Zhihu shares its production-grade architecture for a multi-tenant Agent Memory service that combines Mem0's fact extraction and lifecycle management with MongoDB's vector and full-text search, enabling multiple specialized agents to accumulate, isolate, and retrieve long-term operational memories reliably.
This guide compares six major agent memory systems—Mem0, Graphiti, Letta, Cognee, Supermemory, and Mnemovela—across seven decision points including write timing, conflict resolution, forgetting strategies, and context assembly, helping engineers select the right memory architecture for their AI agents.
This article dissects GBrain, an open-source AI agent brain that stores knowledge as Markdown in Git, builds a zero-LLM-cost knowledge graph via regex, and achieves 49.1% P@5 retrieval precision — 31 points higher than vector search alone — through hybrid retrieval, a nightly Dream Cycle for knowledge maintenance, and a clear separation between durable world knowledge (Brain) and operational state (Memory).
This article surveys the 2026 AI Agent memory ecosystem, comparing specialized memory layers like Mem0, Zep, Letta, and Mnemovela, framework-native solutions, infrastructure offerings, and lightweight tools, with a decision framework for choosing the right cognitive architecture.
This article compares three AI agent memory management frameworks—LangMem, MemOS, and Mem0—detailing their architectures, core features, code integration patterns, and deployment models, with a feature comparison table and decision guidance for selecting the right solution based on complexity, graph memory needs, and enterprise requirements.
The article explains why AI agents need a memory layer, categorizes memory into semantic, episodic and procedural types, describes a dual‑layer design of short‑term sliding‑window and long‑term vector‑database storage, and details practical management operations (ADD, UPDATE, DELETE, NOOP) with conflict detection, TTL expiration and privacy‑compliant deletion.
mem0 is an open‑source AI memory layer that adds long‑term, cross‑session memory to LLM‑based agents, allowing them to retain user preferences, conversation history, and task progress, reducing token usage and latency while integrating with popular models via simple add/search APIs.
The article surveys memory mechanisms across major Agent Harness frameworks, classifies three memory types, evaluates each system’s implementation, highlights benchmark shortcomings, and presents Mem0 as a unified solution that overcomes capacity, retrieval, and isolation limitations.
The article explains Flink Agents' current 0.3 preview, detailing its layered architecture—from Agent definition to execution plan and runtime operators—while outlining the roadmap for Skills integration, Mem0 long‑term memory, durable execution, and observability enhancements aimed at production readiness.
This article explains why AI agents need a structured memory module, outlines three memory types from cognitive science, details short‑term and long‑term storage architectures using vector databases, and provides concrete code and management strategies—including conflict resolution, TTL expiration, and privacy compliance—to build a robust Agent Memory system.
This guide explains how to overcome the stateless nature of large language models by using the Mem0 framework together with Elasticsearch to create a persistent, vector‑searchable memory layer, covering architecture, real‑world scenarios, step‑by‑step deployment, and integration with the OpenClaw agent framework.
This article explains how the combination of Hologres, a unified real‑time data warehouse, and Mem0, an open‑source LLM memory framework, overcomes the limited context window of large language models by providing scalable, low‑latency, and cost‑effective long‑term memory for AI applications.
The article analyzes why large language models are inherently stateless, outlines a four‑layer memory architecture for AI agents—including working, situational, semantic, and procedural memory—and explains write, retrieval, and forgetting mechanisms along with current tooling such as Mem0 and Letta.
This article introduces AgentScope Java 1.0.7, showcases its new features such as Ollama integration, Agent Skill support, and Nacos‑based A2A architecture, and walks through a complete boba‑tea‑shop example that demonstrates a Supervisor‑Worker multi‑agent system, ReActAgent configuration, dynamic MCP registration, MySQL session persistence, Mem0 long‑term memory, and AutoContextMemory compression, plus quick deployment options for local, Kubernetes, and Docker environments.
This article examines the limitations of LLM short‑term context windows and compares three open‑source long‑term memory frameworks—Mem0, Graphiti, and Cognee—by detailing their architectures, storage modes, integration steps, code examples, strengths, drawbacks, and practical selection guidance for building smarter AI agents.
This tutorial explains why AI agents need durable, personalized long‑term memory, introduces the open‑source Mem0 solution, shows how Mem0 works with LLMs and vector stores, and provides step‑by‑step code to integrate Mem0 into a LangGraph workflow for adaptive, user‑specific interactions.
Mem0 is an open‑source memory‑management middleware for large language models that provides dynamic, context‑aware, and adaptive memory, outperforming traditional Retrieval‑Augmented Generation (RAG) and enabling personalized AI assistants, travel planners, and support agents with concrete Python APIs and examples.
