memory-system

Memora is a scalable memory system for AI agents that decouples storage from retrieval, enabling long-horizon tasks with up to 98% fewer context tokens while setting new state-of-the-art on benchmarks. The paper is published at ICML 2026.

Descripción detallada del sistema de memoria OpenClaw para agentes de IA, incluyendo la estructura completa del workspace, archivos de arranque, estado, tiempo, semántica y búsqueda con QMD.

This article outlines the design of a memory system that stores and retrieves facts about a codebase, intended to improve AI coding assistants' understanding and context awareness.

AtomMem introduces a long-term memory system for LLM agents that uses atomic facts as efficient memory units, organizing them into hierarchical event structures and temporal user profiles, achieving state-of-the-art on the LoCoMo benchmark.

Perplexity Brain is a memory system that builds a persistent context graph across tasks, projects, decisions, files, and sources, enabling agents to start with relevant context instead of from scratch, improving answer correctness and reducing task costs.

The author built a 3-layer memory system (project/session/source) for AI coding assistants and is seeking feedback.

The tweet highlights CNVS's cross-agent memory system, which uses append-only JSONL files for efficient memory sharing between AI agents like Claude, Codex, and Cursor, outperforming Mem0 on its own benchmark.

A developer discusses alternatives to the agentmemory library for multi-agent memory systems in coding agents, comparing Mem0 and ByteRover, and asks for confirmations on Opencode and Pi.

Sentra's Code Memory system boosts GPT-5.5 to 88.31% on Terminal-Bench 2.1 at a quarter of the cost, outperforming Anthropic's restricted Mythos 5 model. The memory layer reduces input tokens by 52% and costs by 72.6% while improving task success rates.

A developer shares a workflow using Claude Fable 5 as architect and GPT 5.5 Codex as builder, with a handoff memory system to manage AI-assisted development efficiently.

The article identifies four key flaws in current AI agent memory systems—brittleness, lack of temporal reasoning, forgetting dilemma, and evaluation gap—and presents a novel memory architecture inspired by code agents, achieving high benchmark scores while emphasizing context learning as the next challenge.

Vegvisir is an open-source AI agent harness with a focus on security, featuring a memory system, context management, and a hardware-bound secrets enclave to protect API keys.

The authors developed a collaborative multi-agent memory system with shared/private memory scopes, trust-aware retrieval, lineage tracking, and contradiction resolution, and submitted a paper to a conference.

The author introduces Noosphere, an open-source memory and wiki layer for AI agents and humans, seeking feedback on its structured Postgres-backed memory, Redis-cached recall, and human-editable wiki pages.

This is a pure Python self-evolving AI framework with 64 modules covering features like memory, knowledge graphs, and federated learning, and it runs without additional libraries.

Tencent has open-sourced TencentDB-Agent-Memory, which implements a four-layer memory system. It supports integration via the OpenClaw plugin and provides a Docker-based solution for Hermes, boosting PersonaMem accuracy from 48% to 76%.

GBrain is an AI Agent persistent memory system open-sourced by Y Combinator President Garry Tan, which uses a self-wiring knowledge graph and hybrid retrieval layer to address long-term memory and knowledge accumulation issues for Agents.

Hermes is an open-source AI agent framework that uses strictly limited memory files and a skill system to achieve a more focused and persistent agent experience. The video tutorial demonstrates how to install, configure, and use Hermes to build custom AI assistants.

Claude can now transform an entire codebase into an interactive architecture map for humans and a memory system for AI agents, allowing coding agents to instantly understand APIs, components, authentication, database flows, and dependencies.

PAI (Personal AI Infrastructure) is an open-source GitHub project that gives AI a permanent, structured memory system across sessions, with features like a digital assistant, skills, workflows, and a self-improvement loop.