backlog.md

<h1 align="center">Backlog.md</h1> <p align="center">Markdown‑native Task Manager & Kanban visualizer for any Git repository</p> <p align="center"> <code>npm i -g backlog.md</code> or <code>bun add -g backlog.md</code> or <code>brew install backlog-md</code> or <code>nix run github:MrLesk/Backlog.md</code> </p> ![Backlog demo GIF using: backlog board](./.github/backlog.gif) --- > **Backlog.md** turns any folder with a Git repo into a **self‑contained project board** > powered by plain Markdown files and a zero‑config CLI. > Built for **spec‑driven AI development** — structure your tasks so AI agents deliver predictable results. ## Features * 📝 **Markdown-native tasks** -- manage every issue as a plain `.md` file * 🤖 **AI-Ready** -- Works with Claude Code, Gemini CLI, Codex, Kiro & any other MCP or CLI compatible AI assistants * 📊 **Instant terminal Kanban** -- `backlog board` paints a live board in your shell * 🌐 **Modern web interface** -- `backlog browser` launches a sleek web UI for visual task management * 🔍 **Powerful search** -- fuzzy search across tasks, docs & decisions with `backlog search` * 📋 **Rich query commands** -- view, list, filter, or archive tasks with ease * ✅ **Definition of Done defaults** -- add a reusable checklist to every new task * 📤 **Board export** -- `backlog board export` creates shareable markdown reports * 🔒 **100 % private & offline** -- backlog lives entirely inside your repo and you can manage everything locally * 💻 **Cross-platform** -- runs on macOS, Linux, and Windows * 🆓 **MIT-licensed & open-source** -- free for personal or commercial use --- ## <img src="./.github/5-minute-tour-256.png" alt="Getting started" width="28" height="28" align="center"> Getting started ```bash # Install bun i -g backlog.md # or: npm i -g backlog.md # or: brew install backlog-md # Initialize in any git repo backlog init "My Awesome Project" ``` The init wizard will ask how you want to connect AI tools: - **MCP connector** (recommended) — auto-configures Claude Code, Codex, Gemini CLI, Kiro or Cursor and adds workflow instructions for your agents. - **CLI commands** — creates instruction files (CLAUDE.md, AGENTS.md, etc.) so agents use Backlog via CLI. - **Skip** — no AI setup; use Backlog.md purely as a task manager. All data is saved under the `backlog` folder as human-readable Markdown files (e.g. `task-10 - Add core search functionality.md`). --- ### Working with AI agents This is the recommended flow for Claude Code, Codex, Gemini CLI, Kiro and similar tools — following the **spec‑driven AI development** approach. After running `backlog init` and choosing the MCP or CLI integration, work in this loop: **Step 1 — Describe your idea.** Tell the agent what you want to build and ask it to split the work into small tasks with clear descriptions and acceptance criteria. **🤖 Ask your AI Agent:** > I want to add a search feature to the web view that searches tasks, docs, and decisions. Please decompose this into small Backlog.md tasks. > [!NOTE] > **Review checkpoint #1** — read the task descriptions and acceptance criteria. **Step 2 — One task at a time.** Work on a single task per agent session, one PR per task. Good task splitting means each session can work independently without conflicts. Make sure each task is small enough to complete in a single conversation. You want to avoid running out of context window. **Step 3 — Plan before coding.** Ask the agent to research and write an implementation plan in the task. Do this right before implementation so the plan reflects the current state of the codebase. **🤖 Ask your AI Agent:** > Work on BACK-10 only. Research the codebase and write an implementation plan in the task. Wait for my approval before coding. > [!NOTE] > **Review checkpoint #2** — read the plan. Does the approach make sense? Approve it or ask the agent to revise. **Step 4 — Implement and verify.** Let the agent implement the task. > [!NOTE] > **Review checkpoint #3** — review the code, run tests, check linting, and verify the results match your expectations. If the output is not good enough: clear the plan/notes/final summary, refine the task description and acceptance criteria, and run the task again in a fresh session. --- ### Working without AI agents Use Backlog.md as a standalone task manager from the terminal or browser. ```bash # Create and refine tasks backlog task create "Render markdown as kanban" backlog task edit BACK-1 -d "Detailed context" --ac "Clear acceptance criteria" # Track work backlog task list -s "To Do" backlog search "kanban" backlog board # Work visually in the browser backlog browser ``` You can switch between AI-assisted and manual workflows at any time — both operate on the same Markdown task files. It is recommended to modify tasks via Backlog.md commands (CLI/MCP/Web) rather than editing task files manually, so field types and metadata stay consistent. **Learn more:** [CLI reference](CLI-INSTRUCTIONS.md) | [Advanced configuration](ADVANCED-CONFIG.md) --- ## <img src="./.github/web-interface-256.png" alt="Web Interface" width="28" height="28" align="center"> Web Interface Launch a modern, responsive web interface for visual task management: ```bash # Start the web server (opens browser automatically) backlog browser # Custom port backlog browser --port 8080 # Don't open browser automatically backlog browser --no-open ``` **Features:** - Interactive Kanban board with drag-and-drop - Task creation and editing with rich forms - Interactive acceptance criteria editor with checklists - Real-time updates across all views - Responsive design for desktop and mobile - Task archiving with confirmation dialogs - Seamless CLI integration - all changes sync with markdown files ![Web Interface Screenshot](./.github/web.jpeg) --- ## 🔧 MCP Integration (Model Context Protocol) The easiest way to connect Backlog.md to AI coding assistants like Claude Code, Codex, Gemini CLI and Kiro is via the MCP protocol. You can run `backlog init` (even if you already initialized Backlog.md) to set up MCP integration automatically, or follow the manual steps below. ### Client guides <details> <summary><strong>Claude Code</strong></summary> ```bash claude mcp add backlog --scope user -- backlog mcp start ``` </details> <details> <summary><strong>Codex</strong></summary> ```bash codex mcp add backlog backlog mcp start ``` </details> <details> <summary><strong>Gemini CLI</strong></summary> ```bash gemini mcp add backlog -s user backlog mcp start ``` </details> <details> <summary><strong>Kiro</strong></summary> ```bash kiro-cli mcp add --scope global --name backlog --command backlog --args mcp,start ``` </details> Use the shared `backlog` server name everywhere – the MCP server auto-detects whether the current directory is initialized and falls back to `backlog://init-required` when needed. ### Manual config ```json { "mcpServers": { "backlog": { "command": "backlog", "args": ["mcp", "start"] } } } ``` > [!IMPORTANT] > When adding the MCP server manually, you should add some extra instructions in your CLAUDE.md/AGENTS.md files to inform the agent about Backlog.md. > This step is not required when using `backlog init` as it adds these instructions automatically. > Backlog.md's instructions for agents are available at [`/src/guidelines/mcp/agent-nudge.md`](/src/guidelines/mcp/agent-nudge.md). Once connected, agents can read the Backlog.md workflow instructions via the resource `backlog://docs/task-workflow`. Use `/mcp` command in your AI tool (Claude Code, Codex, Kiro) to verify if the connection is working. --- ## <img src="./.github/cli-reference-256.png" alt="CLI Reference" width="28" height="28" align="center"> CLI reference Full command reference — task management, search, board, docs, decisions, and more: **[CLI-INSTRUCTIONS.md](CLI-INSTRUCTIONS.md)** Quick examples: `backlog task create`, `backlog task list`, `backlog task edit`, `backlog search`, `backlog board`, `backlog browser`. Full help: `backlog --help` --- ## <img src="./.github/configuration-256.png" alt="Configuration" width="28" height="28" align="center"> Configuration Backlog.md merges the following layers (highest → lowest): 1. CLI flags 2. `backlog/config.yml` (per‑project) 3. `~/backlog/user` (per‑user) 4. Built‑ins ### Interactive wizard (`backlog config`) Run `backlog config` with no arguments to launch the full interactive wizard. This is the same experience triggered from `backlog init` when you opt into advanced settings, and it walks through the complete configuration surface: - Cross-branch accuracy: `checkActiveBranches`, `remoteOperations`, and `activeBranchDays`. - Git workflow: `autoCommit` and `bypassGitHooks`. - ID formatting: enable or size `zeroPaddedIds`. - Editor integration: pick a `defaultEditor` with availability checks. - Web UI defaults: choose `defaultPort` and whether `autoOpenBrowser` should run. Skipping the wizard (answering "No" during init) applies the safe defaults that ship with Backlog.md: - `checkActiveBranches=true`, `remoteOperations=true`, `activeBranchDays=30`. - `autoCommit=false`, `bypassGitHooks=false`. - `zeroPaddedIds` disabled. - `defaultEditor` unset (falls back to your environment). - `defaultPort=6420`, `autoOpenBrowser=true`. Whenever you revisit `backlog init` or rerun `backlog config`, the wizard pre-populates prompts with your current values so you can adjust only what changed. ### Definition of Done defaults Set project-wide DoD items in the Web UI (Settings → Definition of Done Defaults) or edit `backlog/config.yml` directly: ```yaml definition_of_done: - Tests pass - Documentation updated - No regressions introduced ``` These items are added to every new task by default. You can add more on create with `--dod`, or disable defaults per task with `--no-dod-defaults`. Array values like `definition_of_done` must be edited in the config file or via the Web UI. For the full configuration reference (all options, commands, and detailed notes), see **[ADVANCED-CONFIG.md](ADVANCED-CONFIG.md)**. --- ### License Backlog.md is released under the **MIT License** – do anything, just give credit. See [LICENSE](LICENSE).