OpenClaw Local Memory System: Storing AI Memories in Markdown

At 2 AM, staring at the Claude Code chat window, I suddenly realized a problem: Does it still remember the architecture proposal I asked the AI to analyze last week? I scrolled through the chat history, only to find it was long gone. Those discussions, those details, just vanished.

You’ve surely encountered this too. AI assistants can solve problems, but their memory is as short as a goldfish’s. Once a conversation ends, what was said before is basically forgotten. Even more unsettling is—where does this conversation data go? Stored on some cloud server? Who can see it?

OpenClaw offers an interesting answer: store all AI memories in Markdown files, with data residing right on your local disk. Sounds a bit old-school, but thinking about it, this solution solves two major problems—AI gets long-term memory, and your data isn’t uploaded to the cloud.

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In this article, I want to deconstruct OpenClaw’s memory system to see how it uses simple text files to achieve persistent storage, and how it manages efficient retrieval while protecting privacy.

Why Markdown: The File-First Design Philosophy

To be honest, when I first saw OpenClaw using Markdown to store AI memories, I was a bit confused. Isn’t Markdown for writing documentation? How can it serve as a database?

But thinking carefully, this design is actually quite smart.

Think about it, if all AI memories were stored in PostgreSQL, and you wanted to see what it remembered, you’d have to open a database client, write SQL queries… just thinking about it gives me a headache. But with Markdown files? Open directly in VSCode, and everything is clear at a glance. Want to change something? Just edit and save. Want to backup? Copy the folder. Want to roll back to last week’s state? One Git command does it.

OpenClaw’s author calls this design philosophy “File-first”. Essentially, it treats Markdown files as the “Single Source of Truth”, where all data lives in files, and the database is only used for indexing and accelerating retrieval.

This concept actually aligns with the NOTES.md pattern recommended by Anthropic. Claude officials suggest developers place a NOTES.md file in the project to record key decisions and context during development, so the AI assistant can read this file every time to maintain context continuity. OpenClaw pushes this idea to the extreme—not just one file, but the entire memory system is based on Markdown.

Compare this with traditional solutions:

• Redis/In-memory DB: Good performance, but lost on restart, requiring extra persistence.
• PostgreSQL/MySQL: Powerful, but heavy, requires maintenance, data isn’t intuitive.
• Vector Databases (like Pinecone): Designed for AI, but usually cloud services, data leaves local.

The advantages of the Markdown scheme are obvious:

1. Human Readable: You can open the file anytime to see what the AI has remembered.
2. Fully Controllable: Data is on your hard drive; back it up, delete it, encrypt it as you wish.
3. Git Friendly: You can use version control to track memory changes, or even collaborate.
4. Zero Dependency: No database service needed, no Docker needed, no cloud service needed.

Of course, this solution isn’t perfect. The biggest issue is retrieval efficiency—how to quickly find relevant content among massive text files? We’ll talk about this later.

Dual-Layer Memory Architecture: Balancing Temporary and Permanent

OpenClaw’s memory system is designed quite like the human brain. Humans have short-term and long-term memory, and OpenClaw essentially has two layers: Daily Logs and Curated Knowledge.

Daily Logs are like your short-term memory—what you did today, what you just said, are all stored in log files like memory/YYYY-MM-DD.md. For example, today is February 5, 2026, OpenClaw will automatically create memory/2026-02-05.md and write all activities into it in an Append-only manner.

The smart part of this design is: OpenClaw automatically loads logs from today and the previous day. Why two days? Because this maintains immediate context continuity; the AI still remembers what you talked about yesterday. But logs further back won’t be automatically loaded, otherwise the context window would explode.

Curated Knowledge is organized long-term memory, stored in a dedicated MEMORY directory. These files are important information seamlessly extracted manually or automatically—such as project architecture documents, key decision records, common code snippets, etc.

Imagine this scenario:

memory/
├── 2026-02-01.md          # Old log, won't auto-load
├── 2026-02-04.md          # Yesterday's log, auto-loads
├── 2026-02-05.md          # Today's log, auto-loads
└── MEMORY/
    ├── project-architecture.md   # Curated knowledge, retrieved when needed
    ├── deployment-notes.md
    └── troubleshooting-guide.md

Every time the AI starts, it directly reads the last two days of log files and stuffs the content into the context window. This way, topics you discussed halfway yesterday can continue seamlessly today. But if you want to find a piece of information recorded a month ago, you need to go through the retrieval system to dig into the MEMORY directory.

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This dual-layer architecture has a very practical feature: Auto-Archiving. When too many log files accumulate, OpenClaw triggers a flush mechanism to compress or archive old logs, preventing the disk from filling up. Important information can be manually or automatically extracted to permanent storage.

Honestly, this design reminds me of the human forgetting curve. Not all memories are worth keeping forever; it’s fine for most temporary information to fade away naturally. Truly important things will precipitate into long-term memory.

Efficient Retrieval: A Hybrid Scheme with SQLite Vector Search

Okay, now the question is: if you have hundreds of Markdown files, how do you quickly find relevant content?

Relying solely on grep or full-text search is definitely not enough. You want to find “how to deploy containerized apps”, but the file says “Docker image build and K8s deployment process”—keywords don’t match, search fails. This is the limitation of pure text retrieval: it only matches literal meaning, not semantics.

OpenClaw’s solution is Hybrid Retrieval: combining keyword search (BM25 algorithm) and semantic search (vector similarity).

How exactly?

1. Indexing Layer: Build indexes using SQLite. Every time a Markdown file is written, OpenClaw cuts the content into small chunks, and then:

   • Uses SQLite’s FTS5 (Full-Text Search) engine to build a full-text index, supporting fast keyword matching.
   • Calls the Embedding API to convert text into vectors and stores them in SQLite.

2. Retrieval: When you ask “how to deploy containerized apps”, the system will:

   • BM25 search identifies text chunks containing “deploy”, “container” keywords.
   • Vector search identifies text chunks that are semantically most relevant.
   • Mixes scores from both results and returns the Top-K.

The benefit is: keyword matching is fast, semantic search is accurate. Complementing each other, they handle both precise queries and fuzzy conceptual questions.

Speaking of vector search, we have to mention the choice of Embedding models. OpenClaw supports three:

• Local Model: Completely offline, data never leaves local, but results might not be as good as API.
• OpenAI Embedding API: Good results, but uses cloud service and requires API key.
• Gemini Embedding API: Google’s solution, larger free tier.

The system automatically selects based on configuration. If you care deeply about privacy, you can use the local model; if you pursue effectiveness, you can use OpenAI or Gemini.

I’ve tried this hybrid retrieval, and it’s indeed much stronger than pure grep. Like I previously recorded a note on “Nginx reverse proxy config”, later asked “how to set up load balancing”, and it found that note, even though I didn’t write the words “load balancing” at the time. This is the power of semantic search.

Privacy and Security: Local-First Protection Mechanisms

When it comes to data storage, privacy issues are unavoidable.

Many people subconsciously avoid mentioning sensitive information when using AI assistants—company internal architecture, customer data, personal privacy, etc. Why? Because they don’t know if these conversations will be uploaded to the cloud, used to train models, or seen by third parties.

OpenClaw’s “Local-First” architecture naturally solves this problem: all memory files exist on your local disk and are not automatically uploaded anywhere. Want to backup to the cloud? Sync yourself with Dropbox or Git. Want encryption? Use VeraCrypt or FileVault yourself. It’s completely under your control.

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This design philosophy aligns with the currently popular “Local-first Software” idea—data sovereignty belongs to the user, software is just a tool.

However, local storage doesn’t represent absolute security. OpenClaw still faces some security challenges:

1. API Key Leakage: If you accidentally record API keys in memory files, and the files are synced to a public GitHub repo… that would be disastrous.
2. File System Permissions: OpenClaw needs to read/write the memory directory at runtime; improper permission configuration could be exploited by malicious programs.
3. Malicious Skills/Plugins: OpenClaw supports extended skills; installing malicious plugins could steal local data.
4. Exposed Instances: Security research has found hundreds of OpenClaw instances exposed on the public internet without authentication, accessible by anyone.

Especially the fourth point, it’s quite scary. Cisco and Vectra AI have issued warnings that many users deploy OpenClaw directly to the public internet without even basic authentication. This means hackers can directly read all your memory files, execute arbitrary commands, or even plant backdoors.

So what to do? A few security best practices:

• Docker Sandbox Execution: Run OpenClaw in a container to isolate it, limiting the scope of files it can access.
• Least Privilege Principle: Give OpenClaw only necessary file read/write permissions, don’t run as root.
• Sensitive Data Encryption: If memory files contain sensitive info, consider file-system-level encryption.
• Access Control: If you must expose to the public internet, you must configure authentication, using Nginx reverse proxy with basic auth or OAuth.
• Regular Audit: Check the memory directory for files that shouldn’t be there, check the skill list for suspicious plugins.

DigitalOcean has a very detailed security hardening deployment plan; I suggest reading it carefully.

In the end, local storage gives you the possibility of privacy protection, but whether it’s truly safe depends on how you configure and use it. It’s like giving you a lock; you have to remember to lock the door.

Practical Guide: Managing and Optimizing Memory Data

Understanding the principles, let’s talk about something practical—how to manage these memory files?

File Organization Structure

OpenClaw’s default structure looks like this:

memory/
├── 2026-02-05.md          # Daily log
├── MEMORY/                # Curated knowledge base
│   ├── projects/          # Classified by topic
│   │   ├── project-a.md
│   │   └── project-b.md
│   ├── reference/         # Reference materials
│   └── troubleshooting/   # Problem solving records
└── .memory_index.db       # SQLite index file

You can adjust the directory structure according to your needs. I personally prefer classifying by project and theme, for example:

MEMORY/
├── work/
│   ├── backend-api-design.md
│   └── database-migration-notes.md
├── learning/
│   ├── rust-ownership-model.md
│   └── kubernetes-networking.md
└── personal/
    └── recipe-collection.md

Data Maintenance Strategy

1. Regular Cleanup: Check old logs every month, delete useless ones, extract important ones to the MEMORY directory.
2. Manual Editing: It’s Markdown files after all, open and edit anytime. If you find AI remembered something wrong, just correct it.
3. Version Control: Add the memory directory to Git; commit records are the evolutionary history of memory.
4. Backup: Regularly backup to cloud or external hard drive to prevent disk failure.

Performance Optimization

If there are too many memory files, performance issues might arise. A few suggestions:

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• Control Single File Size: Recommend keeping single Markdown files under 1MB; split if too large.
• Set Context Window Reasonably: Defaults to loading the last two days of logs. If you find the context is too long causing slow response, verify and change to loading only the current day.
• Regular Index Rebuild: The SQLite index file grows with data. Periodically delete .memory_index.db to let the system rebuild it.
• Pre-compression Trigger Threshold: When log files exceed a certain number (e.g., 30 days), automatically compress or archive old files.

A small tip: You can place an INDEX.md file in the MEMORY directory, manually maintaining a directory index listing summaries and links to all important files. This way, even if the retrieval system fails, you can quickly find the information you need.

To be honest, managing memory files is a bit like organizing a notebook—it requires a bit of discipline and habit formation. But once you form your own workflow, you’ll find this much more comfortable than blindly relying on cloud services. Data in your own hands brings a solid sense of control.

Conclusion

After talking so much, let’s go back to the original question: Where should an AI assistant’s memory be stored?

OpenClaw’s answer is: local Markdown files. This solution looks a bit “retro”, but it solves two core pain points of cloud storage—data privacy and user control.

The dual-layer memory architecture (Daily Logs + Curated Knowledge) mimics human memory patterns, ensuring immediate context continuity while avoiding context window explosion. The hybrid retrieval system (BM25 + Vector Search) allows pure text storage to achieve intelligent retrieval. And the “File-first” design philosophy allows developers to manage AI memory using their most familiar tools (Editor, Git, File Manager).

Of course, this solution is not a silver bullet. It suits developers who value privacy, like local workflows, and have certain technical capabilities. If you need multi-device sync, team collaboration, or a completely maintenance-free solution, cloud services might be more suitable.

But for me, OpenClaw’s memory system gave me an important revelation: AI’s memory doesn’t have to exist in a black box; it can be transparent, controllable, and belong to you.

If you are also developing AI Agent applications, give the Markdown memory scheme a try. Start with a simple NOTES.md file and gradually build your own memory system. What matters isn’t how advanced the technology is, but who holds the data.

For more implementation details on OpenClaw, check out the official documentation and source code. The community is quite active, and problems can usually find answers there.

Oh, and remember to configure security properly. Don’t let your memory become someone else’s data.

13 min read · Published on: Feb 5, 2026 · Modified on: Feb 5, 2026

Easton

AI & Intelligence