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).
Hooman
Own your AI agent stack — local, open source and extensible
Hooman is an open-source, local-first AI agent runtime that works across the terminal, VS Code, ACP-compatible editors and event-driven workflows. Use your preferred models and inference endpoints, including local llama.cpp and MLX, or bring your own API keys. Hooman includes agent, plan, ask and design modes, MCP integrations, reusable skills, subagents, approval controls and persistent project-scoped sessions.
Hey Product Hunt 👋
I built Hooman because I wanted an AI agent stack I could actually own. Most agent tools are either tied to a hosted platform, limited to one interface, or difficult to adapt to private and local infrastructure.
Hooman is an MIT-licensed, local-first agent runtime that works across the terminal, VS Code, ACP-compatible editors, and event-driven daemon workflows—all sharing the same configuration, sessions, skills, MCP tools, and approval model.
You can use hosted providers, bring your own keys, point it at private inference endpoints, or run locally with llama.cpp and MLX. There’s no account requirement and no telemetry; your keys, sessions, and configuration stay on your machine.
It also includes Agent, Plan, Ask, and Design modes, reusable skills, subagents, human-in-the-loop approvals, MCP integrations, background tasks, and design exports to formats such as PDF and PowerPoint.
I’d especially love feedback on:
• What would make you switch from your current coding agent?
• Which model or provider integrations should I prioritize next?
• Where does the setup or documentation still feel unclear?
Thanks for checking it out. Hooman is open source, so issues, stars, feedback, and contributions are all genuinely appreciated 🙌
Local-first is a great call, would love to see a built-in cost and token usage dashboard per session and provider so I can keep tabs on API spend without wiring up my own logging.
Would love to see a built-in TUI dashboard for monitoring active sessions, token usage, and subagent status at a glance. Something like a tmux-style interface where I can attach to a session, see what the agent is currently thinking, intervene mid-run, and pause or kill it without dropping context. Would make managing long-running local workflows way less opaque.
About Hooman on Product Hunt
“Own your AI agent stack — local, open source and extensible”
Hooman was submitted on Product Hunt and earned 0 upvotes and 3 comments, placing #141 on the daily leaderboard. Hooman is an open-source, local-first AI agent runtime that works across the terminal, VS Code, ACP-compatible editors and event-driven workflows. Use your preferred models and inference endpoints, including local llama.cpp and MLX, or bring your own API keys. Hooman includes agent, plan, ask and design modes, MCP integrations, reusable skills, subagents, approval controls and persistent project-scoped sessions.
Hooman was featured in Open Source (68.6k followers), Developer Tools (515.9k followers), Artificial Intelligence (473.7k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 221.6k products, making this a competitive space to launch in.
Who hunted Hooman?
Hooman was hunted by Vaibhav Pandey. 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 Hooman stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Vaibhav Pandey