backlog

Backlog

Manage the backlog of your project with just markdown in git. The goal is to provide a frictionless collaboration between AI agents and developers

Backlog is a zero-configuration task manager written in Go where tasks live inside a Git repository. It leverages plain Markdown files for task storage and a comprehensive command-line interface (CLI) for interaction. This design makes it exceptionally well-suited for AI agents thanks to its MCP (Model Context Protocol) integration.

The system is designed to be offline-first and completely portable, as the entire project state is contained within the Git repository.

• Introduction
• Features
• Quick Start
  • Installation
  • Initialize Your Project
• AI Agent Integration
  • Available Tools
  • Usage with AI Agents
• Usage Examples
  • AI Integration (MCP Server)
  • Basic Task Creation
  • Hierarchical Task Structure
  • Advanced Task Creation
  • Task Management
• Task File Structure
• Project Structure
• Development
• Inspiration

Introduction

THIS SECTION IS FOR HUMANS

The context window deteriorates rapidly on large-scale projects. A workaround I found is to ask the agent to make a plan for X, write it to a markdown file and keep it updated with the ongoing tasks. It is a simple prompt. This technique has worked incredibly well, making refactoring and other significant code changes more resilient to failures, retries or rate-limiting from the model.

However, this approach cluttered the repository's root directory with files like plan.md or refactor_this_big_codebase.md. To solve this, I created an MCP server that I could understand and trust to handle these details, which has provided a much better experience when using AI tools for complex tasks. Through trial and error, I fine-tuned the parameters to get the results I needed from this project.

You can see an example in the .backlog folder of this repo. It has been generated with this prompt :

If you were to recreate this project from scratch, make a plan and break it down into tasks using backlog break down prompt.
Write that plan to a markdown file called "./docs/plan.md".
Check that the plan in ./docs/plan.md is consistent with the list of tasks in the backlog.
Add implementation plan to relevant tasks
All tasks should have at least one acceptance criteria.

Read the full instructions for backlog: ./internal/mcp/prompt.md

Assuming that backlog is registered as an MCP server with the command backlog mcp. See config examples in .gemini, .claude and .vscode.

Interestingly, while this codebase is mostly hand-written, the documentation, comments, examples, and some of the tests were generated with AI. I found that AI agents yield better results when the structure of the project is already there and exactly how you want it, with the libraries you want, in the code style you want. After that, it gets easier.

I used a few tools for learning purposes, all are paid tier:

• gemini-2.5-pro
• claude-sonnet4
• github-copilot
• amp

To instruct your AI tools to use backlog, see ./internal/mcp/prompt.md

The rest of this document is mostly generated by AI (except the Inspiration section). Enjoy.

Features

• Task Management: Create, edit, list, and view tasks with rich metadata
• Hierarchical Structure: Support for parent-child-grandchild task relationships (T01 → T01.01 → T01.01.01)
• Search & Filter: Find tasks by content, status, parent relationships, and labels
• AI-Friendly: MCP server integration for seamless AI agent collaboration
• Git Integration: Tasks are stored as Markdown files with automatic Git commits
• Offline-First: Works completely offline with local Git repository storage
• Portable: Entire project state contained within the Git repository
• Zero Configuration: No setup files or databases required

Quick Start

Installation

# Build from source
git clone https://github.com/veggiemonk/backlog
cd backlog
go build .

# Or install directly
go install github.com/veggiemonk/backlog@latest

You can also download the binary from the release page.

Initialize Your Project

No initialization is needed.

AI Agent Integration

Backlog includes a Model Context Protocol (MCP) server that exposes task management capabilities to AI agents:

Available Tools

• task_create: Create new tasks with full metadata
• task_list: List and filter tasks
• task_view: Get detailed task information
• task_edit: Update existing tasks
• task_search: Search tasks by content
• task_archive: Archive tasks so they are not displayed anymore but still lives in the repository.

Usage with AI Agents

# Start MCP server for AI integration
backlog mcp --http --port 8106
# Start MCP server using STDIO
backlog mcp

# AI agents can now:
# - Create tasks from conversation context
# - Break down large tasks into subtasks
# - Update task status and assignments
# - Search and analyze task patterns

Usage Examples

AI Integration (MCP Server)

# Start MCP server for AI agents (backlog mcp --http transport)
backlog mcp --http  # default port 8106
backlog mcp --http --port 8106 # specify the port

# Start MCP server (stdio transport)
backlog mcp

See this repository .gemini, .claude and .vscode for example configurations.

Basic Task Creation

# Simple task
backlog create "Fix the login button styling"

# Task with description
backlog create "Implement password reset" \
  -d "Users should be able to request a password reset link via email"

# Task with assignees and labels
backlog create "Update dependencies" \
  -a "alex" -a "jordan" \
  -l "maintenance,backend,security" \
  --priority "high"

Hierarchical Task Structure

# Create parent task
backlog create "Implement User Authentication"
# → Creates T01-implement_user_authentication.md

# Create subtask
backlog create "Add Google OAuth login" -p "T01"
# → Creates T01.01-add_google_oauth_login.md

# Create sub-subtask
backlog create "OAuth token validation" -p "T01.01"
# → Creates T01.01.01-oauth_token_validation.md

Advanced Task Creation

# Comprehensive task with acceptance criteria
backlog create "Build reporting feature" \
  -d "Create monthly performance reports in PDF format" \
  -a "drew" \
  -l "feature,frontend,backend" \
  --priority "high" \
  --ac "Report generation logic is accurate" \
  --ac "Users can select date range" \
  --ac "PDF export works correctly" \
  -p "23"

Task Management

# List all tasks
backlog list

# Filter by status
backlog list --status "todo"
backlog list --status "in-progress"
backlog list --status "done"

# Filter by parent (show subtasks)
backlog list --parent "T01"

# View specific task
backlog view T01.02

# Edit task
backlog edit T01 --status "in-progress" --assignee "alex"

Task File Structure

Tasks are stored as Markdown files with YAML front matter in the .backlog/ directory:

---
id: "01.02.03"
title: "Implement OAuth integration"
status: "todo"
parent: "01.02"
assigned: ["alex", "jordan"]
labels: ["feature", "auth", "backend"]
priority: "high"
created_at: 2024-01-01T00:00:00Z
updated_at: 2024-01-01T00:00:00Z
---

## Description

Integrate OAuth authentication with Google and GitHub providers.

## Acceptance Criteria

<!-- AC:BEGIN -->

- [ ] #1 Google OAuth integration works
- [ ] #2 GitHub OAuth integration works
- [x] #3 OAuth scope validation implemented
<!-- AC:END -->

## Implementation Plan

1. Setup OAuth app registrations
2. Implement OAuth flow handlers
3. Add token validation
4. Write integration tests

## Implementation Notes

- Use oauth2 package for Go
- Store tokens securely
- Handle refresh token rotation

File Naming Convention: T{ID}-{slugified-title}.md

• T01-implement_user_auth.md (root task)
• T01.01-setup_oauth.md (subtask)
• T01.01.01-google_oauth.md (sub-subtask)

Project Structure

.backlog/                     # Task storage directory
├── T01-user_auth.md         # Root task (ID: T01)
├── T01.01-oauth_setup.md    # Subtask (ID: T01.01)
├── T01.02-password_reset.md # Sibling subtask (ID: T01.02)
└── T02-frontend_redesign.md # Another root task (ID: T02)

Development

# Build the project
make build

# Run tests
make test

# Generate CLI documentation
make docs

# Lint code
make lint

Inspiration

This project is inspired by

• Backlog.md
• TaskWing

but backlog aims to be simpler and handle fewer use cases while remaining AI-friendly.

For the MCP server, a lot of good examples are in go-sdk.

For real production use cases, GoogleCloudPlatform/gke-mcp is highly recommended.