HelixDB | The Database for AI Memory

What's the best vector database for building AI products? | Liveblocks blog

Vector databases are the backbone of retrieval-augmented generation (RAG), a key technique enabling modern AI products to deliver accurate, context-aware answers from private data. This is our comprehensive comparison of leading vector databases, including Turbopuffer, Pinecone, Qdrant, pgvector, and many more.

topoteretes/cognee

Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory with small models for free

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.

Zep: A Temporal Knowledge Graph Architecture for Agent Memory

We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark. Additionally, Zep excels in more comprehensive and challenging evaluations than DMR that better reflect real-world enterprise use cases. While existing retrieval-augmented generation (RAG) frameworks for large language model (LLM)-based agents are limited to static document retrieval, enterprise applications demand dynamic knowledge integration from diverse sources including ongoing conversations and business data. Zep addresses this fundamental limitation through its core component Graphiti—a temporally-aware knowledge graph engine that dynamically synthesizes both unstructured conversational data and structured business data while maintaining historical relationships. In the DMR benchmark, which the MemGPT team established as their primary evaluation metric, Zep demonstrates superior performance (94.8% vs 93.4%). Beyond DMR, Zep’s capabilities are further validated through the more challenging LongMemEval benchmark, which better reflects enterprise use cases through complex temporal reasoning tasks. In this evaluation, Zep achieves substantial results with accuracy improvements of up to 18.5% while simultaneously reducing response latency by 90% compared to baseline implementations. These results are particularly pronounced in enterprise-critical tasks such as cross-session information synthesis and long-term context maintenance, demonstrating Zep’s effectiveness for deployment in real-world applications.

Zep: A Temporal Knowledge Graph Architecture for Agent Memory

We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark. Additionally, Zep excels in more comprehensive and challenging evaluations than DMR that better reflect real-world enterprise use cases. While existing retrieval-augmented generation (RAG) frameworks for large language model (LLM)-based agents are limited to static document retrieval, enterprise applications demand dynamic knowledge integration from diverse sources including ongoing conversations and business data. Zep addresses this fundamental limitation through its core component Graphiti—a temporally-aware knowledge graph engine that dynamically synthesizes both unstructured conversational data and structured business data while maintaining historical relationships. In the DMR benchmark, which the MemGPT team established as their primary evaluation metric, Zep demonstrates superior performance (94.8% vs 93.4%). Beyond DMR, Zep’s capabilities are further validated through the more challenging LongMemEval benchmark, which better reflects enterprise use cases through complex temporal reasoning tasks. In this evaluation, Zep achieves substantial results with accuracy improvements of up to 18.5% while simultaneously reducing response latency by 90% compared to baseline implementations. These results are particularly pronounced in enterprise-critical tasks such as cross-session information synthesis and long-term context maintenance, demonstrating Zep’s effectiveness for deployment in real-world applications.

Meilisearch: Unified Search & AI Retrieval Platform

Build lightning-fast search and AI retrieval with Meilisearch. Open-source, developer-friendly search engine trusted by 20,000+ teams worldwide.

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%.

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

IWE - Agent Memory in Plain Markdown

A local-first knowledge graph for you and your AI agents. Query markdown like a database, edit it with guarded operations, enforce structure with schemas.

Chroma - open-source search infrastructure for AI

Open-source search infrastructure for AI

Stevens: a hackable AI assistant using a single SQLite table and a handful of cron jobs

There’s a lot of hype these days around patterns for building with AI. Agents, memory, RAG, assistants—so many buzzwords! But the reality is, you don’t need fancy techniques or libraries to build useful personal tools with LLMs.

Search LibGen, the Pirated-Books Database That Meta Used to Train AI

Millions of books and scientific papers are captured in the collection’s current iteration.

Orrery, a Knowledge Graph That Adapts to Its Corpus

Orrery is our open-source knowledge graph that gets deeper as you feed it: domains that hit critical mass earn their own custom extraction specs, agents get an API, and humans get a galaxy. The whole …

JUMPERZ on Twitter / X

karpathy is showing one of the simplest AI architectures that actually works..dump research into a folder, let the model organise it into a wiki, ask questions, then file the answers back in.the real insight is the loop...every query makes the wiki better. it compounds.. now… https://t.co/vRhImgTQuN pic.twitter.com/Uq2D3ONvGT— JUMPERZ (@jumperz) April 2, 2026

AI Agent Memory: Complete Guide & Architecture

AI agent memory: what it is, how it works, architecture, memory types, and how to add persistent long-term context.