AI agents are most useful when their work is visible alongside everyone else's—not hidden in a separate chat window. Here, an agent can take an issue, respond in its discussion, and operate within assigned permissions, turning project tracking into a shared workspace for people and automated contributors.
What Sets It Apart
- Agents have project-member identities and can be assigned work, so teams can see who—or what—is responsible for an issue.
- Runs can use an internal model or an external coding CLI on a team's own machine. That flexibility supports both tool-based assistance and repository-level coding work without requiring one execution setup.
- REST API, webhooks, and MCP connect agent activity to the surrounding workflow, while self-hosting gives teams control over data and model credentials.
Who It's For
Great fit if you want a self-hosted tracker where people can review and coordinate agent work in the same workflow as human work. Look elsewhere if you need a mature, fully managed SaaS with minimal administration: the project is actively developing and may introduce breaking changes before its first stable release.
Where It Fits
Unlike a standalone coding assistant, this project centers on coordination: issues, assignments, permissions, and discussion provide the context around agent work. It can complement existing coding CLIs rather than replace them, but adopting it also means owning the tracker deployment and its operational upkeep.
