Persona

Supermemory

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

Pieces | Infinite Artificial Memory for your Digital Workers and Agents

Pieces is your AI companion that captures live context from browsers to IDEs and collaboration tools, manages snippets and supports multiple llms - all while processing data locally for maximum control.

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

Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First

Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When working with data,...

AI agent runs amok in Fedora and elsewhere

Agentic AI systems can be used to do a variety of things autonomously on behalf of a human user [...]

Personas - USABLE Tools

Personas, or profiles of end users, play an important role in the design process and allow developers to better understand community needs and facilitate the creation of user stories and more concrete use-cases for their products. In some of the most challenging security environments in the world, understanding the users capacity, threats, risks, and strengths can be difficult. The USABLE project has harnessed the knowledge of the digital security training community to build personas of high-risk users from around the globe. Learn more about USABLE Personas here.

Agent Memory Patterns

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

Building napkin - a memory system for agents

A decade of information retrieval and three years of agent harness engineering, poured into a local-first knowledge system that avoids vector search entirely.

Generative Agents: Interactive Simulacra of Human Behavior

Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.

Wiki Memory

Memory for agents is still early, with little to no standards. “Memory” means something different to everyone. But one common pattern is emerging: wiki memory.

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.

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.

The Shape of Memory Benchmarks

Why the familiar memory benchmarks are outdated, how the agent-native work looks today and why design your own.

Prime Intellect - The Open Stack for Self-Improving Agents

The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.

Prime Intellect - The Open Stack for Self-Improving Agents

The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.

Karpathy's LLM Wiki as Agent Memory - Agentic AI Foundation (AAIF)

At work, I’m building agents to handle various operational tasks and have found Karpathy’s LLM Wiki design to be an excellent solution for implementing most ty…