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#Backlog
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#Backlog#AI Coding Agents#Task Management#Local First#MCP#Codex#Claude Code
Backlog is a local-first task and context manager for AI coding agents. It stores tasks, plans, docs, comments, memory, and actor attribution in a SQLite-backed workspace so fresh Claude Code, Codex, Cursor, or OpenCode sessions can pick up work without relying on one giant chat thread. Backlog is worth a look if your coding-agent workflow keeps losing task state between sessions. Instead of leaving project memory inside one long chat, it gives humans and agents a shared local queue backed by SQLite. What it does Backlog creates a local workspace with projects, tasks, plans, comments, docs, memory notes, attachments, and an activity log. The useful part is attribution: commands can write as human:name or ai:name, so parallel agent sessions do not become an anonymous blur. It also ships a CLI, web UI, MCP server, HTTP API, exports, and installable agent skills for Claude Code, Cursor, Codex, and OpenCode. That makes it practical for small teams or solo operators who already coordinate work through terminal-first AI agents. Why it is useful Keeps task context outside the model chat window Works locally, with no hosted account required Uses short task references like TASK-1 Stores plans and project memory alongside the queue Lets multiple agents leave attributed comments and status changes Can expose the same workspace through CLI, web UI, MCP, or HTTP Quick way to evaluate it Create a throwaway repo or copy of a small project. Install the binary or build from source. Run backlog init inside the project. Add two or three tasks with realistic titles and priorities. Connect one coding agent and make it read, claim, plan, and close a task. Inspect the activity log and exported data before trusting it with real work. Practical LinkLoot angle Backlog fits the current agent-ops problem: coding agents are powerful, but they often need a durable queue, scoped context, and a handoff trail. This is especially useful when you restart sessions often or run several agents against one codebase. The main caveat is maturity. Before adopting it for paid work, verify the release binary, backup behavior, DB location, repo license, agent-skill install paths, and whether your team wants task data committed to the repo or kept outside version control. Source check The GitHub repository shows an MIT-licensed project with a July 7, 2026 public release, CLI/web/MCP surfaces, local SQLite storage, and documentation links. Hacker News lists it as a fresh Show HN launch signal, but with limited discussion at the time checked.
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