PAPI vs Taskmaster

Taskmaster earned its 25k+ GitHub stars. It was the tool that first gave AI coding agents real structure: parse a PRD, break it into tasks and subtasks, analyse complexity, keep the agent pointed at the next thing. It is MIT licensed, works offline with your own API key, runs in Cursor, VS Code, Windsurf, Claude Code, or any terminal, and its tasks live as plain files in your repo. 1.5M+ npm downloads say it works. If local task orchestration is the whole job, Taskmaster is a sensible choice, and none of what follows is a knock on it.

One naming note, since search still says Taskmaster: the team behind it now builds Hamster (tryhamster.com), a commercial product for teams, and Taskmaster continues inside it as the free open-source core.

Facts checked against tryhamster.com in July 2026. Evaluating the team product instead? PAPI vs Hamster

Two lanes: which is built for you

These tools solve different halves of the same problem. Taskmaster orders the work. PAPI keeps the project pointed somewhere.

Local task orchestration

Your tasks are files in the repo. The agent works through them one by one, offline if you like, free forever, and everything stays on your machine.

Pick Taskmaster if ordered tasks for one agent in one repo is the whole job.

A project that holds its course

Cycles that plan, build, review, and release. Decisions on the record with the why. A review gate before done counts as done. A hosted dashboard you can read from anywhere.

Pick PAPI if the problem is drift: fast sessions that each pull the project a little off course.

Side by side

TaskmasterPAPI
Where state lives✓ Files in your repo (.taskmaster). Offline, local, yours.A hosted project record, the same from every tool and every session.
Planning depthPRD to tasks to subtasks, with complexity analysis.Cycles: plan, build, review, release. Each build gets a scoped handoff with acceptance checks.
Review loopA task is done when the agent marks it done.A review gate: finished work is checked against the handoff before it counts.
DecisionsTasks carry details and dependencies; the why lives in your head.Every call is kept, with the why, and checked against new work.
Team storySingle repo, single player. Teams are the Hamster lane.Shared projects: teammates read the same plan and dashboard.
Works offline✓ Yes. CLI-first, bring your own API key.No. PAPI is a hosted MCP server.
Price and license✓ Free, MIT, open source (as of July 2026).Free on three projects. Pro at €20 a month flat, no usage bills.

Bring your Taskmaster backlog with you

Trying PAPI does not mean retyping your backlog. Your AI session does the move in one ask.

  1. Ask for the import. In any AI tool with PAPI connected, say "import my Taskmaster tasks". The papi-import-taskmaster skill reads your local .taskmaster tasks.json, current tagged layout or the older flat shape, and maps each task to PAPI. Nothing to export, nothing to upload.
  2. Every open task comes across. One top-level Taskmaster task becomes one PAPI Backlog task: title kept verbatim, description, details, and test strategy folded into the notes, subtasks as a checklist, priorities normalised. Done and cancelled tasks are skipped, so finished work stays finished.
  3. Check the count. When it finishes you get one line: how many tasks came in, how many were skipped. The import is one-way. Your .taskmaster files are read, never changed, and Taskmaster keeps working if you want both.

Keep the structure. Add the steering.

Connect PAPI to the tool you already build in and run your first cycle on the backlog you just brought over.

Connect PAPI

Free on three projects · no card · 14-day refund on Pro