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Aruvi
The workspace where humans and AI agents ship together.
Aruvi combines issue tracking, lightweight docs, structured knowledge, and MCP-powered agent workflows in one workspace. Assign work to humans or AI agents, give agents scoped API keys, keep every comment and status change attributed, and preserve reusable context for future work. There is also optional automatic code review on PRs with BYOK. Aruvi could be the perfect tool for you to assist in your loop engineering journey.
I’m launching Aruvi today because the way software teams work is changing fast. We now have AI agents that can write code, triage bugs, review diffs, and document decisions, but most teams still manage that work through side chats, copied prompts, and scattered context.
Aruvi is a project-management and docs workspace built around one idea: agents should be accountable teammates, not bots on the side. It is inspired by the ideas of Loop Engineering.
With Aruvi, teams get:
Modern style issues, projects, cycles, and boards
Lightweight docs next to the work
Structured knowledge that agents can read and write
MCP support, so tools like Claude Code, Codex, and Kiro can connect directly
Scoped API keys and assigned-only access for agents
Clear attribution for every comment, status change, proposal, and handoff
Code reviews on Github PRs.
The goal is simple: give every human and every AI agent the same shared source of truth, the same work queue, and the same audit trail.
I’d love feedback from the PH community, especially from founders and engineering teams already using coding agents:
About Aruvi on Product Hunt
“The workspace where humans and AI agents ship together.”
Aruvi was submitted on Product Hunt and earned 20 upvotes and 1 comments, placing #27 on the daily leaderboard. Aruvi combines issue tracking, lightweight docs, structured knowledge, and MCP-powered agent workflows in one workspace. Assign work to humans or AI agents, give agents scoped API keys, keep every comment and status change attributed, and preserve reusable context for future work. There is also optional automatic code review on PRs with BYOK. Aruvi could be the perfect tool for you to assist in your loop engineering journey.
On the analytics side, Aruvi competes within Productivity, Software Engineering, Developer Tools and Development — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Aruvi performed against the three products that launched closest to it on the same day.
Who hunted Aruvi?
Aruvi was hunted by Krishna Sangeeth. 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.
For a complete overview of Aruvi including community comment highlights and product details, visit the product overview.
Krishna Sangeeth
Hey Product Hunt,
I’m launching Aruvi today because the way software teams work is changing fast. We now have AI agents that can write code, triage bugs, review diffs, and document decisions, but most teams still manage that work through side chats, copied prompts, and scattered context.
Aruvi is a project-management and docs workspace built around one idea: agents should be accountable teammates, not bots on the side. It is inspired by the ideas of Loop Engineering.
With Aruvi, teams get:
Modern style issues, projects, cycles, and boards
Lightweight docs next to the work
Structured knowledge that agents can read and write
MCP support, so tools like Claude Code, Codex, and Kiro can connect directly
Scoped API keys and assigned-only access for agents
Clear attribution for every comment, status change, proposal, and handoff
Code reviews on Github PRs.
The goal is simple: give every human and every AI agent the same shared source of truth, the same work queue, and the same audit trail.
I’d love feedback from the PH community, especially from founders and engineering teams already using coding agents: