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Q00/ouroboros

by Various

Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI, Ge

MCP

Q00/ouroboros

Added 8 Sept 2026

#agent-os #agentic-ai #ai-agent #ai-coding-agent #claude-code #cli #codex #coding-agent

Overview

Q00/ouroboros is an Agent OS that enables agents to improve themselves through an interview-gated, staged evaluation process with a budgeted evolution loop. It operates as an MCP server and supports 14 runtimes including Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, and Kiro. Written in Python, it is open-source with over 5,700 stars.

Best for

Best for
Developers building self-improving AI agents across multiple CLI environments.

Use cases

  • Automate agent self-improvement pipelines
  • Evaluate agent performance across multiple CLI runtimes
  • Integrate MCP-based agent orchestration into existing workflows

How to use

Install

pip install 'ouroboros-ai[mcp,tui]' && ouroboros setup --runtime claude-cli  # recommended MCP v2 default

Tested with

Claude Code, Continue, ChatGPT

Notes

Q00/ouroboros is an Agent OS that enables agents to improve themselves through an interview-gated, staged evaluation process with a budgeted evolution loop. It operates as an MCP server and supports 14 runtimes including Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, and Kiro. Written in Python, it is open-source with over 5,700 stars.

5,799 stars on GitHub. Last updated 2026-09-08. Licensed MIT.

Use cases

  • Automate agent self-improvement pipelines
  • Evaluate agent performance across multiple CLI runtimes
  • Integrate MCP-based agent orchestration into existing workflows

Pros

  • Broad runtime compatibility with 14 supported tools
  • Self-improving architecture with staged evaluation
  • Active open-source project with 5.8k stars

Cons

  • Requires Python environment and MCP setup
  • Evolution loop may need careful budget tuning
  • Not a standalone agent, depends on external runtimes

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Broad runtime compatibility with 14 supported tools
  • Self-improving architecture with staged evaluation
  • Active open-source project with 5.8k stars

Cons

  • Requires Python environment and MCP setup
  • Evolution loop may need careful budget tuning
  • Not a standalone agent, depends on external runtimes

Open-source & AI alternatives

Swap-in tools that solve the same job. Weigh the trade-offs before you commit.

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