Issue, project, and cycle planning
Tracks issues as the smallest useful unit of work and groups them into projects, cycles, and team-specific flows so teams can manage execution in one place.
Aruvi
BeanspruchenAruvi is a workspace for software teams that unifies issue tracking, docs, knowledge, and AI agent workflows for small product and engineering teams.
Workspace for human and AI collaboration
Aruvi is a workspace for software teams that brings issue tracking, docs, knowledge, and AI agent workflows into one system. The product positions itself as a fast issue tracker and lightweight wiki where humans and AI agents work together instead of in separate tools.
The core idea is to keep context close to execution. Teams can plan work in issues, projects, cycles, boards, lists, and roadmaps; store docs and knowledge in the same workspace; and connect AI tools through an MCP endpoint so agents can be assigned work, leave comments, and operate under scoped guardrails.
Core capabilities
Docs and knowledge storage
Keeps durable product context close to the work with docs and knowledge stored in the same workspace as issues and projects.
Agent participation in workflows
Treats AI agents as first-class teammates that can be assigned to issues, leave comments, and appear in the same review trail as human teammates.
Accountable agent access
Provides scoped access for agents, including scoped API keys, assigned-only guardrails, and attribution for agent actions.
AI tool connections
Supports an MCP endpoint for connecting AI tools, with the docs calling out Claude Code, Codex, and Kiro.
Workspace planning and connectivity
Includes board, list, cycles, roadmap, custom workflow states, context-bundle endpoint, OKF export, and integrations with GitHub, Slack, and Discord.
Common ways teams use Aruvi
Plan and track product work
Use Aruvi to organize day-to-day engineering work in issues, projects, cycles, and roadmap views while keeping team-specific workflow states and labels aligned.
Store product context with the work
Keep specifications, notes, and other durable context in docs and knowledge next to the issue flow so people and agents can find the background without switching tools.
Delegate implementation tasks to agents
Assign a coding agent to a real issue, give it scoped access, and review its output in the same trail used for human work.
Connect external AI coding tools
Use the MCP endpoint to connect Claude Code, Codex, or Kiro when you want AI tools to operate inside the same workspace and workflow structure.
Grow from a small team to a larger workspace
Start small on the Free plan, then move to Pro when you need more issues, projects, or workspace members without changing the workspace model.
Pros and Cons
Pros
- Combines tracker, wiki, and agent workflows in one workspace.
- Supports AI agents as accountable participants rather than external chat assistants.
- Offers both team planning views and durable docs/knowledge in the same product.
- Includes an MCP endpoint and named support for Claude Code, Codex, and Kiro.
- Has a simple pricing model billed per workspace instead of per seat or per agent.
Cons
- The public pages do not provide deep documentation for every feature area, so some workflow details remain abstract.
- The source does not show broad integration coverage beyond the tools and services named on the site.
- Free tier limits apply to issues, projects, and human workspace members, so growing teams may need to upgrade.
FAQ
What is Aruvi used for?
Aruvi combines issue tracking, docs, knowledge, and AI agent access in one workspace so teams can keep work and context together.
Who is Aruvi for?
The docs page says it is designed for small product and engineering teams that want a modern tracker without tab overload and want to delegate work to AI coding tools with guardrails.
Which AI tools and integrations does Aruvi support?
Aruvi supports AI tools such as Claude Code, Codex, and Kiro through an MCP endpoint, and the pricing page also lists GitHub, Slack, and Discord as integrations.
What is the difference between Free and Pro?
The pricing page says every feature is included on both plans, while Pro removes usage caps from the Free plan. Free is limited to 500 issues, 2 projects, and 3 workspace members.
How quickly can you get started?
The docs say you can go from sign-in to your first agent-ready issue in about five minutes.
Quick Facts
- Category
- Issue tracker and lightweight wiki
- Primary users
- Small product and engineering teams
- AI workflow
- Agents can be assigned to issues and work through MCP-connected tools
- Billing
- Per workspace, not per seat or per agent
- Plans
- Free and Pro
- Website
- aruvi.dev