博客。
Do public SKILL.md files actually make agents better?
The open skill ecosystem has a quality problem, not a supply problem. SkillCorpus filters ~821,000 crawled skills down to 96,401, pairs them with a fine-tuned retrieval stack, and tests the result across three benchmarks, two harnesses, and two backbones — improving all three, and naming the two boundaries where the gains stop.
SkillCorpus
SKILL.md
agent skills
skill curation
skill retrieval
LLM agents
SkillsBench
GDPVal
agent harness
CRAFT: learning how to fuse video tokens, not just which to drop
A single video can become tens of thousands of visual tokens, but pruning them aggressively deletes the details temporal reasoning needs. CRAFT splits the decision in two — global similarity picks which tokens merge, while position-aware weights and a channel-wise gate learn how — retaining 96.8% of backbone accuracy at roughly 8× compression.
CRAFT
video token compression
vision-language models
video VLM
KV cache
prefill cost
token merging
token pruning
temporal reasoning
Self-evolving agents have a measurement problem
Generating changes to an agent harness is the easy part. Knowing which changes genuinely helped — rather than got lucky, overfit, or never activated at all — is the hard part. HarnessBank pairs LLM-driven diagnosis with deterministic credit assignment: validity checks, activation checks, paired significance testing, and a sealed test.
self-evolving agents
agent harness
HarnessBank
credit assignment
LLM agents
agent evaluation
harness optimization
significance testing
Skill Hub: a measured foundation for community-powered agents
Skill Hub turns the scattered open SKILL.md ecosystem into a curated, searchable, and benchmarked capability layer for LLM agents — aggregating 800k+ raw skills, releasing 100k audited OSI-compliant skills, and showing statistically significant gains across four real-world benchmarks.
skillhub
skill benchmark
SKILL.md
community skills
ai agent
介绍 mRAG：EverOS 如何检索真正重要的信息
这篇文章解释了什么是 mRAG、它为何存在，以及它对你和你的代理意味着什么。
mRAG，多模态，多模态检索，RAG
介绍自我进化的智能体记忆：EverOS 如何帮助您的 AI 智能体从经验中学习
这篇文章解释了什么是 Agent Memory、它如何运作，以及这对构建真正能随着时间推移不断改进的智能体意味着什么。
自我进化的智能体记忆、智能体记忆、自我进化、智能体技能、智能体案例
突破 1 亿 Token 限制：MSA 架构为 LLM 实现高效端到端长期记忆
我们提出记忆稀疏注意力（MSA）：一种端到端可训练、可扩展的稀疏潜状态记忆框架。
长期记忆、RAG、上下文、AI 智能体、OpenClaw、稀疏注意力、Transformer、LLM、KV 缓存
EverOS：四项内存基准测试中的 SOTA 结果及其对 LLM 智能体的意义
我们已发布关于 EverOS 的最新研究，现已在 arXiv 上可查看！ 大语言模型正迅速从“单轮聊天机器人”演进为长期交互式智能体。但一旦要求智能体在跨越数周的对话中保持连贯，它就会遇到一个现实瓶颈：受限的上下文窗口和碎片化记忆。即使加入检索，许多系统的表现仍像是在提取彼此孤立的片段——常常遗漏冲突、未能更新用户状态，或在时间推移中给出不一致的建议。 在我们的最新研究中，我们提出 EverOS——一个自组织记忆操作系统，它不把记忆视为扁平存储，而是视为一个生命周期——其灵感来自生物学“engram”原理——从而使智能体能够持续将原始交互转化为结构化、不断演化的知识。
EverOS、长期记忆、RAG、上下文、LoCoMo、LongMemEval、PersonaMem
人工智能记忆系统统一评估框架
使用统一、生产级的评估框架，我们在相同的数据集、指标和答案模型下，对领先的记忆系统——EverOS、Mem0、MemOS、Zep 和 MemU——进行了基准测试。该框架为评估智能体时代真实世界的记忆性能提供了一个公平、透明且可复现的标准。而 EverOS 在 LoCoMo 和 LongMemEval 上都交出了同类最佳的结果。
AI 记忆、评估框架、EverOS、Mem0、MemU、ZEP、MemOS、LoCoMo、LongMemEval
EverMind AI 以 EverMemReRank 树立 RAG 新标准
作为 EverMemModel 模块的一部分，ReRankModel 在 2wiki 和 Hotpotqa 上取得了 SOTA 性能。
SOTA
2wiki
HotpotQA
RAG
超越 RAG：EverMemModel 通过一次性摄取整个数据库实现 SOTA
EverMemModel 在检索任务和问答任务上都达到了 SOTA 性能。
SOTA
EverOS 在 LoCoMo 上达到 SOTA 性能
EverOS 是一款智能记忆操作系统，旨在赋予 AI 不仅能够记住，还能够理解、推理和进化的能力。在 LoCoMo 基准测试中，我们基于 EverOS 构建的方法取得了 92.3% 的推理准确率（由 LLM-Judge 评估），在我们的内部评估中优于可比方法。
SOTA、LoCoMo、长期记忆
How to Edit AI Memories Across Platforms
Memory management requires platform-specific controls: ChatGPT users edit stored context via Settings > Personalization > Memory, while Claude offers no memory summary to edit at all — so rolling back across platforms means clearing each tool individually. This walkthrough covers editing, locking, and rolling back AI memories on ChatGPT, Claude, and Gemini, and where a unified memory layer replaces the per-app settings crawl.
how to edit AI memories
ChatGPT memory management
Claude context management
roll back AI memory
lock AI context
manage persistent AI memory
AI context window management
ChatGPT memory settings
Gemini memory
EverCore
EverMemOS
AI memory layer
Traceable AI Memory vs Black Box Personalization: A Transparency-First Comparison
Traceable AI memory and opaque personalization differ chiefly in whether a human can inspect the reasoning behind a result. One personalizes from an auditable record of what a user did and why a recommendation followed; the other outputs predictions from hidden model internals no one can trace. A side-by-side comparison across transparency, debuggability, bias detection, and compliance.
traceable AI memory
black box personalization
explainable AI
XAI
AI observability
GDPR Article 22
GDPR Article 17
EU AI Act
AI auditability
user data control
bias detection
AI memory architecture
EverOS
EverMind vs Cognee: A Benchmark-Backed AI Agent Memory Comparison
EverMind is an Apache 2.0 memory layer that stores agent memory in Markdown, SQLite, and LanceDB with no MongoDB, Elasticsearch, or Redis dependency; Cognee is an open-source GraphRAG framework that structures ingested data into a traversable knowledge graph. Written for developers and technical decision-makers choosing an agent memory layer, this comparison covers memory architecture, benchmark accuracy, latency and scalability, developer experience, pricing, and use-case fit.
EverMind
Cognee
agent memory
GraphRAG
knowledge graph memory
AI memory comparison
LoCoMo
LongMemEval
HaluMem
BEAM benchmark
LanceDB
SQLite
Apache 2.0
self-hosted agent memory
EverMind vs MemoryLake: Which Wins?
Four criteria separate serious contenders from marketing claims: benchmark performance, session continuity, fact freshness, and integration model. This comparison weighs EverMind and MemoryLake against a stated evaluation framework — LoCoMo accuracy, cross-session persistence, retrieval speed under load, and integration effort — rather than vendor promises.
EverMind vs MemoryLake
AI agent memory comparison
LoCoMo benchmark
memory API for AI agents
AI memory infrastructure
AI agent memory layer
persistent memory for AI agents
MemoryLake AI memory
EverMind AI memory
LongMemEval
HaluMem
PersonaMem v2
EverMind vs Dume: Which AI Memory Tool Should You Choose?
EverMind is a self-hosted, Apache 2.0 memory infrastructure layer that stores agent memory as portable, editable Markdown backed by SQLite and LanceDB; Dume is a fully managed SaaS AI assistant that automates email, calendar, and tasks across 50+ tool integrations. Written for teams deciding between a portable memory layer and a ready-made assistant, this comparison covers memory architecture, benchmark accuracy, pricing, integrations, privacy, and migration.
EverMind
Dume
Dume.ai
AI memory
agent memory
AI assistant
self-hosted memory
memory architecture
LoCoMo
LongMemEval
HaluMem
Apache 2.0
workflow automation
EverMind vs Redis Agent Memory Server: Which AI Agent Memory Layer Should You Use?
EverMind is a purpose-built, Apache 2.0 memory layer that stores agent memory in Markdown, SQLite, and LanceDB with no Redis dependency; Redis Agent Memory Server is an open-source, self-hosted memory service built on Redis infrastructure you operate yourself. Written for developers and technical decision-makers choosing an agent memory layer, this comparison covers session and long-term memory, retrieval quality, deployment model, pricing, and migration.
EverMind
Redis Agent Memory Server
agent memory
Redis vector search
self-hosted agent memory
AI memory comparison
long-term memory
LanceDB
LongMemEval
LoCoMo
EverMind vs Cloudflare Agent Memory: Which Agent Memory Should You Choose?
EverMind is a portable, Apache 2.0 open-source memory framework deployable on any infrastructure; Cloudflare Agent Memory is a fully managed, Cloudflare-native service with no self-host option. Written for developers and technical decision-makers evaluating persistent memory infrastructure for AI agents, this comparison covers memory architecture, integration effort, pricing and lock-in, and use-case fit.
EverMind
Cloudflare Agent Memory
agent memory
Cloudflare Workers
self-hosted agent memory
AI memory comparison
long-term memory
context rot
retrieval accuracy
AI Memory Hub: AI Solutions for Business Productivity and Financial
An AI memory hub is durable, retrievable awareness that persists across sessions — not a bigger context window. This guide explains what a memory hub actually stores, how layered agent memory architecture works, and how to choose between open-source self-hosting and a managed cloud path.
AI memory hub
AI memory
agent memory
memory architecture
agent memory framework
persistent memory for AI
long-term memory AI agents
retrieval augmented generation
context window vs memory
EverOS
Claude Memory Alternatives: Portable, Cross-Model Memory Systems Compared
Claude Memory helps Claude retain context across sessions, but it stays tied to Anthropic's ecosystem and gives teams no fully portable memory layer. This guide compares the leading Claude Memory alternatives — Evermind EverOS, Mem0, Zep, Letta, Supermemory, Cognee, Hindsight, and Backboard — on portability, control, cost, and best-fit use case.
Claude Memory alternatives
Claude Memory
AI memory alternatives
agent memory
cross-model memory
Evermind EverOS
Mem0
Zep
Letta
self-hosted memory
ByteRover Alternatives: Free & Open-Source Memory Layers for AI Coding Agent
ByteRover is an MCP memory server that gives AI coding agents persistent memory across sessions. This guide compares ByteRover with the leading free, open-source, and hosted alternatives, highlighting where each tool performs best and which type of developer or team it is designed for.
ByteRover alternative
ByteRover alternatives
open-source memory layer
AI agent memory
MCP memory server
agent memory
persistent memory
EverOS
Mem0
Zep
self-hosted memory
Shared Memory AI Agents: Connect Across Platforms in Claude
Local memory layer solves this: connect Claude, ChatGPT, Cursor, and other agents to one shared local memory store instead of separate walled-off boxes. Configuring a live read/write memory layer (via MCP or similar) lets every tool access identical context, eliminating repeated explanations across all five-plus AI apps.
shared memory AI agents
cross-tool AI memory
MCP memory server
persistent AI memory
unified AI context
Claude Cursor ChatGPT memory sync
EverMemOS
EverOS
agent memory
MCP
Your Personal AI Memory: Own Your Digital Mind
Personal AI memory belongs to a user when it lives outside a closed chat log and stays under that person's control. EverMemOS, from Evermind AI, structures durable memory through hierarchical extraction rather than raw retrieval, achieving a notable share accuracy on LoCoMo. Its open-source self-hosting option lets privacy-conscious professionals retain full ownership of stored context.
personal AI memory
private AI assistant
data ownership AI
local-first AI assistant
AI memory privacy
AI memory stack
portable AI memory
own your AI data
EverMemOS
EverMemOS Cloud
How to Build an AI Twin That Actually Remembers You
Memory-persistent AI twins require pairing a trained avatar—like Captions' Mirage Avatar X, built from a short recording for under $30—with a continuously updated knowledge base of your conversations, preferences, and past interactions, ensuring recall rather than one-off cloning of just face and voice.
AI twin
AI digital twin
personal AI with memory
long-term memory AI
AI memory architecture
memory operating system
EverMemOS
EverOS
persistent memory
self-evolving agent memory
What Your Personal AI Digital Twin Can Do
A personal AI digital twin functions as a persistent, memory-driven agent that retains context across meetings, sessions, and platforms, acting on a professional's behalf. Built on infrastructure like EverOS, powered by EverCore, these twins deliver sub-500ms retrieval and self-evolving skill memory.
personal AI digital twin
AI digital twin
digital twin
AI memory
persistent memory
EverOS
EverCore
knowledge retention
agent memory
self-evolving memory
LangMem Alternatives: The Best AI Agent Memory Tools Compared
LangMem's tight LangChain coupling, narrow memory-type coverage, and incomplete self-hosting documentation drive developers toward purpose-built alternatives.
AI memory
The Best Mastra Alternatives for Building Production AI Agents
AI agent
EverOS
What Is a Self-Improving AI Agent?
A self-improving AI agent is an autonomous system that measurably increases its own performance over time by learning from its outcomes - via feedback, memory, or parameter updates - rather than staying fixed after deployment.
AI agent
EverOS
What Is an AI Agent Harness?
An AI agent harness is the surrounding software layer that wraps a large language model. It supplies the tool access, context, memory, and control logic the model needs to act as a reliable working agent.
Agent harness
EverOS
如何通过 EverOS 记忆集成向我的 LangGraph 智能体添加长期记忆
本全面指南涵盖了 EverOS 集成的各个方面，包括其核心功能、具体实施步骤，以及这为提高企业生产力所带来的优势。
LangGraph，EverOS
Why Does My AI Agent Forget Things? How to Fix Agent Amnesia with AI Memory Management
In this article, we will explore the reasons behind AI agents forgetting things, the technical challenges that contribute to this problem, and effective memory management techniques that can alleviate agent amnesia.
AI Memory
EverOS
一个能记住我告诉它的一切的AI：为专业人士解锁持久型AI记忆软件的力量
本文将深入探讨此类人工智能系统的功能、其带来的优势，以及它们如何改变各领域专业人士的生产力。
EverOS
人工智能记忆是如何工作的？理解神经网络记忆与人工智能记忆架构
本文将探讨神经网络记忆背后的机制、记忆增强神经网络 (MANN) 以及不同的人工智能记忆架构。
AI 记忆
How to Build Persistent Memory for GPT-4 Agents: Effective GPT-4 Memory Management and AI Agent Statefulness
Throughout this article, we will explore various methods and strategies for developing and utilizing persistent memory in AI agents.
GPT-4
EverOS
Best AI Memory Systems in 2026: Why the Future Belongs to Agentic Memory OS
AI memory is evolving from retrieval plugins into agentic operating-system infrastructure. Explore the leading architectures of 2026, the benchmark evidence behind EverOS, and why multi-party memory, agentic retrieval, Cases, and Skills matter more than a longer context window.
AI memory systems
AI agent memory
long-term memory for AI agents
agentic memory operating system
Agentic Memory OS
multi-agent memory
shared agent memory
agent memory API
self-evolving agent memory
EverOS
EverMind
persistent AI memory
RAG vs memory
What Is Context Compression for LLMs: Optimizing Memory and Long Context Handling
This article delves into the importance of context compression, its associated challenges, and the various techniques that underpin these methodologies.
LLM
EverOS
如何为我的 LangGraph 智能体添加长期记忆：Evermind AI 记忆集成指南
本指南将探讨 LangGraph 智能体中长期记忆的概念、其重要性，以及如何利用 Evermind AI 的 EverOS 高效实现它。
长期记忆，EverOS
Memory-as-a-Service for AI: How AI Memory Management Platforms Enhance Business Productivity
This article delves into how AI memory management platforms, such as Evermind AI's EverOS, facilitate this transformation by offering robust solutions that maximize productivity through contextual memory services.
AI memory
EverOS
Understanding Context Window vs Agent Memory vs Retrieval Augmented Generation: Key Differences and Business Benefits
By diving deep into each topic, we aim to provide a comprehensive overview for those seeking to optimize their AI strategies.
agent memory
2026年拥有长期记忆 strength 的10佳人工智能助手
在这份清单中，我根据内存深度、持久性、用户控制、集成功能、实用性、隐私选项以及对实际工作的契合度对这些工具进行了排序。
AI 助手，长期记忆
EverOS 与 Mem0 基准测试对比
本文深入探讨了两大平台的各个方面，分析了关键指标、用户体验和独特功能。
EverOS
Mem0
2026年面向开发者的8大最佳AI智能体内存/记忆框架
本榜单根据生产就绪度、开发者体验、检索质量、治理以及长期扩展性等实用偏好，对2026年最适合开发者的 AI Agent 记忆框架进行了排名。
智能体记忆
EverOS
Zep
Mem0
Letta
2026年最佳开源智能体内存框架
本榜单根据 Agent 原生设计、长期记忆深度、检索质量、透明度、生态契合度、部署灵活性以及采用现状，评选出了 2026 年度最佳开源 Agent 记忆框架。
智能体记忆
EverOS
Mem0
Graphiti
Letta
Mem0 vs Zep vs EverOS: Comparing AI Agent Memory Platforms for Business Productivity
How these platforms impact business performance, their key features, usability, and integration capabilities.
EverOS
agent memory
productivity
个人知识库 AI：利用 AI 知识管理工具提升生产力
本文将探讨个人知识库 AI 的概念、其核心功能，以及它如何显著提升知识管理实践。
个人知识管理，效率
个性化 AI 助手如何记住我的偏好以提升效率
本文深入探讨了 AI 如何记住偏好、其背后的技术，以及它所带来的显著好处，尤其是对金融顾问等专业人士而言。
生产力，AI 助手
ChatGPT 记住我：个性化记忆如何提升你的 AI 对话体验
本文深入探讨 ChatGPT 的记忆功能如何运作、它为用户和企业带来的好处，以及为保护用户数据而采取的隐私措施。
ChatGPT，个性化记忆
AI 助手的长期记忆：增强持久上下文与生产力
在本文中，我们将探讨 AI 长期记忆背后的机制、它为企业带来的益处，以及 Evermind AI 的 EverOS 如何支持高级记忆管理。
AI 助手，长期记忆
具备记忆的个人 AI：增强 AI 记忆管理和知识留存
读者将了解支持有效知识保留的不同类型的记忆架构、记忆增强神经网络的作用，以及 Evermind AI 的 EverOS 如何为 AI 智能体提供持久记忆。
个人 AI，AI 记忆
如何为 AI 智能体添加记忆：理解 AI 智能体记忆架构与集成
本次讨论将涵盖 AI 智能体中的关键记忆类型，包括持久记忆和上下文记忆，以及这些类型如何提升 AI 性能。
AI 智能体，智能体记忆
2026 年 AI Agent 记忆的最佳 Mem0 替代方案：全面比较
在这份全面指南中，我们介绍了 2026 年 Mem0 的五大替代方案，包括 Evermind.ai、Zep、Letta、Cognee 和 Supermemory。我们将比较它们的核心功能、定价结构、基准性能、理想应用场景和目标受众，帮助你做出明智的决定。
mem0、EverOS、Zep、Letta、Cognee、Supermemory、AI 智能体、智能体记忆
2026 年用于 AI 智能体记忆的最佳 MemGPT 替代方案：全面比较
无论你是想摆脱框架锁定、降低自我编辑记忆带来的高 token 成本，还是只是为生产环境寻找一个更可靠的记忆层，本指南都会梳理目前可用的最佳 MemGPT 替代方案。
MemGPT，EverOS
2026 年 AI 智能体记忆的最佳 Letta 替代方案：全面比较
无论你是遇到框架锁定、苦于自我编辑记忆带来的不可预测性，还是只是想寻求更高的基准测试表现，本指南都会为你拆解 2026 年 Letta 的最佳替代方案。
Letta、EverOS、Zep、mem0、Congee、长期记忆、AI 智能体
2026年AI智能体记忆的最佳 Zep 替代方案：全面比较
在这份全面指南中，我们将探讨 2026 年 Zep 的最佳替代方案——包括 Evermind.ai、Mem0、Letta、Cognee 和 LangMem——并从架构、基准性能、定价和理想应用场景等方面进行比较，帮助您做出明智的决定。
Zep、EverOS、AI 智能体、智能体记忆
智能体记忆框架：理解用于持久上下文智能的 AI 智能体记忆系统
在本文中，我们将探讨 AI 智能体记忆系统背后的机制、它们在各种应用中的重要性，以及它们如何改变业务运营。随着组织越来越依赖 AI 解决方案，理解智能体记忆框架对于充分发挥其潜力至关重要。我们将深入了解这些系统的工作方式、认知架构的作用，以及它们所带来的具体优势，尤其是在金融咨询领域。
AI 智能体，EverOS，智能体记忆
AI中的智能体记忆：AI智能体记忆管理如何增强上下文感知与持久化AI系统
本文将探讨智能体记忆的概念、它在 AI 中的重要性，以及它如何提升整体 AI 性能。此外，我们还将讨论支撑这些记忆系统的架构，以及智能体记忆的各种商业应用，尤其是在提高生产力和服务交付方面的作用。最后，我们将介绍 Evermind AI 的专有操作系统 EverOS；该系统旨在优化面向企业应用的智能体记忆功能。
智能体记忆、AI 智能体、EverOS
