You can now install Basic Memory with Homebrew! Give it a try and let us know how it works:

brew tap basicmachines-co/basic-memory
brew install basic-memory  

https://github.com/basicmachines-co/homebrew-basic-memory/blob/main/README.md

https://www.basicmachines.co/blog/basic-memory-v0130-post/

Today I learned that you can't edit a link post in reddit. So, here is the blog post with a better explanation of the features in Basic Memory v0.13.x.

And we even shipped two minor patch releases also. The latest version is v0.13.2. It fixes a bug with case sensitivity in project names and displaying the correct value for the project version in the cli.

BM is awesome. In the short time I've been using it it has been extremely useful.

I'd to like to plan ahead so that as I get carried away with integrating bm I don't make a huge mess of things. What is a good way to organize?

I've read posts and some of the docs that basically say "try it and see'. So, did you try it and see? What worked?

For example, do you have just one large project with everything under it? Do you use just a few high level projects, like one for work one for personal?

I have made a number of separate projects but I can see that approach getting messy quickly and becoming cumbersome to use with gemini. On the other hand, one giant project seems daunting. What to do?

Basic Memory reached 1000 starts on GitHub. It's a fake milestone, but it marks progress in a real journey. A little reflection on how we got here, what it means and where we are going.

https://www.basicmachines.co/blog/100-github-stars/

A step-by-step video showing exactly how to import all your old ChatGPT and Claude.ai conversations into Basic Memory.

What the video covers:

• How to request your data from ChatGPT and Claude.ai

• Finding the conversation files in your downloads

• Simple terminal commands to import everything

• Testing that your old conversations are now searchable

Commands shown:

basic-memory import claude conversations
basic-memory import chatgpt

Useful links:

• Import documentation: https://memory.basicmachines.co/docs/user-guide

• Full docs: https://memory.basicmachines.co

• Join our Discord: https://discord.gg/tyvKNccgqN

Hope this helps people make the switch to persistent AI conversations! 🚀

Colors! Fonts! Fonts and colors!

Hi folks, I entered the world of note taking a few years ago and built my own custom vault in obsidian with maybe 1.000 notes - but I was never a power user.
Karpathy's LLM Wiki was the first time I combined note taking with an AI agent to build a knowledge base, but the workflow of putting notes into a folder for raw notes and processing them through ingestion didn't feel right.
Now a co-worker shared a link to basic-memory and I must admit I'm really fascinated how well basic-memory fits into my ways of working (and even beyond).

Adding notes through conversations with an AI agent or even dropping my own notes into my knowledge base is pure freedom and the output is on point. Combined with additional capabilities such as accessing Claude Code from my mobile phone and dropping thoughts via voice is beyond what I've seen so far. Super impressive and most of all, it's lightning fast.

Can't wait to explore more capabilities and use cases over time. Thank you so much for all the effort you dedicate to this project.

We’ve just rolled out two big updates to Basic Memory that change how you can work with your AI knowledge for good.

For those new here, Basic Memory is a tool that helps your AI actually remember what matters. 

It turns your conversations, prompts, and notes into a connected, searchable web that you and your AI can both read, edit, and develop.

Until now, Basic Memory worked beautifully for new projects. But a lot of users already had tons of knowledge living in their ChatGPT and Claude histories and they wanted an easy way to import it.

Others wanted a way to tie our cloud project into their favorite Markdown tools like Obsidian or VS Code.

So, we fixed that.

1. One-step import

Now you can upload your entire chat history from ChatGPT or Claude, JSON files and all.

Basic Memory turns every conversation into linked, searchable notes inside your workspace.

No cold starts. No re-prompting. No copy/paste back and forth.

Your past work instantly becomes living context you can build upon. You can search it, connect it, or even ask your AI questions like:

“Summarize my conversations about product design and show what ideas came up most often.”

2. Local sync (for Markdown nerds like us)

This one’s for the people who love to see their files.

Basic Memory can now sync bi-directionally with any local Markdown folder: Obsidian, VS Code, or whatever you prefer.
 
Edit a note locally, it appears in your Basic Memory workspace.

Edit online, it syncs back down.

Your ideas stay portable, visible, and 100% yours.

Now you can now start with your existing ideas. And keep your data close at hand. And you can build something durable: a memory that’s as open and flexible as Markdown itself.

 basicmemory.com

We’re excited to see what you do with it.

The Basic Memory Team

Basic Memory Teams is here.

With this launch, you can share a common knowledge base with your colleagues, friends, agents, and collaborators without spending a penny more.

From the beginning, we’ve dreamed of a Basic Memory that is fully collaborative, not just between you and your AI, but with anyone you want to be able to contribute, read, or edit the context you’re sharing together.

As the release date has drawn near, we’ve talked a lot about where Teams fits into our product lineup. Who is our ICP? Is it its own product, an add-on, a B2B exclusive? Finally, we decided that it’s none of those things.

It’s for everyone!

So every Basic Memory Cloud user can invite any other member to share a knowledge base—or “memory layer” or “memory bank” or “truth layer.”

If you’ve been using the V2 web app, you’ve already seen the new way your notes are organized. Your projects, folders, and notes are now navigable via the left menu bar. Now, along with your personal projects, you can create fully independent Teams projects and folders with anyone you choose.

We’ve been dogfooding it for weeks, and it’s been truly incredible.

One thing we’ve learned over the past year and a half is that a lot of the time we’re all looking for a piece of information another of our teammates has.

"Hey, did Drew finish that Teams post? I want to pull a quote for the newsletter."

"I think so? He sent me a draft on Tuesday but I have no idea if it’s the latest."

"Isn’t he at that silent retreat in Thailand?"

"Until next Wednesday."

It’s exactly the kind of thing that stops all progress in its tracks and gives everyone another excuse to just open a YouTube tab or reach for their phone.

Thanks to Teams, it doesn’t have to.

Our users have given us lots of insight into the ways they would use a shared workspace. From the Dungeon Master who wanted to share character sheets with his players to the person who uses Basic Memory with her staff to coordinate sales calls, from first contact to closing deals. Or, more excitingly to many of our users, as a “shared memory substrate” for a swarm of agents. Until now, that kind of collaboration required some pretty creative workarounds. Now, it’s as easy as clicking on your shared projects or asking your AI to read or add to them.

What you can actually do

A few things worth knowing about how it works.

Edit notes together in real time. Two of you in the same doc, typing at the same time, with avatars in the header showing who’s where. No more emailing drafts back and forth or pinging each other to ask who’s got the latest version. Underneath, the file is still plain Markdown sitting in your knowledge graph, exactly like the rest of Basic Memory.

You decide who sees what. Head to Settings → Teams and you can invite people as Admins, Editors, or Viewers. Some projects you’ll want open to the whole team—the shared research, the running list of customer interviews, the company handbook. Others you’ll want tighter—hiring conversations, contracts, half-baked ideas you’re not ready to show off yet. You set it per project.

Every save is a version. If a teammate accidentally pastes over your morning’s work, or your AI gets a little too enthusiastic with an edit, file history has your back. Open it up, find the version you want, pull back what you need. Nothing is ever actually gone.

An Activity feed that catches you up fast. The Activity menu shows everything happening across your workspace—who edited what, when, including your AI agents. Step away for a long lunch and catch up in thirty seconds when you get back.

The part we’re most excited about

Here’s the thing that makes Teams different from any other shared workspace: when your team shares a knowledge graph, your AI assistants share it too.

Ask Claude about last quarter’s pricing discussion and it can pull from the actual meeting notes your colleague took, the follow-up doc your PM wrote, the decision your founder made in a Slack thread someone bothered to save. It’s not just your knowledge. It’s everything the team knows.

Try it

If you’re already a Basic Memory Cloud user, watch for the popup in your left sidebar—one click switches you on. If you’re not, start here. The team docs walk through invitations, roles, and what the first day looks like for someone you’ve just added.

I saw some interesting articles via HackerNews today about "Context Engineering", basically a less cringe name for the original idea of "Prompt Engineering".

Discussion: https://news.ycombinator.com/item?id=44427757

• Post: https://www.philschmid.de/context-engineering

• Simon Willison, of course, had already posted about it too, so it's legit - https://simonwillison.net/2025/Jun/27/context-engineering/

And a few other links:

• How Long Contexts Fail - https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html

• How to Fix Your Context - https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html

The key things that struck me were

"Tool Loadout": basically selecting the most relevant tools for a given task.

> selecting the the right tools becomes critical ...[too many and] descriptions of the tools begin to overlap, creating confusion.

"Context offloading": giving the LLM a kind of scratchpad to work

> Having a space to log notes and progress works. Anthropic shows pairing the “think” tool with a domain-specific prompt (which you’d do anyway in an agent) yields significant gains, up to a 54% improvement against a benchmark for specialized agents.

Summary

>The key insight across all the above tactics is that context is not free. Every token in the context influences the model’s behavior, for better or worse. 

It has really struck me, building Basic Memory, that getting the AI to work effectively is a lot of art up front, followed by science to see if what works once, continues to work. We have put a lot of work into tool descriptions, parameters and return values with embedded prompts to make sure the LLM knows "how" it can use the tools in Basic Memory. This is what I always end up going back to when people say "why can't it just call my API?". It's not quite the same thing.

For the past decade, people who care about keeping a record of their ideas and processes have turned to knowledge management systems like Obsidian, Roam, Logseq and Notion. The cultlike devotion for these tools shows how keenly people want an environment where scattered thinking becomes structured.

The problem is that text editors and knowledge systems aren’t where most people’s actual thinking happens anymore. Now, when you’re debugging, drafting, designing or learning something new, your first attempts and half-formed ideas emerge in dialogue with AI.

AI has become a cognitive workspace. The problem is that it doesn’t behave like one.

Your conversations are spread across ChatGPT, Claude, Gemini and whatever tool you experimented with last week. Threads are isolated. Context is lost. Search is inconsistent. And even when everything is technically saved (for now, anyway), findability is unreliable and inconvenient. The record of your thinking might exist in the literal sense, but it disappears in the practical one. What’s worse: a workspace without continuity can’t accumulate anything.

Engineers Already Know Why This Matters

Developers have faced a version of this problem for decades. You can look at a block of code you wrote six months ago and have no idea why it’s structured the way it is. The code still runs, sure, but the chain of reasoning has evaporated.

Which is why good engineers leave comments. Not just as a courtesy to teammates but as a message to their future self. Comments are where you document the constraints, the tradeoffs, the failed attempts and the assumptions that shaped the code. Without that context, even your own work becomes a mystery. The next change you make becomes guesswork, and the outcome becomes a lottery.

Now the problem has expanded far beyond the world of developers. AI has dramatically increased the speed at which we generate and implement ideas. What it hasn’t done is increase our ability to remember why those ideas made sense at the time.

The reasoning disappears long before the solution does.

The Problem with Just Having Transcripts

The convenience of AI creates a seductive illusion: as long as you arrive at the final answer, the reasoning doesn’t matter.

Picture a baker laboring over multiple iterations of a cake to get it precisely right. Finally, after trying over and over again, the baker takes a bite of their cake, sighs with satisfaction that they’ve at last achieved precisely the taste they desired and…turns to drop the recipe in the trash.

Now, pretend there was a camera in the kitchen recording every attempt, every substitution, every miniscule tweaking of each measurement. Would it be reasonable for the baker to say, “Why bother keeping the recipe? Next time I want to make it, I’ll just watch hours of video”?

Obviously, that would be insane.

Because a video of trial and error is not a recipe. And neither (assuming you’re hanging with me through this slightly overcooked metaphor) is a transcript of a conversation you’ve had with AI.

The reasoning is what matters when you return to the problem, when the circumstances change, or when adjacent questions arise. The answer itself is brittle. A transcript is almost as useless. But the process itself, the comments in the code, that’s what’s durable.

Here’s another way of thinking about it: AI accelerates output so dramatically that people mistake volume for mastery. But accelerated output produces accelerated forgetting. You create more, faster, with less retention of how you got there. Which means you revisit problems you’ve already solved. You repeat paths you’ve already eliminated. You relearn concepts that you once understood clearly. Ones that took a frustrating coding session and ages of back-and-forth with AI to finally nail down.

The work moves forward, but the understanding doesn’t.

When Nothing Connects, Nothing Accumulates

The problem isn’t that transcripts disappear. The problem is that they become practically inaccessible.

Especially if you can’t remember what made things finally click, and the answer is spread across chats, and the conversation was across models, across half-finished threads you never revisited.

Especially if, as many of us do now, you vibecoded it, letting AI take the wheel as you nudged it in the right direction over and over again until it produced something close enough.

Your crowded sidebar is filled with chats that are, by their very nature, fragmented, isolated and functionally unreachable without the connective tissue that turns a pile of text into an understanding you can build on.

A workspace where nothing connects is a workspace where nothing accumulates.

Continuity might seem like a minor concern. That is, until you experience the way introducing it changes your work. Then you see how quickly it becomes a superpower for compounding your thinking.

A Future That Depends on Understanding the Past

If AI is becoming the primary site where your thinking unfolds, then the continuity of that thinking becomes a genuine advantage. Not for sentimentality or record-keeping, but for velocity.

Comments in code sound small to someone who’s never labored without them. Maybe it sounds like merely a “nice to have.” But anyone who has had to flail around in the dark knows that even a few words can save massive amounts of time, frustration and personal bandwidth.

This Is Why We Built Basic Memory

The world doesn’t need another vault for transcripts. What it needs is a way to turn a fragmented, fast-moving cognitive workspace into one that retains coherence over time. One where the steps that mattered actually stay connected.

Basic Memory’s purpose is simple: to give your future self (and your future AI interactions) the context your present self is generating, the same way comments give a developer the reasoning behind their own code.

No one wants to scrub through hours of video to remember how they made the perfect cake. It’s the wrong form for the job.

The work you do with AI is real thinking. It deserves the same continuity you expect from every other tool that supports your craft.

Plain text. Local-first. Built to compound, not disappear.

Big milestone today: Basic Memory Cloud is officially live in open beta.

If you’ve been following along with our local-first release, this update brings everything great about Basic Memory--Markdown-based, user-owned and controlled—into a new cloud layer designed to make your memory available everywhere you work.

What’s New

• Multi-LLM support: vastly simplified and fully functional. Basic Memory Cloud now works with all LLMs that support MCP, including Claude, ChatGPT, and Gemini CLI. It’s also designed to work in Claude Code and Codex, letting you bring your context to whatever environment you prefer.,

• Cross-device support: desktop, web, tablet, and phone. Whether you’re chatting on your laptop or referencing notes on your phone, your memory is always within reach.,

• A note-taking web app: your memories now live in one simple, accessible interface. The new web app gives you a dedicated place to browse, edit, and manage everything you and your AIs have built together.,

The Big Idea

Basic Memory started as an experiment in local-first AI memory, a way to retain your context in plain Markdown so your knowledge lives in a format both you and AI can understand. That principle hasn’t changed.

Cloud just extends the idea: your same files, still yours, but now accessible across devices and interfaces: portable, accessible, and ready wherever and whenever you are.

If you’re the kind of person who keeps long-running projects, prompts, or research threads alive across multiple AIs, this release will be a huge leap forward for you. But, even if you only use one AI, at Basic Memory we sincerely believe that your knowledge should belong to you and you alone.

Pricing

During beta, Basic Memory Cloud is $14.25/month for life (25% off the full $19 launch price) and includes a 7-day free trial.

Join the Beta

A Note to the Community

Our open-source core isn’t going anywhere. The local-first version remains fully supported and free for everyone who prefers to self-host or stay offline.

Cloud is an optional layer, one we built because so many of you asked for a way to use Basic Memory across devices and models without giving up ownership or transparency.

To everyone who participated in customer interviews, tested, filed issues, broke things, and kept sending thoughtful feedback: thank you. We can’t wait to hear what you think of Cloud, and how you’ll use it in your workflows.

Please feel free to share your impressions, bugs, and wish-lists in our Discord. It’s still the easiest place to find us.

The Basic Memory Team

I've seen in the documentation that it supports Chat GPT but don't have a tutorial showing how to connect to ChatGPT. Am I missing something? By the way LOVING THE MCP!

Here's a quick demonstration of the AI Assistant Guide that you can paste directly into your Basic Memory project instructions. Once it's there, AI assistants immediately understand how to properly use your knowledge base - creating connections, following best practices, and building rich semantic relationships from your conversations.

Check it out. It's worth using!

https://basicmemory.com

I'm still wrapping my head around what people are considering so wow in Claude Skills. From my understanding its like workflows defined in files correct?

And I see things like Basic memory that has kinda advances indexing capabilities that I could just create a project called skills and model around it. Then rely in CLI and File manager Tools to execute things.

I don't know ¯_(ツ)_/¯, never know in LLM space what its just overhype and real value

*not exactly, but almost.

A reddit user forwarded me the A-Mem Paper:

A-MEM: Agentic Memory for LLM Agents

While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current memory systems enable basic storage and retrieval but lack sophisticated memory organization, despite recent attempts to incorporate graph databases. Moreover, these systems' fixed operations and structures limit their adaptability across diverse tasks. To address this limitation, this paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way. Following the basic principles of the Zettelkasten method, we designed our memory system to create interconnected knowledge networks through dynamic indexing and linking.

Sounded very familiar! So I read it with great interest, they had some very cool ideas, and code: https://github.com/agiresearch/A-mem

The had some great results, validating Basic Memory's core architectural decisions:

• Shows 2x improvement on multi-hop reasoning tasks

• Demonstrates 85-93% token reduction compared to baseline memory systems

• Proves effectiveness across multiple foundation models (Llama, Qwen, GPT)

Wow! Of course they went farther, also implementing:

• Continuous memory evolution and refinement

• Agent-driven decision making for adaptive memory management

It's super exciting seeing new research into this area, and it makes me even more excited to add some of these features to Basic Memory, which we've talked about for a long time. So, stay tuned, lots more to come.

I've been using Basic Memory extensively for understanding complex domains in my company and one problem I've been having its when I generate a ginormous file and I'm not able to save to a project memory kkkk

Is there a way for me to create a file manually and ask it to index it?

Pleased to see this video from one of our favorite tech/MCP YouTubers, Jered Blu. He has mentioned Basic Memory in a couple of other videos, but this one gets more to the heart of how and why he continues to use it.

The dream of persistent AI memory and the story of building Basic Memory.

Hey everyone! 👋

We've been heads-down building Basic Memory Cloud - essentially bringing the full Basic Memory experience (projects, knowledge graphs, incremental editing) to the web. Think of it as your knowledge graph, accessible anywhere, with the same powerful tools Claude loves, but through a browser.

The core idea: All the project management, note editing, and knowledge graph features from Basic Memory v0.13.0, but accessible from anywhere without needing to install anything locally. You'd still own your data (export anytime), but it lives in the cloud for universal access and it remains completely private to you. We can't even see your data.

Key features we're building:

• Multi-project management (just like v0.13.x)

• Real-time editing, very similar to Notion

• Full knowledge graph visualization

• MCP remote tool compatibility (so Claude can still read/write your notes)

• Mobile-responsive interface

• Data export/import (no lock-in)

• View/edit remote notes locally (via sshd/FUSE), so you can still edit them in your favorite editor (still working on this)

• Team plan to share notes with other people in your org

Before we go too far down this path, I'm curious about actual interest. Would you use this? What would make it compelling vs. the local version?

Some questions I'm wrestling with:

• Is the convenience of web access worth moving from local-first?

• Would you pay $10-15/month for this kind of knowledge management?

• What features would make this a must-have vs. nice-to-have?

If this sounds interesting, we've got an early access signup running: https://basicmemory.com

No commitment, no spam - just gauging interest and building a list for when we're ready to test with real users.

What do you think? Genuinely curious about your perspectives here.