AI Skill Hub 强烈推荐:连接洋葱 是一款优质的Agent工作流。已获得 1.2k 颗 GitHub Star,AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。
连接洋葱 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
连接洋葱 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:pip 安装(推荐)
pip install connectonion
# 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 connectonion
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/openonion/connectonion
cd connectonion
pip install -e .
# 验证安装
python -c "import connectonion; print('安装成功')"
# 命令行使用
connectonion --help
# 基本用法
connectonion input_file -o output_file
# Python 代码中调用
import connectonion
# 示例
result = connectonion.process("input")
print(result)
# connectonion 配置文件示例(config.yml) app: name: "connectonion" debug: false log_level: "INFO" # 运行时指定配置文件 connectonion --config config.yml # 或通过环境变量配置 export CONNECTONION_API_KEY="your-key" export CONNECTONION_OUTPUT_DIR="./output"
<img src="https://www.connectonion.com/favicon.png" width="96" height="96" alt="ConnectOnion logo">
co rem init
One line installs co and sets it up: your agent identity, an OpenOnion key with starter credit, and the skills and command index that tell Codex and Claude Code about co.
curl -fsSL https://raw.githubusercontent.com/openonion/connectonion/main/install.sh | sh
It uses uv (installing it if needed) to keep co out of your system Python, so you need no Python of your own. Then connect what you use:
co auth google # Gmail and Google Calendar
co auth microsoft # Outlook
co commands # everything else; add --help to any
<details> <summary>With pip instead</summary>
```bash
$ co linear --help $ co linear issues -n 3 3 open issues CON-3 Todo No priority - 2026-10-01 Import your data CON-1 Todo No priority - 2026-10-01 Get familiar with Linear CON-4 Todo No priority - 2026-10-01 Set up your teams Next: co linear issue CON-3 ```
An agent's work lives in many places: your inbox, your calendar, a chat thread, a web page behind a login, a file on the NAS, an issue tracker, its own notes, another agent. ConnectOnion strings all of that context together with one command line, co. The agent reads a thread with one command, looks something up with the next, and writes the answer with a third. Every step is a line in its shell, and the output of one is the input of the next.
Watch the 43-second film — real runs from co 1.8.10.
<details> <summary><strong>See search, Linear, Slack, Canny and audit examples</strong></summary>
<picture> <source media="(prefers-reduced-motion: reduce) and (max-width: 600px)" srcset="https://www.connectonion.com/aha-search-mobile-poster.png"> <source media="(prefers-reduced-motion: reduce)" srcset="https://www.connectonion.com/aha-search-poster.png"> <source media="(max-width: 600px)" srcset="https://www.connectonion.com/aha-search-mobile.gif"> <img alt="co search ConnectOnion printing an answer with its GitHub and website sources" src="https://www.connectonion.com/aha-search.gif"> </picture>
co search ConnectOnion answers from the web and cites its sources. The default engine uses your own search key, then ConnectOnion credits; --engine ddg is free.
<picture> <source media="(prefers-reduced-motion: reduce) and (max-width: 600px)" srcset="https://www.connectonion.com/demos/linear-mobile-poster.png?v=2"> <source media="(prefers-reduced-motion: reduce)" srcset="https://www.connectonion.com/demos/linear-poster.png?v=2"> <source media="(max-width: 600px)" srcset="https://www.connectonion.com/demos/linear-mobile.gif?v=2"> <img alt="co linear issues listing three open issues, then co linear create previewing a new issue" src="https://www.connectonion.com/demos/linear.gif?v=2"> </picture>
List your Linear issues, then create one. The create shows a preview and changes nothing until --yes.
<picture> <source media="(prefers-reduced-motion: reduce) and (max-width: 600px)" srcset="https://www.connectonion.com/demos/slack-mobile-poster.png?v=2"> <source media="(prefers-reduced-motion: reduce)" srcset="https://www.connectonion.com/demos/slack-poster.png?v=2"> <source media="(max-width: 600px)" srcset="https://www.connectonion.com/demos/slack-mobile.gif?v=2"> <img alt="co slack search finding a message, then co slack thread reading its replies" src="https://www.connectonion.com/demos/slack.gif?v=2"> </picture>
Search Slack, then read the whole thread. Each message carries the id that co slack send --reply-to takes.
<picture> <source media="(prefers-reduced-motion: reduce) and (max-width: 600px)" srcset="https://www.connectonion.com/demos/canny-mobile-poster.png?v=2"> <source media="(prefers-reduced-motion: reduce)" srcset="https://www.connectonion.com/demos/canny-poster.png?v=2"> <source media="(max-width: 600px)" srcset="https://www.connectonion.com/demos/canny-mobile.gif?v=2"> <img alt="co canny posts ranked by votes, then co canny status previewing a move to planned" src="https://www.connectonion.com/demos/canny.gif?v=2"> </picture>
Your most-voted Canny requests, and a status change you preview before voters hear about it.
<picture> <source media="(prefers-reduced-motion: reduce) and (max-width: 600px)" srcset="https://www.connectonion.com/demos/audit-mobile-poster.png?v=2"> <source media="(prefers-reduced-motion: reduce)" srcset="https://www.connectonion.com/demos/audit-poster.png?v=2"> <source media="(max-width: 600px)" srcset="https://www.connectonion.com/demos/audit-mobile.gif?v=2"> <img alt="co audit co linear scoring 13 help pages and reporting fit for an agent harness" src="https://www.connectonion.com/demos/audit.gif?v=2"> </picture>
co audit scores a CLI's help pages the way an agent reads them. CI runs it on every co page, and it works on any other CLI too.
</details>
- Every agent already has one. Claude Code, Codex, Cursor and any agent you write can run a shell command. There is no server to start, no client to configure and nothing to install into the agent. - It costs no context until it is used. Tool schemas sit in an agent's prompt whether it needs them or not. A command line is discovered on demand: bare co lists the groups, co gmail --help explains one, and only the page the agent opens enters its context. - The help page is the prompt. Each --help says what the command does, what it changes (Read-only, Sends, Changes, Deletes…) and gives a real example, so there is no separate skill file or schema to keep in step. CI fails any page that loses one of those parts (co audit). - Every answer names the next step. A list ends with how to open the first item, a missing login ends with Next: co auth microsoft, a typo ends with the command you meant. The agent recovers without asking you. - It composes. Commands pipe into each other and into scripts, and the transcript is the same commands you would type, so you can read exactly what the agent did, and run it yourself.
```console
ConnectOnion 是一个 AI 框架,提供了一个强大的 AI 编程助手、前端和后端功能,以及多种工具和插件。它支持 Python 3.10+,并且可以通过 pip 安装。
ConnectOnion 的主要功能包括:内置 AI 编程助手、前端和后端功能、多种工具和插件、自动压缩上下文、子代理和超轻量工作模式等。这些功能使得 ConnectOnion 成为一个强大的 AI 框架。
ConnectOnion 需要 Python 3.10+,并且需要通过环境变量设置 OpenAI API 密钥。
ConnectOnion 可以通过 pip 安装,使用以下命令:`pip install connectonion`。如果需要通过 Docker 部署,可以使用 `docker pull connectonion` 和 `docker run -p 8080:8080 connectonion` 等命令。
ConnectOnion 的使用教程包括:创建一个 Agent 实例、设置 OpenAI API 密钥、使用内置工具和插件等。具体使用方法请参考 ConnectOnion 的文档。
ConnectOnion 的配置包括:设置环境变量、设置 MCP 等。具体配置方法请参考 ConnectOnion 的文档。
ConnectOnion 的 API 包括:设置 OpenAI API 密钥、使用内置工具和插件等。具体 API 方法请参考 ConnectOnion 的文档。
ConnectOnion 的工作流包括:12 个生命周期钩子和插件系统。具体工作流和模块请参考 ConnectOnion 的文档。
ConnectOnion 的常见问题包括:如何设置 OpenAI API 密钥、如何使用内置工具和插件等。具体答案请参考 ConnectOnion 的 FAQ。
高质量的开源AI工作流框架
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。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
总体来看,连接洋葱 是一款质量优秀的Agent工作流,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 | connectonion |
| 原始描述 | 开源AI工作流:The Best AI Agent Framework for Agent Collaboration. 。⭐1.2k · Python |
| Topics | AI工作流智能体 |
| GitHub | https://github.com/openonion/connectonion |
| License | Apache-2.0 |
| 语言 | Python |
收录时间:2026-06-04 · 更新时间:2026-06-05 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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