AgentRQ is a (optionally) human-in-the-loop, self learning closed loop task manager for agents. Agents can create and schedule tasks for themself and work on them on their own schedule.
In high level it comes with one supervisor MCP that controls workspaces(worker agents) and unlimited number of isolated workspace MCPs (self learning agents).
Each workspace/agent has a mission/persona for the agent. And self-learning-loop note.
I am using it about 6 weeks in production, and completed more than 500 tasks. I just released the opensource version(as is in production) under Apache 2.0 license.
Currently it supports Gemini CLI and Claude code. I am going to extend support all major agents soon.
Happy to answer any questions.
Interesting approach.
I’m especially curious about the “self-learning loop” — in practice, does it actually improve outcomes over time, or does it tend to reinforce suboptimal patterns?
And How much autonomy do the agents actually have in practice?
I’ve found that fully autonomous loops tend to need a lot of guardrails to stay useful.
How does a team setup look like? Maybe tested it with someone?
[flagged]
[dead]