{
  "id": 6283702,
  "title": "Agent Memory Explained: Types, Tools & How to Add It",
  "url": "https://urgent.news/2026/09/08/agent-memory-explained-types-tools-how-to-add-it",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-08T14:07:00.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/mind_anthony/agent-memory-explained-types-tools-how-to-add-it-56h"
  },
  "original_language": "en",
  "account": "What is Agent Memory?\n\nAgent memory is a mechanism that enables AI agents to retain information across conversations, sessions, and tools. This information can include facts, preferences, past actions, and outcomes. Without memory, an agent cannot recall details from prior interactions, limiting its ability to provide consistent and informed responses. Memory is distinct from the model, framework, and context window.\n\nWhy Memory Matters More in 2026\n\nIn recent years, chatbots have evolved to perform multi-day tasks, hand off work between tools, and make decisions that span weeks. As these agents become more complex, the need for persistent memory increases. While context windows have expanded, allowing models to handle millions of tokens, this alone does not solve the problem of memory persistence.\n\nFour Types of Agent Memory\n\nThere are four main types of agent memory:\n\n1. Borrowed from cognitive science, this type of memory lives within the context window. It exists only for the duration of the current conversation and is lost when the call ends.\n2. Long-term / semantic memory is stored in external systems like vector databases, knowledge graphs, or document stores. This memory survives sessions and is shared across different agents.\n3. Episodic memory is a log of specific past events, usually timestamped. It remains as long as the log is kept, although it may be pruned or summarized over time.\n4. Procedural memory involves learned patterns about how to perform tasks, rather than specific events. This memory persists indefinitely and generalizes across multiple episodes.\n\nDifferentiating Memory from RAG\n\nWhile Retrieval-Augmented Generation (RAG) is often built alongside agent memory, they serve different purposes. RAG is a technique used during the generation process to fetch relevant information from memory when generating a response. Agent memory, on the other hand, is responsible for deciding what information the agent should retain, consolidate, or forget over time. RAG focuses on the retrieval mechanism, while memory encompasses both the write and read paths.\n\nLimitations of Context Windows\n\nA larger context window cannot replace memory because it is still working memory that resets with each new conversation. Even with a million-token window, the agent would forget everything unless additional information is written outside the window first. Moreover, loading entire histories into every prompt is inefficient and costly, as most production agents selectively summarize or retrieve only a portion of the information.\n\nFramework Differences in Implementing Memory\n\nThere is no universal approach to implementing agent memory. Five notable projects take different paths:\n\n1. LangGraph uses checkpointing, persisting the entire graph state after each step.\n2. Letta (MemGPT) employs an OS-inspired model, managing its own memory by paging facts between limited main context (RAM) and unlimited archival memory (disk).\n3. Mem0 acts as an extraction-and-consolidation layer, with an LLM determining what information is worth remembering and storing it with vector or graph indexing.\n4. Zep / Graphiti uses a temporal knowledge graph, where facts are nodes and edges with validity windows, enabling reasoning about what was true at different times.\n5. MCP memory servers expose memory as standard MCP tools, allowing any compatible agent to access the memory system independently of its underlying framework.\n\nAdding Memory to an Agent via MCP\n\nTo add memory to an agent using MCP, you do not need to integrate a specific framework's memory API. Instead, you point the agent at an MCP memory server, which exposes memory as standard MCP tools. The setup process typically takes about 10 minutes and involves configuring the connection between the agent and the memory server. When implementing memory, it is essential to consider the scope, which can be per-user, per-agent, or global, depending on the specific use case.",
  "summary": "Originally published on m-i-n-d.ai . I write and maintain MIND, an MCP memory server — full disclosure up front, since this piece also covers memory options that aren't ours. What Is Agent Memory? The four types, how the major frameworks differ, and how to add real memory to an agent you're building. By Anthony Conti · Astra AI, LLC · Last updated September 7, 2026 Agent memory is the mechanism…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}