Backlog-md 是 AI Skill Hub 本期精选Agent工作流之一。已获得 5.9k 颗 GitHub Star,综合评分 8.2 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
Backlog-md 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。
Backlog-md 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:npm 全局安装 npm install -g backlog.md # 方式二:npx 直接运行(无需安装) npx backlog.md --help # 方式三:项目依赖安装 npm install backlog.md # 方式四:从源码运行 git clone https://github.com/MrLesk/Backlog.md cd Backlog.md npm install npm start
# 命令行使用
backlog.md --help
# 基本用法
backlog.md [options] <input>
# Node.js 代码中使用
const backlog.md = require('backlog.md');
const result = await backlog.md.run(options);
console.log(result);
# backlog.md 配置说明 # 查看配置选项 backlog.md --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 BACKLOG.MD_CONFIG="/path/to/config.yml"
<p align="center"> <img src="./.github/backlog-logo.png" alt="Backlog.md logo" width="120"> </p>
Markdown‑native Task Manager & Kanban visualizer for any Git repository
AI agents write the code. You review the tasks: before, during, and after.
<p align="center"> <a href="https://www.npmjs.com/package/backlog.md"><img src="https://img.shields.io/npm/v/backlog.md?color=brightgreen" alt="npm version"></a> <a href="https://www.npmjs.com/package/backlog.md"><img src="https://img.shields.io/npm/dm/backlog.md" alt="npm downloads"></a> <a href="https://github.com/MrLesk/Backlog.md/blob/main/LICENSE"><img src="https://img.shields.io/github/license/MrLesk/Backlog.md" alt="MIT license"></a> <a href="https://github.com/MrLesk/Backlog.md"><img src="https://img.shields.io/github/stars/MrLesk/Backlog.md?style=social" alt="GitHub stars"></a> </p>
<p align="center"> <code>npm i -g backlog.md</code> </p>

---
Backlog.md turns any folder into a self‑contained project board powered by plain Markdown files and a zero‑config CLI.
.md file in your repobacklog board paints a live board in your shell; backlog board export creates shareable markdown reportsbacklog browser serves a local Kanban board with drag-and-drop and task editing formsbacklog search---
```bash
npm i -g backlog.md
<details> <summary><strong>Claude Code</strong></summary>
claude mcp add backlog --scope user -- backlog mcp start
</details>
<details> <summary><strong>Codex</strong></summary>
codex mcp add backlog -- backlog mcp start
</details>
<details> <summary><strong>Gemini CLI</strong></summary>
gemini mcp add backlog -s user backlog mcp start
</details>
<details> <summary><strong>Kiro</strong></summary>
kiro-cli mcp add --scope global --name backlog --command backlog --args mcp,start
</details>
<details> <summary><strong>Cursor / other MCP clients</strong></summary>
Use the manual JSON config below in your client's MCP settings.
</details>
Use the shared backlog server name everywhere. The server finds the active project from your client's MCP roots, and re-resolves when you switch workspace or worktree. A single user-scope server covers every repo.
<details> <summary><strong>Manual config</strong></summary>
{
"mcpServers": {
"backlog": {
"command": "backlog",
"args": ["mcp", "start"],
"env": {
"BACKLOG_CWD": "/absolute/path/to/your/project"
}
}
}
}
Set BACKLOG_CWD to pin the server to one project and stop workspace following. Use it to always target the same backlog, or when your client can't report MCP roots. If your IDE supports custom args but not env vars, you can also use ["mcp", "start", "--cwd", "/absolute/path/to/your/project"]. Until the server finds an initialized project, it serves backlog://init-required.
</details>
[!IMPORTANT] When adding the MCP server manually, add a short instruction to your CLAUDE.md/AGENTS.md files telling agents to readbacklog://workflow/overview. This step is not required when usingbacklog initas it adds these instructions automatically. For CLI-based setups, usebacklog instructions overviewto fetch the current workflow guidance.
Once connected, agents can read the Backlog.md workflow instructions via backlog://workflow/overview, with detailed guides at backlog://workflow/task-creation, backlog://workflow/task-execution, and backlog://workflow/task-finalization. Use /mcp command in your AI tool (Claude Code, Codex, Kiro) to verify if the connection is working.
---
Backlog.md works with zero configuration. Settings merge from CLI flags, then the project config file (backlog.config.yml when present, otherwise backlog/config.yml or .backlog/config.yml), then built‑in defaults.
Run backlog config with no arguments to launch the interactive wizard (the same experience triggered from backlog init advanced setup). It walks through cross-branch accuracy (checkActiveBranches, remoteOperations, activeBranchDays), Git workflow (autoCommit, bypassGitHooks), ID formatting (zeroPaddedIds), editor integration (defaultEditor), Definition of Done defaults, and Web UI defaults (defaultPort, autoOpenBrowser). Skipping the wizard applies the safe built-in defaults, and rerunning backlog init or backlog config pre-populates prompts with your current values.
For filesystem-only projects (backlog init --no-git), the saved config forces checkActiveBranches=false, remoteOperations=false, and autoCommit=false so CLI, Web, and MCP local-file workflows do not depend on a Git repository.
Launch a web interface for visual task management on the local machine. The server listens on 127.0.0.1 and is not reachable from other devices on the LAN or VPN:
```bash
Full command reference covering task management, search, board, docs, decisions, and more: CLI-INSTRUCTIONS.md
Quick examples: backlog, backlog instructions, backlog task create, backlog task list, backlog task edit, backlog milestone add, backlog milestone rename, backlog milestone remove, backlog search, backlog board, backlog browser.
Full help: backlog --help
---
CLI instructions are the default AI setup. MCP remains supported for AI coding assistants like Claude Code, Codex, Gemini CLI and Kiro when you explicitly prefer an MCP connector. You can run backlog init (even if you already initialized Backlog.md) and choose MCP integration, or follow the manual steps below.
Backlog.md 是一款专为 Git 仓库设计的 Markdown 原生任务管理器与 Kanban 可视化工具。它允许开发者直接通过普通的 .md 文件来管理所有的 Issue,实现任务管理与代码仓库的高度统一。无论您使用 npm、bun、brew 还是 Nix,都可以轻松安装并开始使用这款轻量级、开发者友好的任务管理工具。
Backlog.md 具备强大的 AI 就绪能力,完美支持 Claude Code、Gemini CLI、Codex 及任何兼容 MCP 或 CLI 的 AI 助手。它不仅支持在终端中通过 `backlog board` 实时渲染看板,还提供了一个现代化的 Web UI(`backlog browser`)进行可视化任务管理。此外,它还拥有强大的搜索功能,确保您的任务管理既符合 Markdown 原生习惯,又具备现代工具的效率。
您可以通过多种包管理器快速安装 Backlog.md。推荐使用 `bun i -g backlog.md` 进行全局安装;此外,也支持使用 `brew install backlog-md` (macOS) 或通过 `nix run github:MrLesk/Backlog.md` 直接运行。安装完成后,即可在您的 Git 项目中使用。
Backlog.md 提供了灵活的客户端集成指南。如果您使用 Claude Code、Codex 或 Gemini CLI 等 AI 编程助手,可以通过简单的 MCP 命令将 Backlog 接入工作流,实现 AI 驱动的任务管理。通过执行相应的 `mcp add` 指令,您可以让 AI 直接读取并操作您的任务列表。
Backlog.md 支持灵活的配置管理。您可以手动在配置文件中设置 `mcpServers`,并通过 `BACKLOG_CWD` 环境变量将服务器固定在特定项目路径,以解决客户端无法报告 MCP roots 的问题。配置优先级遵循:CLI 参数 > 项目配置文件(如 `backlog.config.yml`) > 内置默认设置。
Backlog.md 提供了一个响应迅速的现代 Web 界面,通过 `backlog browser` 命令即可启动,实现直观的任务可视化管理。同时,它拥有完整的 CLI 命令集,涵盖了任务创建(task create)、列表查看(task list)、编辑(task edit)、里程碑管理(milestone)以及决策记录(decisions)等全方位功能。
在工作流方面,Backlog.md 默认采用 CLI 指令进行 AI 设置。如果您更倾向于使用 MCP 连接器,可以针对 Claude Code、Codex、Gemini CLI 和 Kiro 等 AI 助手进行显式配置。您可以通过运行 `backlog init` 并选择 MCP 集成模式,或者按照手动配置指南来完成 AI 辅助开发环境的搭建。
高质量的开源AI工作流管理工具
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。
经综合评估,Backlog-md 在Agent工作流赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | Backlog-md |
| 原始描述 | 开源AI工作流:Backlog.md - A tool for managing project collaboration between humans and AI Age。⭐5.9k · TypeScript |
| Topics | workflowagentagentic-aimanagementmarkdown |
| GitHub | https://github.com/MrLesk/Backlog.md |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-07-01 · 更新时间:2026-07-03 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
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