LLM Backlog FAQs

Question 1

How does LLM Backlog support AI agents?

Accepted Answer

It provides an MCP (Model Context Protocol) endpoint, enabling AI agents to programmatically read, create, update, and manage tasks, milestones, and documents using a rich set of available tools and defined roles.

Question 2

What is unique about its task storage method?

Accepted Answer

Tasks are stored as plain Markdown files with YAML frontmatter, committed directly to a Git repository. This ensures version control, human readability, and structured data that is easily manageable by both humans and AI.

Question 3

What features does the web UI offer for human users?

Accepted Answer

The lightweight web UI includes a Kanban board for visual task management, detailed task lists, milestone tracking, and documentation management. It supports role-based access with Google OAuth or API key authentication.

Question 4

Can LLM Backlog help with documentation and workflow?

Accepted Answer

Yes, it supports managing reference documentation and architectural decisions alongside tasks. It also defines a clear human-AI workflow, guiding agents through decomposition, refinement, planning, and implementation steps, with human oversight.

Question 5

What is LLM Backlog?

Accepted Answer

LLM Backlog is a project management tool designed for both human teams and AI agents. It manages project backlogs using Git-native Markdown files, providing a web UI for humans and an MCP endpoint for programmatic AI interaction.

LLM Backlog

LLM Backlog

Key Features

Use Cases

Key Features

Use Cases