GBrain and Obsidian: How to Give Every AI Agent One Shared Brain

GBrain is an open-source memory layer for AI agents, and it has more than thirty-one thousand stars on GitHub. Garry Tan, president and CEO of Y Combinator, built it to run his own agents. 

This leaves the question, can one shared memory work across Muse, Instinct, Grok Bot, and Claude? I gave my own agents one shared brain, and they started working as one team immediately. I’m convinced the memory your agents share will outlast every agent you test this year.

Along the same lines, Obsidian comes up in the same breath, since plenty of teams already keep their notes there as plain markdown. My company also builds SingleBrain, which puts one trusted brain behind a fleet of business agents. So what does this all mean for how you run and manage your agents?

Keep reading to learn what GBrain does, how GBrain vs Obsidian plays out, which agents can plug in today, and the SingleBrain setup my team runs.

TL;DR

• GBrain is a shared memory you control. It stores facts with their sources, accepts corrections, and serves the same memory to Claude Code, Codex, Cursor, Grok Bot, and Muse.
• Obsidian and GBrain do different jobs. Obsidian is where people read and write markdown notes, and GBrain imports those notes so agents can search them and return cited answers.
• Most of the bots in that comment can connect today. GBrain publishes setup guides for Grok Bot and Muse, plus a setup path for Claude, while its guides list no Instinct connector yet.
• Storage alone stops scaling fast. My team’s memory files once ate about 40% of the context window, so retrieval, source rules, permissions, and feedback loops carry as much weight as storage.

What Is GBrain and Why Are AI Agent Builders Using It?

GBrain is a memory layer you run yourself and plug into the agents you already use. It stores explicit facts with their sources, supports corrections and withdrawals, and serves the same memory to every agent you connect. You start with keyless memory and keyword search, then add semantic search, synthesis, and background enrichment when you need them.

Some quick history: Garry Tan built GBrain as the production brain behind his own OpenClaw and Hermes agents. His brain holds over one hundred fifty-five thousand pages, about twenty-four thousand people, and over five thousand companies. Sixty-six cron jobs run on their own, and his agent ingests meetings, emails, voice calls, and ideas while he sleeps.

What Makes GBrain Different From a Folder of Notes

When you ask most note tools a question, you will usually get a list of pages that you have to read yourself. GBrain’s optional synthesis layer returns an actual answer with citations, plus a note on what the brain doesn’t know yet. Its typed knowledge graph also links people, companies, and deals, so a question like who works at a company walks those links in milliseconds.

You can probably see how significant this is! 

Your hardware, your database, and your keys stay yours. Durable preferences and facts can be shared across agents, while short-lived task state stays local to each one.

Is GBrain Free?

Yes! The code is open source under the MIT license, so you can run the software yourself for free. However, please note that model usage is a separate bill once you add embeddings, synthesis, or always-on enrichment. A hosted version lives at gbrain.io for teams who would rather skip the install.

So where does Obsidian fit, when so many people already keep a second brain there?

GBrain vs Obsidian: Which One Should Hold Your Agents’ Memory?

Obsidian is a note app built on local markdown files. It’s free without limits, keeps your data on your device, and charges only for optional sync and publishing. Unlike Obsidian, GBrain is a memory service your agents query, with sources and corrections built in.

Where Obsidian Works Best as an AI Agent Notebook

Obsidian shines as the place humans think. You can open any note, read it, and fix it by hand. Agents read markdown well, so a vault already works as a simple shared memory for a small fleet.

I use it this way myself. In my seven-step company brain checklist, I log my decisions in Obsidian so my system can see how my thinking changed over time. My Hermes chief of staff playbook also uses Obsidian for memory, with Linear for tasks and connectors into Granola, Gong, and HubSpot.

Where GBrain Pulls Ahead

Obsidian was designed for people, so search across a huge vault depends on your folder habits. GBrain was designed for agents. It returns cited answers, tracks where each fact came from, and treats corrections as memory in their own right.

Why I’d Run Both

You don’t have to pick a side. GBrain imports a folder of markdown with one command, and it can resolve Obsidian-style wikilinks once you switch on basename matching. Obsidian stays the notebook your team reads, and GBrain becomes the memory your agents search.

Pro-Tip
If your vault uses bare [[note-name]] links, run gbrain doctor after the import. It reports how many of those links would resolve before you turn on basename matching, so you can decide before you flip the switch.

Storage is only half the story, though. Your agents still have to reach it.

Can Muse, Instinct, Grok Bot, and Claude Share One Memory?

I tested Muse, Instinct, Grok Bot, and Hermes side by side on my real work. My verdict was to use all four, because each has strengths the others lack. There are no solutions, only trade-offs, and combining them gets the most done across personal and business work.

Four agents create a new problem. Each agent learns about you separately unless all of them read from the same memory. Here is how each agent in that comment lines up with GBrain.

Grok Bot

GBrain publishes a Grok Bot setup guide. The recommended path keeps the brain on your own computer, publishes it over MCP, and grants the bot a memory-writer client. Grok Bot is my daily driver because I run so many routines on it, and it anchors the AI operating system my team relies on.

I compared it with OpenAI Dots and Muse in my team agent breakdown (OpenAI Dots vs Grok Bot vs Meta Muse). In my four-agent test, my conclusion was simple. You ultimately want a chief of staff bot like Grok Bot to manage your other bots.

Meta Muse

Muse is Meta’s personal agent, and it already remembers your preferences and goals. GBrain’s Muse guide adds a portable record with sources and shared access on top of Muse’s own editable memory. I use Muse to rework content, and I paired it with Jev to find the best Instagram hooks.

Claude, Codex, and Cursor

Coding agents have the smoothest path. One setup command wires GBrain into Claude Code or Codex, and Cursor connects through a standard MCP config. Claude Desktop connects to a remote brain through its integrations settings.

Instinct

Instinct is the outlier. It’s a text-first personal agent, and it can now place phone calls through Instinct Concierge. GBrain’s guides list no Instinct connector, so for now you would hand it context yourself.

So a common memory already works for most of the bots in that comment. The bigger lesson came from my own fleet.

How I Gave My AI Agents One Shared Brain

My own shared brain started with plain files. The fix was simpler than most multi-agent frameworks suggest.

Three Smart Agents in Three Soundproof Rooms

I ran three agents on my marketing operations. Oracle handled SEO intelligence, Flash ran content, and Alfred acted as my chief of staff. Between them, they ran thirty scheduled jobs every day with no human in the loop.

Each agent was excellent alone and braindead as a group. Oracle found a Reddit marketing agency page converting at 17.5%, and the insight sat in an SEO report. Flash wrote about something unrelated thirty minutes later.

One Shared Folder Fixed It

The fix needed zero new agents. I built one shared directory of thirty-eight markdown files and symlinked it into every agent’s workspace. When I update priorities, all three agents read the same source of truth on their next run.

The setup took about twenty minutes, with no LangChain and no custom protocol. Oracle flagged the Reddit page, Flash read the report, and Flash wrote Reddit marketing posts for X and LinkedIn pointing at that same page. Organic and social finally worked as one plan.

One Rejection Teaches Every Agent

Every agent recommendation came with approve and reject buttons. Each rejection landed in a shared feedback folder every agent reads. After sixteen feedback entries in two weeks, Oracle stopped suggesting tool-query keywords and Flash learned which angles I approve.

Your agents rarely need to message each other. They need to read from the same page, the same way a team reads one wiki. I scaled the same idea to an AI agent for every employee, all sharing one brain.

Then the shared brain got big, and a new problem showed up.

Why Shared Memory for AI Agents Breaks Without Retrieval

More context for every agent worked for about three weeks. Then memory itself became the bottleneck. Our persistent memory files ate about 40% of the context window, and agents pulled the wrong notes at the wrong time.

Memory is raw material, and retrieval is the operating layer. My team rebuilt our company brain around five layers, and they apply whether you use GBrain, Obsidian, or both. The five layers stack in this order.

1. Capture. Calls, CRM activity, content decisions, SOPs, agent outputs, and human corrections land in one place.
2. Retrieval. Each task pulls only the context it needs, such as the ICP, offer, objections, and brand voice for an outbound email.
3. Source truth. You decide which source wins when the CRM, a Slack correction, and an old SOP disagree.
4. Permissions. Each workflow sees only what it’s allowed to use, so a content agent never touches client financials.
5. Feedback loops. Every human correction becomes a rule the whole system follows next time.

Skip any layer and your agents become confident liars with better formatting.

GBrain covers several of these layers out of the box. It tracks the source of every fact, supports corrections, and limits remote clients by source and operation grants. Its docs also warn that local files and shared database credentials sit outside those limits, so read the sharing boundaries before you open a brain to your team.

Pay Attention
Fresh facts should beat old ones. A decision from last month should outrank a decision from three years ago, so give memory an expiry date, the same way you would expire stale CRM facts before an agent acts on them.

Get those layers right, and the setup itself is the easy part.

How to Set Up One Memory for All Your AI Agents

You can build a working version in a week without writing code. Four steps get you there. First pick one workflow, then put shared context in markdown, then connect each agent, and finally add a feedback loop.

1. Pick one workflow close to revenue. Content, SEO, sales calls, and weekly reporting all work. Start with one, because the brain earns the right to touch more.
2. Put shared context in markdown. Create files for priorities, decisions, brand voice, and feedback in Obsidian or a plain folder, then import them into GBrain.
3. Connect each agent. Use the setup command for Claude Code or Codex, the MCP guides for Grok Bot and Muse, and paste context by hand into agents without a connector.
4. Add approve and reject buttons. Write every rejection into the feedback file so one decision teaches every agent.

Keep the files short and specific. If your team already stores prompts in files, the six fields every team prompt file needs keep them consistent across agents too.

Here is a starter prompt. Paste it into whichever agent you trust most with your notes.

Starter prompt for a shared agent memory
Read every file in my shared-context folder before you act. Summarize my current priorities, my last ten decisions, and every rule in the feedback file. Then list which of my other agents would need this context, flag any fact older than ninety days, and suggest the three files I should add first. Don’t change or delete any file.

Rerun it whenever you add a new agent, so every bot starts from the same page.

Where SingleBrain Fits in a Shared Memory Stack

GBrain remembers what you know. Your agents still need live business data, and that sits in Search Console, HubSpot, Gong, Slack, and Stripe. Plenty of people still use AI in single-player mode, and I broke down why a company brain makes it multiplayer.

SingleBrain Gateway for Live Business Data

SingleBrain Gateway gives your agent one key to the business accounts you connect. Your key works across Claude, Cursor, and Codex, so you can switch agents and keep every account you already connected. Reading your own accounts is free, and paid tools bill per call from one balance, starting with $5 in credits.

Ask which accounts need attention, and your agent can pull deal context, Slack activity, and renewal timing together. My team broke down the churn signals hiding across HubSpot, Slack, and Stripe and how an agent catches them early.

SingleBrain for Managed Revenue Agents

For companies who want the whole system run for them, SingleBrain deploys managed revenue agents inside Slack or Microsoft Teams. One trusted brain coordinates specialist agents for sales, creative, SEO, AEO, analytics, and paid media. Each client gets an isolated deployment, the trade-off I covered in client isolation vs shared memory.

First deployments go live in five days, with the ambient agent plus three workflows in week one.

Give Every AI Agent the Same Brain

That comment about a common memory for Muse, Instinct, and Grok points at where AI is heading. Agents will keep changing, and the brain they read from is the asset you keep. GBrain, Obsidian, and one shared folder all get you started, while retrieval, permissions, and feedback keep it working.

If you want help building a shared brain behind your marketing agents, my team at Single Grain offers AI transformation services, AEO services, and SEO services. Talk to Single Grain and my team will map the first workflow your agents should share.

Frequently Asked Questions About GBrain and Shared AI Agent Memory

• What is GBrain?

  GBrain is an open-source memory layer for AI agents built by Garry Tan, president and CEO of Y Combinator. It stores facts with their sources, supports corrections, and serves the same memory to agents like Claude Code, Codex, Cursor, Grok Bot, and Muse.

• Is GBrain free?

  Yes, the software is open source under the MIT license, so you can run it yourself at no cost. Model usage for embeddings, synthesis, or always-on enrichment is a separate cost from your chosen provider. Teams who prefer no install can use hosted GBrain instead.

• GBrain vs Obsidian: which is better for AI agent memory?

  They do different jobs. Obsidian is a free markdown note app where people read and edit notes, while GBrain is a memory service agents query for cited answers. Many setups use both, with Obsidian as the notebook and GBrain importing the vault so agents can search it.

• Can GBrain connect to Grok Bot, Muse, and Claude?

  Yes. GBrain publishes setup guides for Grok Bot and Muse, a setup command for Claude Code and Codex, and MCP configs for Cursor and Claude Desktop. Its guides list no Instinct connector, so Instinct still needs context added by hand.

• How do I give all my AI agents shared memory?

  Start with one workflow, put priorities, decisions, brand voice, and feedback in shared markdown files, connect each agent to the same files or to a GBrain brain, and log every rejection so one correction teaches every agent. My team’s guide to the company brain context layer covers the bigger picture.

• What is SingleBrain?

  SingleBrain is Single Grain’s managed revenue agent platform. It puts one trusted brain inside Slack or Microsoft Teams and coordinates specialist agents for sales, creative, SEO, AEO, analytics, and paid media. SingleBrain Gateway separately gives your own agent one key to your connected business accounts.

If you were unable to find the answer you’ve been looking for, do not hesitate to get in touch and ask us directly.