This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Hubzoid is an open-source framework for internal AI agents. Put your team’s knowledge, rules and tools in one shared Hub, then use it in chat, workflows and compatible assistants via MCP. Install: pip install hubzoid. Self-host it. Apache 2.0, team controls included.
Hubzoid is an open-source framework for internal AI agents. Bring your team's knowledge, rules, skills and tools into one shared Hub, and reuse that foundation across agents.
Connect via MCP from ChatGPT or Claude to use your Hub’s context and authorized tools.
Start with one task and your own material. Your Hub starts with readable Markdown files you can inspect, edit and version in Git. Correct a rule, save it, try again, then share it with teammates.
Fully open source. Apache 2.0. Team controls included. No paid feature gates. Self-host it. Install:
pip install hubzoid
Need implementation help? Our Build with us service uses the same open-source product.
What's one internal task you'd hand to an agent, and what would it need to know to do it your way?
Hi everyone, I'm Darshika, co-creator of Hubzoid 👋
I'll share a bit about how it feels to use it.
I set up a Hub from our own notes and rules, gave a teammate access to just one agent, and scheduled a daily report. All of it lives in plain Markdown files, so nothing is hidden in a prompt somewhere.
The best part: when the agent gets something wrong, you fix one line and ask again. Everyone on the team gets the corrected answer, and the change is saved in Git.
If you try it today, tell me where setup slowed you down. That's the feedback we want most. I'll be here all day.
About Hubzoid on Product Hunt
“Open-source framework for your internal AI agents”
Hubzoid was submitted on Product Hunt and earned 4 upvotes and 2 comments, placing #64 on the daily leaderboard. Hubzoid is an open-source framework for internal AI agents. Put your team’s knowledge, rules and tools in one shared Hub, then use it in chat, workflows and compatible assistants via MCP. Install: pip install hubzoid. Self-host it. Apache 2.0, team controls included.
Hubzoid was featured in Open Source (69k followers), Developer Tools (521.5k followers), Artificial Intelligence (481.6k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 269.8k products, making this a competitive space to launch in.
Who hunted Hubzoid?
Hubzoid was hunted by Darshika Joshi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Hubzoid stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Darshika Joshi
Hi Product Hunt! 👋
Hubzoid is an open-source framework for internal AI agents. Bring your team's knowledge, rules, skills and tools into one shared Hub, and reuse that foundation across agents.
⭐ GitHub: https://github.com/hubzoid/hubzoid
📖 Docs: https://hubzoid.com/docs
Use your Hub three ways:
Chat with agents about your team's work.
Run workflows, like a morning briefing.
Connect via MCP from ChatGPT or Claude to use your Hub’s context and authorized tools.
Start with one task and your own material. Your Hub starts with readable Markdown files you can inspect, edit and version in Git. Correct a rule, save it, try again, then share it with teammates.
Fully open source. Apache 2.0. Team controls included. No paid feature gates. Self-host it.
Install:
Need implementation help? Our Build with us service uses the same open-source product.
What's one internal task you'd hand to an agent, and what would it need to know to do it your way?