Every AI agent you run keeps what it learns to itself. Work through a problem with Claude Code in the morning, then ask Codex about it that afternoon, and the second agent has no idea the first one exists. Add a home-server agent like OpenClaw or Hermes into the mix, and you end up reexplaining the same context to a different tool every time you switch.
When AI engineer Sajal Sharma ran into this problem in his own work, he solved it by building a personal knowledge base to act as a shared brain for every agent he runs. On this week’s episode of Zero to Agent in 30 Minutes, Sajal showed how to set up that shared workspace yourself so that a task added on one tool shows up for all the others.
How to set up a knowledge base for your agents
Here’s how Sajal’s setup breaks down:
- Create a workspace map. Set up an AGENTS.md file that lists where everything in your knowledge base lives, from current tasks to project notes to decision logs. This will help each of your agents navigate your workspace without guessing.
- Layer daily notes into summaries. Keep the most detailed notes at the daily level, then roll several days into a weekly summary and several weeks into a monthly one. An agent can then work from the summarized view instead of reading through months of individual files, which keeps token use manageable as the knowledge base grows.
- Bridge AGENTS.md with CLAUDE.md. Claude Code reads CLAUDE.md, not AGENTS.md, so add a short pointer in CLAUDE.md that redirects to the AGENTS.md or link the two files directly. Sajal uses this pattern to avoid maintaining two files separately and having them drift out of sync.
- Package repeatable tasks as skills. Turn routines you do often, like producing a daily briefing or turning a saved article into a note, into skill files stored in the shared workspace. Any agent that can read the workspace can then run the task the same way, rather than working out the steps on its own each time.
- Sync the workspace across machines. Use a file-sync tool, Git, or a shared server to keep your local copy of the knowledge base and your server copy aligned. That way, you ensure that an agent running on a laptop and one running on a home server, through a gateway like OpenClaw, are working from the same files.
- Have agents reread the state before every write. Add an instruction in AGENTS.md telling every agent to check the current version of the knowledge base before making a change. When you have several agents writing to the same files, this step keeps one agent from acting on information another has already updated.
Sajal closed by pointing to two projects as evidence that this “shared brain” pattern is spreading beyond his own setup. LangChain recently released OpenWiki, a tool that generates and maintains repository documentation that both people and coding agents can use. And Y Combinator president Garry Tan built and open-sourced GBrain, a memory layer for agents built on the same principle.
Sajal’s starter repo is available on GitHub if you want to set up your own version, and you can reach out to him on LinkedIn to discuss the topic further.
Coming up next
On September 16, data science educator and AI consultant Chester Ismay joins Zero to Agent in 30 Minutes to build a personal sports concierge agent that will read the schedules for every sport he follows, decide what’s worth his time, and send a single weekly update to his phone. Viewers can take the pattern home to plan their own week.
Follow along with Zero to Agent in 30 Minutes on Radar, or watch the latest episode on YouTube, Spotify, Apple, or wherever you get your podcasts. If you’re an O’Reilly member, you can watch live. Save your seat.
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