How Do AI Agents Remember? A Detailed Guide from Principles to Practice

Recently, long-term memory for Agents has become very popular, and everyone is talking about memory systems.

But when you look at those explanations, most of them just throw a bunch of terms at you: vector databases, RAG, context windows, compression, episodic memory...

After reading, you still can't explain exactly how it works, can you?

It's not your fault; most articles assume you already have a foundation.

However, Agent memory systems are currently the hottest topic in interviews. If you don't understand them, you'll be at a disadvantage in both work and interviews.

So, in this post, I'll take a different approach, starting from the basics without piling up jargon! I'll try my best to make it understandable for everyone!!

I guarantee that after reading this, you'll be able to answer these three questions yourself:

What is a memory system?

How do we understand OpenClaw's memory system?

What does an enterprise-level solution look like? I chose EverOS (github.com/EverMind-AI/EverOS

This article is quite long and took me several days to write. If you have friends interested in Agent memory, you can bookmark it and forward it to them later.

Basic Knowledge About Agent Memory Systems

This section mainly covers how Agents maintain memory within a single session and across different sessions. If you already understand this, feel free to skip ahead.

First, there is no memory between two API calls to a large model. What does that mean?

For example: If you say you like eating oranges in the first call, but you don't append "I like eating oranges" to the prompt in the second call, the model will have no memory of your preference.

So how does an Agent maintain this memory during a conversation?

First, every time you ask something, the underlying system sends your entire previous chat history. The model sees it, ensuring short-term memory.

But when the chat history becomes so long that it exceeds the model's maximum context window, it compresses the history. It summarizes the current conversation history and stuffs it back into the prompt, creating space to continue the chat.

This is the principle of how a model maintains memory in a single long conversation. If you're a bit confused, look at the diagram below:

Now you know how memory is maintained in a single session, but how is it maintained between different chat sessions?

This is where the long-term memory system comes in!!

What it does is store important information in a storage space when your context is compressed or when you request to remember something.

Then, when you start a new conversation, it extracts and adds the relevant information to the prompt at the appropriate time.

By "swapping out the old for the new," it creates the illusion of remembering many things. This is similar to human working memory and long-term memory.

Alright, with this basic knowledge of memory, we can move on to understanding what a memory system is.

Below, I will give you a conceptual framework. If you finish reading it, I guarantee you'll have a basic understanding of any memory system solution.

The Memory System

There are at least dozens of solutions claiming to give Agents long-term memory. How do we study so many?

Next, I'll break down a paper to give you a basic understanding of Agent long-term memory, and then compare the differences between OpenClaw and other memory frameworks for better comprehension.

Google published a paper in November 2025 titled "Context Engineering, Sessions and Memory."

In this paper, they follow the methods of cognitive science from half a century ago, dividing Agent memory into three categories:

• Episodic Memory: What happened yesterday, what was discussed last time.
• Semantic Memory: What is your name, what do you like, what is your identity.
• Procedural Memory: How to complete a task, what the process is.

Together, these three types of memory constitute the Agent's memory.

But that's only half the story; the other half is about how to maintain and use memory.

Just like humans, Agents can't remember everything. Therefore, a memory system needs a reliable method to extract important information from conversation history and then save it.

I call this step Extraction.

In addition, we need to organize and merge memories.

For example:

Three months ago, I said I was in Dali, but later I moved to Chengdu. If this information isn't merged, the memory will contain contradictory entries.

The correct approach is to update the memory to "User is in Chengdu" after I move.

I call this step Updating.

There is also the Retrieval step, which involves many methods: keyword search, semantic search, hybrid search, or using large models to retrieve.

So, to understand a memory system, you only need to understand these two aspects:

1. How many categories of memory are there, and what does each store?
2. How is memory extracted, updated, and retrieved?

Now, using this framework, let's figure out how OpenClaw's long-term memory is implemented.

How many categories of memory does OpenClaw have, and what does each store?

Its memory is divided into the following three types:

1. memory.md (Memory): Belongs to semantic memory; stores your identity, preferences, and stable facts.

1. daily logs: Belongs to episodic memory; records what happened each day, organized by date. It only adds new entries and never deletes.

1. session snapshots: Belongs to the episodic layer; when you use the /new or /reset commands to start a new session, it summarizes the last 15 "meaningful" messages from the old conversation and saves them as a markdown file.

How are extraction, updating, and retrieval done?

Extraction occurs in three situations:

1. When a conversation is about to be compressed: Valuable information is written into the daily logs.
2. When you use /new or /reset to start a new session: Valuable information is saved to session snapshots.
3. When the user requests to remember something: The system decides which memory type to store it in.

Retrieval occurs in two situations:

1. When starting a new conversation: memory.md is automatically injected into the prompt, and it also reads today's and yesterday's daily logs for recent context.
2. When OpenClaw feels it's necessary to check memory: It calls memory search, finds the memory location via hybrid search (keywords + vectors), and then reads the file content via memory get.

When does updating happen? My personal understanding is that it happens during extraction, when deciding what to remember.

If you still don't quite understand, look at the diagram below:

Now you have some understanding of memory systems, but to be honest, OpenClaw's system has several issues:

1. It consumes a lot of tokens.
2. If the Markdown is gone, the memory disappears.
3. It often forgets things.

However, true enterprise-grade memory systems have many optimizations to ensure stability. The technology behind them is worth understanding for anyone who loves tech.

Next, I'll analyze enterprise-grade Agent memory systems!!

Enterprise-Grade Agent Memory Systems

In the AI era, every programmer should understand the technology behind enterprise-grade Agent memory systems; otherwise, you'll lose your competitive edge.

Why?

Because large models will continue to eat up our programming work. The only choice is to build supporting systems for them.

To make it easier to explain, I'll pick an open-source solution called EverOS to break down.

If you're planning to start learning Agent memory systems from this project, feel free to give it a star:

github.com/EverMind-AI/EverOS

As I said before, to understand a memory system, you only need to answer two questions.

How does EverOS answer them?

Question 1: How is memory categorized?

The general framework has 3 types, but EverOS breaks each down further, as shown below:

1. Semantic Memory Long-term memory of who you are, divided into two layers:
2. Stable Traits: You're a night owl, a programmer, live in Beijing—things that don't change for a long time.
3. Temporary States: You stayed up late today, were busy this week, had a cold last week.

1. Episodic Memory Divided into three types:
2. Episode: A condensed summary of a conversation or task, not a daily log. Example: User asked how to deploy a model, got stuck on environment variables, and spent 30 minutes on it.
3. EventLog: Extracts key facts from conversations, each with a timestamp. Example: 2026-05-10 User bought a Mac mini, 2026-05-12 User linked GitHub.
4. Foresight: Time-related "next steps"—things you said you'd do or things it infers you'll involve later, with expiration times for reminders. Example: Send the proposal before next Friday.

1. Procedural Memory Divided into two types:
2. Agent Case: After finishing a task, it records "what was intended + step-by-step actions + a quality score." Example: Sending an email—it checks contacts, drafts, asks for confirmation, then sends—this whole set is archived with a quality score.
3. Agent Skill (Distilled Skill): After doing similar tasks several times, it automatically distills a general approach from these archives, with a maturity score. The more it's done, the more reliable it becomes. Example: After 5 email tasks, it learns to check if the recipient is a key person before deciding on a formal or casual tone.

As you can see, EverOS splits the original 3 categories into 6 types, allowing for more precise storage and more effective memory.

Moreover, it's more similar to human memory—it predicts the future and summarizes/refines skills.

Question 2: How are extraction, updating, and retrieval done?

How is memory extracted?

EverOS automatically judges if "this segment is finished." Once finished, it cuts it and packs it into a memory unit.

Each unit contains 4 things:

• Plot: What was discussed and done—a condensed summary, not verbatim.
• Key Facts: What facts inside are worth noting separately.
• Foresight: Things you said you'd do or it infers you'll do, with expiration times for reminders.
• Context Tags: When, where, how credible, and what the emotion was at the time.

You just chat; it handles the segmentation details.

How is memory updated?

For example:

A month ago, you told the AI: I'm planning to start working out. Two weeks later, you said: I've been busy, haven't gone to the gym. Today you say: Forget it, I'm not working out.

Ordinary solutions pile all three into the log. Whichever one the model retrieves is what it considers the fact. But in reality, the answer should be the latest one.

EverOS relies on "Semantic Consolidation," which does three things:

• Automatically determines which is the latest (workout stopped).
• Merges duplicates or things referring to the same event.
• Maintains a user profile, separating stable preferences from temporary states (officially called Profile Evolution).

Details are shown below:

How is memory retrieved?

EverOS gives you 4 retrieval methods to choose from based on the scenario:

• Keywords: Exact matching, suitable for specific names or IDs.
• Vector Search: Semantic matching—different words with the same meaning can match.
• Hybrid: Keywords + vectors together, then filtered by a rerank model—the recommended default.
• Agentic: Used for complex multi-part questions; the LLM judges what and how to search, iterating until found (used when hybrid isn't enough).

But the 4 methods aren't the key; the key is the retrieval logic.

Ordinary solutions are passive—you give keywords, it returns matching documents, and that's it.

EverOS actively reconstructs context:

1. Analyzes what you want to do this time.
2. Activates relevant thematic scenarios.
3. Filters out expired information (e.g., preferences from a year ago might be invalid).
4. Iteratively searches until enough information is gathered.

Ordinary solutions are like a search engine that finishes after one search. EverOS repeatedly looks from different angles until it finds sufficient information.

EverOS achieved an overall accuracy of 93.05% on the long-term memory benchmark LoCoMo (using GPT-4o-mini), beating the comparison solution Zep (85.22%) by nearly 8 percentage points.

After reading this section, you should have a good idea of production-grade Agent memory systems. But how do they land in actual engineering, and what can you do with them?

Actual Production Implementation

I'll continue using this open-source project to explain for two reasons: the API is open for free, and the repository contains 20 real-world cases—perfect for discussing implementation!!

Free Open API

EverOS's Cloud API is open for free.

Three steps to get started:

1. Open everos.evermind.ai in your browser to register; the page gives you an API Key, save it.
2. Install the SDK via command line: pip install everos
3. Instantiate the client in Python and start using it.

EverOS is not only free to try, but it also supports the recently popular Skill Self-Evolution feature!!

How to use Skill Self-Evolution?

When an Agent repeatedly performs similar tasks, EverOS automatically distills the experience into reusable skills. Next time a similar task comes up, it uses the skill directly instead of starting from scratch.

Using it in code involves chaining 3 APIs:

Two points to note:

• The first time you feed a trajectory, it only generates a case (archive of a single task). Skills are only clustered and distilled after several similar tasks.
• You must use the /memories/agent endpoint; regular /memories won't extract skills.

If you don't understand the Skill Self-Evolution feature, look at the diagram below:

I've briefly mentioned the code usage, but as Agent infrastructure, this project has extremely valuable real-world use cases.

And these cases are all open-source and ready for learning!!

20 Real Use Cases

The repository README lists 20 use cases, here are a few:

• MemoCare (Alzheimer's Memory Assistant): Provides an external memory that never forgets for patients with cognitive decline—this is one of the most heartwarming public welfare projects.
• Claude Code Plugin: Adds long-term memory to Claude Code, remembering across sessions.
• Game of Thrones: Feeds GoT plots to the AI to play characters who remember who they are long-term.
• OpenHer: AI girlfriend, emotional companionship + memory evolution.
• Computer-Use with Memory: Lets the Agent control a computer and remember experiences from each operation.
• Memory Graph Visualization: Visualizes the memory system as a graph.

The full list is in the README at github.com/EverMind-AI/EverOS.

By the way, here are a few official plugins:

APIs aren't enough, so EverOS packaged memory capabilities into several out-of-the-box plugins:

• Claude Code Plugin: Adds long-term memory to Claude Code—automatically saves after each reply and recalls context for each question, with a visual Memory Hub panel. Install with one command.
• OpenClaw Plugin: Connects EverOS as a "memory slot" for OpenClaw—the Agent automatically retrieves relevant memory (plots, profiles, cases, skills) before running and saves the conversation and tool calls afterward.
• OpenClaw Skill: Connects EverOS memory tools to OpenClaw / Claude Code as "skills," allowing the Agent to call memory as needed rather than having it permanently attached.

Returning to the three questions at the beginning:

What is a memory system? How is OpenClaw's memory system? What does an enterprise-level solution look like?

You should have the answers now.

EverMind is an excellent project:

1. The entire project is Apache 2.0 open source, currently with 4500+ stars.
2. EverMind has strong academic and algorithmic roots, constantly publishing papers; their previous MSA was also a very advanced concept.
3. EverMind is an AI Native company under Shanda, with plenty of resources.

If you're planning to start learning Agent memory systems from this project, feel free to give it a star:

github.com/EverMind-AI/EverOS

They also have new products launching at the end of the month, looking forward to it!!

This is my first attempt at explaining technical concepts in an article. To make it understandable for most people, I've omitted many details.

The technology involved is complex; feel free to point out errors in the comments for correction.

If you like my article, you can bookmark, comment, forward it to friends, and follow me.