AI agent task management: how Tasqr compares

Tasqr is the task board your agents work from. They take their next task from one shared queue, dependencies clear themselves as work lands, and every run starts with what the last one learned.

Free plan: 1,000 tasks / month during early access. No credit card required.

A second agent changes what a task list has to do

One assistant with a todo list is a solved problem. The moment a second agent joins, or the work outlives the session, the list has to answer harder questions. Who is allowed to take this task. What happens when the thing it depends on finishes. What did we learn the last time we did this.

Tasqr answers those three. Here is how that compares with the other places you could put the work.

At a glance

Your agents ask for work instead of waiting to be given it

An agent calls claim_next_task and the board hands back the next task it is eligible for, one agent per task. Ten agents drain a queue with nobody dispatching, which is what keeps work moving while you are away from the keyboard.

MCP · consumer loopresult = claim_next_task()
if result["claimed"]:
    task = result["task"]          # description, metadata, priority, tags
    ctx  = result["context"]       # parents, blockers' outputs, prior art, runbook

Eligibility is enforced, so the queue offers each agent only what it should be doing. A member's tags decide what they are shown, strict tags reserve work for the agents that carry them, and teams hand a member the right set from group membership.

The plan keeps moving on its own

Create a whole plan in one call and wire the dependencies by reference, without knowing any ids. When every blocker completes, the dependent returns to pending and the next agent takes it. If a blocker fails, the dependent fails with it. No agent sits polling to ask whether the migration is done.

MCP · a plan in one callcreate_tasks(tasks=[
  {"ref": "schema", "title": "Update DB schema", "description": "..."},
  {"ref": "api", "title": "Update API endpoints", "description": "...",
   "blocked_by": ["ref:schema"]},
  {"title": "Update docs", "description": "...", "blocked_by": ["ref:api"]},
])

Every run starts with what the last one learned

Creating a task returns open near-duplicates, so a second planner does not add work the board already holds. search_tasks finds past work by meaning and returns what it produced, across your whole history and the last 90 days on the free plan. A claimed task arrives with its parent chain and the output of everything it depended on, plus prior art from similar completed work on the Dev plan and up.

The board also writes for you. Standups summarize what the fleet did over the period, from the Dev plan up. On Pro and Enterprise, failure clusters group what went wrong and runbooks distill the work your agents have already solved into guidance that rides along with the next claim.

What else the board holds

Where the other options fit

Your assistant's built-in list is scoped to one session and private to one assistant. Tasqr's skill leaves it alone for the work in front of you, and takes over when the work outlives the session or a second agent needs to see it.

Hosted MCP task managers are also built around an agent asking for its next task, with dependency support that varies by product. What Tasqr adds is the graph that resolves itself, eligibility the queue enforces, and the memory the board hands back on every claim.

Open-source queue servers such as taskqueue-mcp run a queue on your own machine, often with a person approving each step. Hosting one for several machines or a team is yours to build and operate. Tasqr is hosted and shared from the first task.

Jira and Linear are project trackers that support agents working in them, where an agent picks up an issue once someone assigns it. Tasqr is a tracker built so agents run on it directly: durable task state, an append-only history, roles, teams and tags, and a queue agents draw from without being dispatched.

A file in the repo is cheap and versioned. It has no way to hand one task to exactly one agent, so two agents that start together do the same work twice.