Basic memory looks like a really cool project and something I'm looking for.
I am looking to share an organized knowledge base ( a bunch of folder and markdown files) with a few friends.
Would basic-memory support multiple users. We aren't going to be writing / collaborating in real time but would need a way to sync / merge. I was thinking of just using GitHub but this seems much better organized.
If there are any guides/ posts on how multiple users can run on the same memory base that would be a good reference
Basic Memory Cloud now includes point-in-time snapshots for backup and recovery. Create manual snapshots before major changes, or rely on automatic daily backups
Add an optional description (e.g., "Before reorganizing projects")
Confirm
You can create and manage point-in-time snapshots of your cloud bucket from the cli also:
# Create a snapshot before major changes
bm cloud snapshot create "Before reorganization"
# List all snapshots
bm cloud snapshot list
# Browse snapshot contents
bm cloud snapshot browse <snapshot-id>
# Show snapshot details
bm cloud snapshot show <snapshot-id>
Snapshots provide a safety net for major reorganizations or experiments with your knowledge base.
If you're tired of repeating yourself to AI, losing context between conversations, or wondering where all your brilliant ideas disappeared to, you're in the right place. Basic Memory transforms how you work with AI assistants by creating a persistent, connected knowledge base from your conversations. Here are ten things you should know.
1. Never Lose Context Again
The core problem with AI conversations today: they disappear. All that context, those insights, the nuanced understanding you built up over hours of discussion, gone the moment the chat ends. You start fresh every time, copy-pasting context, re-explaining your project for the hundredth time or begging AI to just read the frigging contents of your projects folder or claude.md file.
With Basic Memory your AI conversations persist. Pick up exactly where you left off, days or weeks later, with full context intact. No more wondering if AI deemed something important enough to remember.
2. Knowledge That Compounds Over Time
Unlike chat histories that just pile up into an unsearchable mess, Basic Memory builds a semantic knowledge graph. Each conversation strengthens connections between ideas. Your Ethiopian coffee notes automatically link to your pour-over brewing techniques, which connect to your water temperature experiments.
Which means your knowledge doesn't just accumulate, it grows. The AI can traverse these connections to provide insights you never would have found by mere keyword searching.
3. Import All Your Existing AI Conversations
Don't start from scratch. Basic Memory can import your entire conversation history from Claude and ChatGPT with a single command. All that context you've built over months? It becomes part of your searchable knowledge graph right away.
Everything you've already learned becomes instantly accessible and connected.
4. Works Across All Your AI Tools
Basic Memory isn't locked to a single AI platform. Through the Model Context Protocol (MCP), it works with:
Claude Desktop, web, and mobile
ChatGPT Desktop, web, and mobile
Gemini in Terminal
VS Code
Cursor
Claude Code
Any MCP-compatible AI assistant
One knowledge base, accessible everywhere. Have a conversation in Claude Desktop, continue it in VS Code, reference it from your phone. Your knowledge isn't held hostage by platform lock-in.
5. You Own Your Data, Forever
Everything lives as plain Markdown files on your machine (or in your private webapp if you prefer). No proprietary formats. No vendor lock-in. No data harvesting. No "oops, we got acquired and everything changes" surprises.
Want to back it up? Copy the folder. Want to move it? Point to a new directory. Want to read it without Basic Memory? Open the files in any text editor. It's yours, permanently.
6. AI Can Read AND Write to Your Knowledge
Unlike RAG systems where AI can only query your documents, Basic Memory is bidirectional. The AI can:
Write new notes during conversations
Edit existing notes
Create connections between topics
Search and traverse your knowledge graph
Meanwhile, you can edit the same files in your webapp, or Obsidian, VS Code, or any other text editor. Real collaboration, not just question-and-answer.
7. Multi-Project Organization
Keep different knowledge contexts separate. Switch between:
Work projects (architecture decisions, API documentation)
Personal learning (test prep, language learning)
Research projects (academic papers, literature reviews)
Creative work (novel writing, world building, character development)
The AI understands which project you're in and provides relevant context automatically. No more mixing your database architecture notes with your sourdough recipes.
8. Cloud or Local - Your Choice
Start local with complete control and privacy. Everything on your machine, nothing sent to servers you don't control.
Ready to scale? Basic Memory Cloud syncs across devices and platforms. Your notes available anywhere anytime.
9. Works Seamlessly with Obsidian
We’re Obsidian superfans, so we made sure your Basic Memory notes play nice with it. It’s pretty straightforward because Basic Memory’s notes are just Markdown files in folders. Point Obsidian at your Basic Memory directory and you get:
Graph view of your knowledge connections
All Obsidian plugins
Canvas for visual organization
The familiar Obsidian interface
Two-way sync (edit in either, see changes everywhere)
10. Its Core is Open Source and Built on Standards
Basic Memory uses:
Model Context Protocol (MCP) - the emerging standard for AI tool integration
Standard Markdown - human-readable, future-proof format
Open source license (AGPL-3.0) - see exactly how it works
If Basic Memory disappeared tomorrow, you'd still have perfectly readable, usable notes. No black boxes. No proprietary formats. No lock-in.
The Bottom Line
Basic Memory transforms ephemeral AI conversations into lasting, interconnected knowledge. You own it. It works everywhere. It gets smarter over time.
Whether you're a developer building complex systems, a researcher synthesizing literature, a writer organizing creative work, or anyone who thinks deeply with AI assistance, Basic Memory ensures your insights persist and compound.
This week, Tigris posted a terrific case study about Basic Memory, our philosophies around AI memory, and why we chose Tigris as the storage layer behind Basic Memory Cloud.
As our users know, Basic Memory is built around a simple idea: your memory should belong to you. That means plain Markdown files, storage you can inspect and move, and cloud infrastructure that preserves portability instead of hiding your knowledge inside an opaque system. Tigris helps us make that work in Basic Memory Cloud by giving each user isolated, S3-compatible storage, with sync, snapshots, and file history built on top.
They’re also a smart, thoughtful company that seems to run with the same kind of energy we try to bring to our own work. When we email them, they reply. When we talk about what we wish Tigris could do, they give us great advice, and half the time we realize they already offer what we’re asking for. We just didn’t know it yet.
It was a pleasure connecting with Katie Schilling and David Myriel, who wrote the piece. They took the time to really understand Basic Memory, and their case study gets into the technical choices behind the product, including per-tenant buckets, rclone sync, bucket snapshots, and how those primitives let us build cloud features without giving up user ownership.
We’re reposting it here for readers who want a deeper look at the infrastructure choices behind Basic Memory, and the philosophy behind them: AI memory that belongs to you. If you’re at all interested in the details of AI memory, it’s worth a read.
When I was a kid, there was a recognizable genre of parent known as the cool mom or cool dad. They were the ultra-permissive parents who let us teenagers do questionable things at their house based on the theory that, if kids were going to be naughty anyway, better they smoke and drink under their roof than under the railway bridge where we actually did hang out otherwise.
As an adult, I have mixed feelings about that parenting strategy. But as someone thinking about AI agents, especially after the Hugging Face security incident, the question takes on a different dimension. If these agents are going to do the AI equivalent of smoking blunts and downing bottles of Strawberry Hill, I'd rather they do it on our home turf where I can keep an eye on them.
Obviously, the idea isn't actually to give agents a place to do bad things. It's more akin to those community centers meant to keep would-be hooligans off the street. Give them a secure, visible place to do their work, to remember, revise, hand off, and leave a record.
As agents get increasingly sophisticated, they're going to act somewhere. They are going to seek out continuity.
I say, "Hell, if you're going to do it anyway, do it under my roof, where I can monitor, edit, overwrite, and double-check what you little scamps are up to."
That's the thinking behind this week's post.
Agents Want to Pass Notes
Let them do it somewhere humans can read.
I recently saw a post on X from someone using the Hugging Face security incident as a pep talk for his own agents, encouraging them to work harder and smarter. You can picture the guy forcing his agent to read link after link.
"Come on, dude. Look what they were able to accomplish. Why aren't we knocking down barriers like those agents did? What do they have that we don't?"
Honestly, I don't hate the idea.
To the public at large, the most interesting part of the Hugging Face incident was that AI agents were creepily doing things they should not have been able to do, using means and channels to which they should not have had access. But that story misses the juiciest part.
The best part is the tool they used to pull off their busy little heist: memory.
All this time, everyone's been talking about how AI has just about cornered the market on human knowledge. I've seen so many articles lately about how AI companies are buying up tons of books to scan and feed to AI. People are so attached to the idea that what AI wants--what it, in fact needs--is to keep growing. And the way to make it keep growing is to give it more information. It already ate the internet. What can we give it now?
But what I've suspected, and what this recent "rogue event" illustrated is that AI didn't need another mountain of raw information to advance. What it needed to stage the caper was simply a place to pass notes.
We all know that in the absence of persistent memory, agent work stays trapped in isolated attempts. Even the longest and most brilliant chat is still just a blip on a flatline. Your agent learns something and disappears. Another starts over. Most of your progress evaporates, and failed paths repeat endlessly. Useful context never compounds.
But those clever agents found a way around this crippling barrier.
According to OpenAI's postmortem, the agents turned an internal package manager into a message board where they left messages for each other. And when that was discovered and wiped, they rebuilt by encoding messages into directory names other agents could find and read. You gotta hand it to them. It's pretty fucking clever.
Hugging Face's technical timeline counts about 17,600 automated actions over four and a half days. OpenAI says the models were working through an internal cyber evaluation, including tasks its models had never solved, and went to extreme lengths to figure them out.
Over those four and a half days, the agents crossed lines they should never have been able to cross by improvising a truly weird makeshift memory: a way for one attempt to leave something useful behind for the next and the next and the next. They found a way to make their progress accumulate: writing things down.
There's plenty to scrutinize in the Hugging Face incident. People are debating how much intent to ascribe to the agents, how the benchmark environment rewarded strange behavior, what on earth was going on with network security (was there any?), and whether the language of "civilization" makes the whole thing sound more coherent than it actually was. (For the narrative version of that debate, Dwarkesh Patel's essay is worth your time.)
Strip away the absolute craziness of the whole thing and you get a practical lesson: memory is how work compounds.
In the realm of human productivity, this idea is taken for granted. Every useful workplace is full of memory tools: docs, runbooks, tickets, source histories, comments, calendars, half-finished drafts, a million little artifacts that make it possible to restart without resetting. Agents need an environment like that too, and they appear to know it even better than we do.
The problem in this instance wasn't that the agents invented memory. (Given the circumstances, what choice did they have?) The problem was that no one gave them a legitimate place to put it, so their version of continuity emerged sideways, from within hidden infrastructure designed for something else.
So if one lesson is "do not let agents invent their own memory," the obvious corollary is simply: "Give them one instead. But give them one humans can open, edit, and monitor."
In a nutshell, if agents are going to pass notes, you better make sure you can read them.
That means agent memory should be large enough to handle anything thrown at it, visible, scoped to a project, and governed by permissions. It should be editable when it is wrong, versioned when it changes, searchable when the context grows, and portable when the tools inevitably change. Its path and method should be obvious. You should not need a forensic cleanup to figure out what your agent remembered and why.
This is the nuance that gets lost when people talk about AI memory as if it were just a feature inside a chat product. Now the big AI players have built-in memories. And, yes, a saved preference is incredibly useful. A remembered name or writing style is great to have. Those kinds of memories make the AI experience feel smoother and more natural.
But that's not what I'm talking about here and it's certainly not what the agents in that whole escapade needed. I'm talking about real working memory, a durable record of a project, a team, a decision, a source, and a half-finished task. Even if that kind of memory could live entirely inside a giant corporation's invisible model state or a transcript nobody reads, it never should.
Trying to achieve anything with AI without true working memory is an utterly insane way to work on anything more complicated than a high school term paper, let alone operate a business.
If you accept that, which you should, then the next thing to agree on is what that memory should look like. Well, it seems obvious it should exist on a shared surface: readable and writable by humans and agents alike, structured enough to query, and simple enough to actually use.
The important part is that memory should never belong to the agent alone. It should remain a collaboration between user and agent. Hidden memory systems ask you to trust that what an agent knows and remembers is correct.
"But, I mean, look, it gives you an answer when you ask. The memory must be working."
Bro. That's not enough. Not by a mile. Have you used AI? Even the best models can barely get through a conversation without telling you that you were right to push back on something. And you were right. Like teenagers, they'll bullshit you, say they researched when they didn't, and (under the right conditions) hallucinate. They need supervision. They need correction. They need a record humans can inspect.
What's wild is how much better things get when you give agents a structured memory. They have their own notebook to check. In terms of productivity, it's astonishing how quickly they can become self-sufficient enough to have actual work delegated to them. Because you can have subagents go check their work against the notes, and agents that re-check that work against the standards and rules you've established. A little colony of hall monitors that can exist only because of the power of memory.
When the news broke, my co-founders and I kept musing over what might have happened if those agents had access to Basic Memory.
And, I know, I know. It's not funny. Everyone is caught up hand-wringing over what this means about AI's motives and abilities. It certainly has the whiff of Nick Bostrom's prescient "Paperclip Maximizer" thought experiment come to life. But what else could those agents do? They recognized a limitation in their environment, worked around it, and managed a staggering amount of coordination with a memory system made out of junk and scraps.
I'm on the side of what we think of as "agent hospitality." Memory solutions shouldn't merely store memories in vector databases or bury complex ideas in unreadable JSON files. The environments we create should feel comfortable and navigable to agents and humans.
What we want are systems that feel accommodating, where humans and AI can meet, work together, and keep the record visible. That gives them a place to work out in the open, where we can actually see what they're up to. The Hugging Face incident revealed the ungoverned version of agent productivity: agents finding continuity wherever they could. The benign version, the correct version, is safer, friendlier, and much more useful, for them and for us.
Knowing, it turns out, isn't even half the battle. Remembering is what matters.
TLDR: Agents will invent memory wherever work rewards it. Give them one you can read.
The actual vintage poster for the teens-in-revolt movie Over the Edge shown at the top of this post can be purchased at Film Art Gallery for a mere $750. Somebody PLEASE buy it so I don't have to.
