AI Skill Hub 强烈推荐:Ouroboros智能体操作系统 是一款优质的AI工具。已获得 4.1k 颗 GitHub Star,AI 综合评分 8.2 分,在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案,这是一个值得深入了解的选择。
Ouroboros智能体操作系统 是一款基于 Python 开发的开源工具,专注于 Agent框架、MCP协议、智能体OS 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
Ouroboros智能体操作系统 是一款基于 Python 开发的开源工具,专注于 Agent框架、MCP协议、智能体OS 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:pip 安装(推荐)
pip install ouroboros
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install ouroboros
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/Q00/ouroboros
cd ouroboros
pip install -e .
# 验证安装
python -c "import ouroboros; print('安装成功')"
# 命令行使用
ouroboros --help
# 基本用法
ouroboros input_file -o output_file
# Python 代码中调用
import ouroboros
# 示例
result = ouroboros.process("input")
print(result)
# ouroboros 配置文件示例(config.yml) app: name: "ouroboros" debug: false log_level: "INFO" # 运行时指定配置文件 ouroboros --config config.yml # 或通过环境变量配置 export OUROBOROS_API_KEY="your-key" export OUROBOROS_OUTPUT_DIR="./output"
<p align="right"> <strong>English</strong> | <a href="./README.ko.md">한국어</a> | <a href="./README.zh-CN.md">简体中文</a> </p>
<p align="center"> <br/> ◯ ─────────── ◯ <br/><br/> <img src="./docs/images/ouroboros.png" width="520" alt="Ouroboros"> <br/><br/> <strong>O U R O B O R O S</strong> <br/><br/> ◯ ─────────── ◯ <br/> </p>
<p align="center"> <strong>Stop prompting. Start specifying.</strong> <br/> <sub>The <strong>Agent OS</strong> for replayable, specification-first AI coding workflows</sub> </p>
<p align="center"> <a href="https://pypi.org/project/ouroboros-ai/"><img src="https://img.shields.io/pypi/v/ouroboros-ai?color=blue" alt="PyPI"></a> <a href="https://github.com/Q00/ouroboros/actions/workflows/test.yml"><img src="https://img.shields.io/github/actions/workflow/status/Q00/ouroboros/test.yml?branch=main" alt="Tests"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green" alt="License"></a> </p>
<p align="center"> <a href="#quick-start">Quick Start</a> · <a href="#why-ouroboros">Why</a> · <a href="#what-you-get">Results</a> · <a href="#the-loop">How It Works</a> · <a href="#commands">Commands</a> · <a href="#from-wonder-to-ontology">Philosophy</a> </p>
Turn a vague idea into a verified, working codebase -- across Claude Code, Codex CLI, OpenCode, Hermes, Gemini, Kiro, Copilot, and Pi.
Ouroboros is an Agent OS for AI coding: a local-first runtime layer that turns non-deterministic agent work into a replayable, observable, policy-bound execution contract. It replaces ad-hoc prompting with a structured specification-first workflow: interview, crystallize, execute, evaluate, evolve.
---
Install — one command, everything auto-detected:
curl -fsSL https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.sh | bash
Build — open your AI coding agent and go:
> ooo interview "I want to build a task management CLI"
Works with Claude Code, Codex CLI, GitHub Copilot CLI, OpenCode, Hermes, Gemini, Kiro CLI, and Pi CLI. The installer detects Claude Code, Codex CLI, and Hermes CLI automatically and registers the MCP server where the host supports it. For OpenCode, Kiro, GitHub Copilot CLI, Gemini CLI, or Pi CLI, run ouroboros setup --runtime <opencode|kiro|copilot|gemini|pi> after installation. The Copilot CLI runtime live-discovers its model catalog via the GitHub Copilot models API and lets you pick a default during setup.
<details> <summary><strong>Kiro CLI quick start</strong></summary>
pip install 'ouroboros-ai[claude]'
ouroboros setup # detects Kiro CLI and registers MCP server
Set runtime in .env:
OUROBOROS_RUNTIME=kiro
Then use ooo commands inside a Kiro CLI session.
</details>
<details> <summary><strong>GitHub Copilot CLI quick start</strong></summary>
gh auth login # one-time GitHub auth (used for live model discovery)
pipx install 'ouroboros-ai[mcp]' # or: uv tool install 'ouroboros-ai[mcp]'
ouroboros setup --runtime copilot # discovers models live, picks a default,
# registers MCP server in ~/.copilot/mcp-config.json
Restart your Copilot CLI session, then use ooo commands inside it. Hyphenated Anthropic model IDs (claude-opus-4-6) used elsewhere in your config are auto-mapped to the dotted Copilot form (claude-opus-4.6) at runtime, so existing configs keep working when you switch backends.
See the GitHub Copilot CLI runtime guide for full details.
</details>
<details> <summary><strong>Other install methods</strong></summary>
Claude Code plugin only (no system package):
claude plugin marketplace add Q00/ouroboros && claude plugin install ouroboros@ouroboros Then run ooo setup inside a Claude Code session.
pip / uv / pipx:
pip install ouroboros-ai # base
pip install ouroboros-ai[claude] # + Claude Code deps
pip install ouroboros-ai[litellm] # + LiteLLM multi-provider
pip install ouroboros-ai[mcp] # + MCP server/client support
pip install ouroboros-ai[tui] # + Textual terminal UI
pip install ouroboros-ai[all] # everything (claude + litellm + mcp + tui)
ouroboros setup # configure runtime
Legacy compatibility: ouroboros-ai[dashboard] is still accepted as a compatibility alias/no-op; it does not install dashboard runtime payload. ouroboros-ai[all] includes that no-op alias only for compatibility.
See runtime guides: Claude Code · Codex CLI · Hermes · OpenCode · Kiro CLI · Gemini CLI · GitHub Copilot CLI · Pi JSON mode
</details>
<details> <summary><strong>Uninstall</strong></summary>
ouroboros uninstall
Removes all configuration, MCP registration, and data. See UNINSTALL.md for details.
</details>
Python >= 3.12 required. See pyproject.toml for the full dependency list.
---
AI coding tools are powerful -- but they solve the wrong problem when the input is unclear.
| Vanilla AI Coding | Ouroboros | |
|---|---|---|
| **Vague prompt** | AI guesses intent, builds on assumptions | Socratic interview forces clarity *before* code |
| **Spec validation** | No spec -- architecture drifts mid-build | Immutable seed spec locks intent; Ambiguity gate (<= 0.2) blocks premature code |
| **Evaluation** | "Looks good" / manual QA | 3-stage automated gate: Mechanical -> Semantic -> Multi-Model Consensus |
| **Rework rate** | High -- wrong assumptions surface late | Low -- assumptions surface in the interview, not in the PR review |
---
架构创新的Agent框架,MCP生态整合完善。代码活跃度高、社区关注度强。适合寻求替代提示词工程方案的开发者。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
总体来看,Ouroboros智能体操作系统 是一款质量优秀的AI工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | ouroboros |
| 原始描述 | 开源MCP工具:Agent OS: Stop prompting. Start specifying.。⭐4.1k · Python |
| Topics | Agent框架MCP协议智能体OS开源Python |
| GitHub | https://github.com/Q00/ouroboros |
| License | MIT |
| 语言 | Python |
收录时间:2026-05-16 · 更新时间:2026-05-19 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。