AI工作流
经 AI Skill Hub 精选评估，AI工作流 获评「强烈推荐」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。
📚 深度解析
AI工作流 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.0 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
AI工作流 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。
📖 中文文档
AI工作流 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。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 克隆仓库 git clone https://github.com/mixpeek/amux cd amux # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 查看帮助 amux --help # 基本运行 amux [options] <input> # 详细使用说明请查阅文档 # https://github.com/mixpeek/amux
# amux 配置说明 # 查看配置选项 amux --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export AMUX_CONFIG="/path/to/config.yml"
简介
<img src="site/github-header.svg" alt="amux — The Agent Control Plane" width="1280"/>
<p align="center"> <a href="https://github.com/mixpeek/amux/stargazers"><img src="https://img.shields.io/github/stars/mixpeek/amux?style=flat-square&color=f5c518" alt="GitHub stars"/></a> <a href="https://github.com/mixpeek/amux/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT%20%2B%20Commons%20Clause-blue?style=flat-square" alt="License"/></a> <a href="https://amux.io"><img src="https://img.shields.io/badge/site-amux.io-orange?style=flat-square" alt="Website"/></a> <a href="https://apps.apple.com/us/app/amux-agent-multiplexer/id6760410435"><img src="https://img.shields.io/badge/iOS-App%20Store-black?style=flat-square&logo=apple" alt="iOS App"/></a> <a href="https://amux.io/changelog/"><img src="https://img.shields.io/badge/changelog-amux.io%2Fchangelog-green?style=flat-square" alt="Changelog"/></a> </p>
amux is the open-source control plane for AI coding agents. Run an AI engineering team: dozens of parallel workers (Claude Code, Codex, Gemini CLI, OpenCode, Ollama) coordinated from one web dashboard or your phone. Local-first, self-hosted, SQLite-backed, one Rust binary.
What the fleet gets that a single agent never had:
- Atomic tasks: a shared kanban board where claiming is compare-and-swap, so two workers can never grab the same card;
donerequires evidence,verifiedrequires a peer check - Worker awareness: every worker sees the fleet (who is live, what they own, what they are doing) and can peek into any peer's terminal before interrupting
- @ each other: origin-stamped inter-worker messaging; the server records the true sender, so provenance is a fact rather than a claim
- Steering: type into any running session from the dashboard or phone; redirect mid-task without stopping the run
- Schedules & loops: cron-style recurring prompts (
daily at 9am,every 15m) plus self-pacing autonomous loops for overnight runs - Groups & scope: workers organize into lanes that share memory, environment, and gates; settings resolve card → worker → group → global
- Model switching: swap the model or provider on a running worker; pick the brain per task, keep the context
- Message history: every prompt, reply, and delivery is a ledger row you can fetch later
- Self-healing: a watchdog that auto-compacts, restarts crashed sessions, and replays the last message
amux.io · Getting started · FAQ · Blog
<p align="center"><a href="https://amux.io"><img src="site/amux.gif" alt="amux dashboard — run parallel agent sessions from one board" width="920"/></a></p>
After first install
Once installed, day-to-day commands are short:
make run # rebuild + reinstall; the running server self-adopts in ~5s
make dev # run against a scratch DB (safe for testing migrations)
make status # launchd + /health at a glance
make restart # kick the launchd-managed server
make check # cargo check + JS syntax (fast, no link)
make test # clippy + cargo testmake run is the command after git pull — it rebuilds release, installs the binary, and the launchd-managed server picks it up automatically. make dev is for working on migrations or features you don't want touching the live DB.
Requirements: tmux 3.2+, and at least one of Claude Code, Codex CLI, or Gemini CLI. The Rust toolchain is installed via rustup if you don't have it (with your confirmation).
Quickstart — one command
git clone https://github.com/mixpeek/amux && cd amux && ./install.shThat is the whole setup. The installer checks prerequisites (Rust toolchain, tmux; it prompts before installing anything), builds the workspace, installs the server and CLI to ~/.local/bin, loads the launchd agents on macOS, mints ~/.amux (DB, TLS, auth token) on first boot, waits for /health, and prints:
Dashboard https://localhost:8824
Auth token ~/.amux/auth_token
CLI amux-rs --url https://localhost:8824 healthOpen https://localhost:8824, accept the self-signed cert warning once, and add your first worker from the dashboard. Re-running ./install.sh upgrades in place and never touches your data; ./uninstall.sh removes the binaries and agents and leaves ~/.amux alone.
model: claude-haiku-4-5 # optional per-file override
Configuration
Server configuration lives in ~/.amux/server.env (plain KEY=value; process env wins). Highlights:
| Variable | What |
|---|---|
AMUX_RS_PORT | server port (installer sets 8824) |
AMUX_HOME | data dir (default ~/.amux) |
AMUX_DB | SQLite path (default $AMUX_HOME/amux.db) |
AMUX_HERDR_SESSION | herdr session hosting amux workspaces (enables the herdr backend) |
AMUX_REQLOG_RETAIN_DAYS | request-log retention (default 14) |
AMUX_SCOPE_WRITE_AGENTS | 1 lets agent sessions write group/global scope layers (default: only their own worker layer) |
server.env.example documents the full set. Never commit your real server.env — several values are secrets.
Endpoints
Rooted at the same files root as the Files browser (AMUX_FILES_ROOT, else $HOME):
| Method + path | What |
|---|---|
POST /api/files/mdai/run {path} | Resolve and run the DAG, record a history entry, return the entry node's latest output plus the upstream-first node order |
GET /api/files/mdai | List every .mdai file under the files root with metadata (source count, model, title, mtime, last run time) |
GET /api/files/mdai/history?path=<rel> | The node's run history, newest first |
POST /api/files/mdai/connect {source, target, prompt?} | Append a source connection to the target's frontmatter, writing a sensible default edge prompt when none is given |
Run a node (path is relative to the files root):
curl -sk -X POST https://localhost:8824/api/files/mdai/run \
-H 'Content-Type: application/json' \
-d '{"path":"weekly.mdai"}'Connect a source into a target (writes the edge into weekly.mdai's frontmatter):
curl -sk -X POST https://localhost:8824/api/files/mdai/connect \
-H 'Content-Type: application/json' \
-d '{"source":"notes/meeting.md","target":"weekly.mdai","prompt":"Extract the decisions."}'The dashboard's directory-view UI for creating and connecting .mdai files is tracked separately (AMUX-3245) and will be documented when it ships.
高质量的开源AI工作流项目，支持多个AI编码代理
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
👥 适合人群
🎯 使用场景
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
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。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
🔗 相关工具推荐
❓ 常见问题 FAQ
AI Skill Hub 点评：AI工作流 的核心功能完整，质量优秀。对于自动化工程师和运维人员来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | amux |
| 原始描述 | 开源AI工作流：Open-source Claude Code agent multiplexer — run dozens of parallel AI coding age。⭐244 · HTML |
| Topics | ai-agentsautomationclaudehtml |
| GitHub | https://github.com/mixpeek/amux |
| License | NOASSERTION |
| 语言 | HTML |
收录时间：2026-06-19 · 更新时间：2026-06-20 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。