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).
Create, configure, run, and monitor AI agents across Slack, Microsoft Teams, and Telegram from one self-hosted control plane.Agent Barn is an open-source control plane for AI agent fleets. Agents are Markdown files you fork, pin and roll back. Each runs in its own pod with its own credentials. Inference goes through OpenRouter; LiteLLM attributes every token per agent, per model. Helm-install on your own Kubernetes, or clone it and the whole stack comes up on your laptop. Apache 2.0.
We just published our open-source control plane for AI agents. One command, six steps, and you have a working agent in your own Kubernetes cluster, talking to your tools, on your infrastructure. It runs OpenClaw and Hermes under the hood so if you wanted an easy and secure way to get an agent to every employee, now it's a great time to do that.
𝗪𝗵𝘆 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗶𝘁
Most teams we work with have one hard requirement: software runs on their machines, not in someone else's cloud. The moment a sales call turns to deployment, that requirement kills half the deals.
Agent Barn is our answer. It is open source under Apache 2.0, self-hostable with Helm and PostgreSQL, and built to be the thing your IT team is willing to approve.
When a manufacturing client asks "can we see the source, can we run it on our hardware, can we audit it," the answer is now "yes, today, here is the repo."
𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗯𝗼𝘅
• Agents in Slack, Microsoft Teams, Telegram, and Discord, plus a chat surface in the web UI. • Per-agent model and cost attribution through an LLM proxy that lives in your namespace. No opaque monthly bill. • Versioned agent templates. Skills with encrypted credentials. A real audit trail of conversations, tool calls, and lifecycle events. • A control plane for orgs, agent access, lifecycle, costs, and observability. One place, not five. • Integrated tools and skills that work with your stack out-of-the-box.
𝗪𝗵𝘆 𝗻𝗼𝘄
Two years of agent tooling taught us that the platform matters. People do not want another model picker. They want a way to give an agent a job, give it the right tools, and watch it do the work.
This is the thing we wanted to build for the last twelve months. It is the thing we wish existed when we started. It is the foundation for every paid agent engagement we sell.
𝗪𝗵𝗮𝘁 𝗶𝘀 𝗻𝗲𝘅𝘁
We will be working with our manufacturings client this quarter. Even though the Agent Barn is open source, manufacturing requires custom implementations, integrations, and vertical-specific templates to make it work.
We are aiming for 100 GitHub stars in the first month. If you have an opinion on agent infrastructure, if you have a use case that needs a real platform under it - give it a star, open an issue, join the Discord. That signal is what we will use to decide how fast to push the roadmap.
Honestly, this is pretty cool. The self-hosted part especially caught my attention. I can see why that would matter a lot for teams with strict infrastructure requirements. Congrats!!
Agent Barn was submitted on Product Hunt and earned 5 upvotes and 4 comments, placing #25 on the daily leaderboard. Create, configure, run, and monitor AI agents across Slack, Microsoft Teams, and Telegram from one self-hosted control plane.Agent Barn is an open-source control plane for AI agent fleets. Agents are Markdown files you fork, pin and roll back. Each runs in its own pod with its own credentials. Inference goes through OpenRouter; LiteLLM attributes every token per agent, per model. Helm-install on your own Kubernetes, or clone it and the whole stack comes up on your laptop. Apache 2.0.
Agent Barn was featured in Open Source (68.8k followers), Artificial Intelligence (478.1k followers), GitHub (41.4k followers) and Community (3.2k followers) on Product Hunt. Together, these topics include over 168.2k products, making this a competitive space to launch in.
Who hunted Agent Barn?
Agent Barn was hunted by Gaurav Kunwar. 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 Agent Barn stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Gaurav Kunwar
We just published our open-source control plane for AI agents. One command, six steps, and you have a working agent in your own Kubernetes cluster, talking to your tools, on your infrastructure. It runs OpenClaw and Hermes under the hood so if you wanted an easy and secure way to get an agent to every employee, now it's a great time to do that.
𝗪𝗵𝘆 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗶𝘁
Most teams we work with have one hard requirement: software runs on their machines, not in someone else's cloud. The moment a sales call turns to deployment, that requirement kills half the deals.
Agent Barn is our answer. It is open source under Apache 2.0, self-hostable with Helm and PostgreSQL, and built to be the thing your IT team is willing to approve.
When a manufacturing client asks "can we see the source, can we run it on our hardware, can we audit it," the answer is now "yes, today, here is the repo."
𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗯𝗼𝘅
• Agents in Slack, Microsoft Teams, Telegram, and Discord, plus a chat surface in the web UI.
• Per-agent model and cost attribution through an LLM proxy that lives in your namespace. No opaque monthly bill.
• Versioned agent templates. Skills with encrypted credentials. A real audit trail of conversations, tool calls, and lifecycle events.
• A control plane for orgs, agent access, lifecycle, costs, and observability. One place, not five.
• Integrated tools and skills that work with your stack out-of-the-box.
𝗪𝗵𝘆 𝗻𝗼𝘄
Two years of agent tooling taught us that the platform matters. People do not want another model picker. They want a way to give an agent a job, give it the right tools, and watch it do the work.
This is the thing we wanted to build for the last twelve months. It is the thing we wish existed when we started. It is the foundation for every paid agent engagement we sell.
𝗪𝗵𝗮𝘁 𝗶𝘀 𝗻𝗲𝘅𝘁
We will be working with our manufacturings client this quarter. Even though the Agent Barn is open source, manufacturing requires custom implementations, integrations, and vertical-specific templates to make it work.
We are aiming for 100 GitHub stars in the first month. If you have an opinion on agent infrastructure, if you have a use case that needs a real platform under it - give it a star, open an issue, join the Discord. That signal is what we will use to decide how fast to push the roadmap.