, and LLM re-ranking for better AI memory.

Towards infinite context windows: neural KV cache compaction | Base Labs

Working to advance and democratize open-source intelligence.

turbopuffer: fast search on object storage

Inaugural blog post about the development of turbopuffer, a search engine that uses object storage and SSD caching for cost-effective, low latency search. This post describes into the motivation behind its creation, its unique architecture, and how it significantly reduces costs for large-scale vector searches. Discover how turbopuffer is transforming search infrastructure for companies like Cursor and Suno, offering a scalable and reliable solution.

🚀 Announcing readwise-vector-db: Supercharge Your Readwise Library with Local, Semantic Search

Memory retrieval tuned to your knowledge base | Enzyme

Your users bring content. Enzyme gives your agent their conceptual landscape from the first import. 42,000+ installs. Local CLI and hosted memory workflows.

Basic Memory

AI conversations that actually remember. Never re-explain your project to your AI again. Join our Discord: https://discord.gg/tyvKNccgqN

nanomem: An Extremely Simple, Inference-Time Memory Module

nanomem is an extremely simple, user-owned memory module that casts memory management as LLM calls / agent loops on a markdown file tree. You interact with the tree with natural language commands like nanomem add <fact>, nanomem retrieve <query>, and nanomem import <chatgpt>. As such, nanomem is by design interpretable, partitionable, portable, and versioned.

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.

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 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.

LLM in a flash: Efficient Large Language Model Inference with Limited Memory

Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, especially for devices with limited DRAM capacity. This paper tackles the challenge of efficiently running LLMs that exceed the available DRAM capacity by storing the model parameters in flash memory, but bringing them on demand to DRAM. Our method involves constructing an inference cost model that takes into account the characteristics of flash memory, guiding us to optimize in two critical areas: reducing the volume of data transferred from flash and reading data in larger, more contiguous chunks. Within this hardware-informed framework, we introduce two principal techniques. First, "windowing" strategically reduces data transfer by reusing previously activated neurons, and second, "row-column bundling", tailored to the sequential data access strengths of flash memory, increases the size of data chunks read from flash memory. These methods collectively enable running models up to twice the size of the available DRAM, with a 4-5x and 20-25x increase in inference speed compared to naive loading approaches in CPU and GPU, respectively. Our integration of sparsity awareness, context-adaptive loading, and a hardware-oriented design paves the way for effective inference of LLMs on devices with limited memory.

ChatGPT will now use its 'memory' to personalize web searches | TechCrunch

ChatGPT will now use its 'memory' to personalize web searches, thanks to a new feature called Memory with Search.

Awni Hannun on Twitter / X

I love this line of research from my colleagues at Apple:Augmenting a language model with a hierarchical memory makes perfect sense for several reasons:- Intuitively the memory parameters should be accessed much less frequently than the weights responsible for reasoning. You… https://t.co/q7HQnYmdR4 pic.twitter.com/38b9q7YKCe— Awni Hannun (@awnihannun) October 6, 2025

CaviraOSS/OpenMemory

Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.

MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models

Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language perception and generation, current LLMs fundamentally lack a unified and structured architecture for handling memory. They primarily rely on parametric memory (knowledge encoded in model weights) and ephemeral activation memory (context-limited runtime states). While emerging methods like Retrieval-Augmented Generation (RAG) incorporate plaintext memory, they lack lifecycle management and multi-modal integration, limiting their capacity for long-term knowledge evolution. To address this, we introduce MemOS, a memory operating system designed for LLMs that, for the first time, elevates memory to a first-class operational resource. It builds unified mechanisms for representation, organization, and governance across three core memory types: parametric, activation, and plaintext. At its core is the MemCube, a standardized memory abstraction that enables tracking, fusion, and migration of heterogeneous memory, while offering structured, traceable access across tasks and contexts. MemOS establishes a memory-centric execution framework with strong controllability, adaptability, and evolvability. It fills a critical gap in current LLM infrastructure and lays the groundwork for continual adaptation, personalized intelligence, and cross-platform coordination in next-generation intelligent systems.