Ouroboros is an Agent OS that enables AI coding agents to improve autonomously through iterative evaluation and refinement loops.
The tool addresses the problem of AI agents requiring constant prompt engineering and manual tuning by implementing a self-improving cycle where agents run, encounter failures, and automatically become smarter across generations. Rather than relying on static prompts or manual grading criteria, Ouroboros uses interview-gated evaluation and staged evolution within a budgeted loop. The system operates as an MCP server and integrates with multiple LLM platforms and runtimes including Claude Code, Gemini CLI, Copilot, and others, allowing agents to learn from their own execution patterns without human intervention between cycles.
Ouroboros suits teams building replayable AI coding workflows who want to reduce manual prompt engineering overhead. It works best for projects where agents need to handle iterative tasks and improve performance over time through self-directed learning. The approach is particularly valuable when you want to separate task execution from the underlying engine logic, allowing different tasks to run on different hosts while sharing a common improvement mechanism.
The project's issue tracker is primarily populated by internal planning rather than external user requests, suggesting development is driven by the maintainers' roadmap. Responses to new issues and pull requests typically arrive within a day. Work focuses on enhancements, bug fixes, and OS-level improvements.
