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Start free trialCoordination layer · Claude Code, Codex, Cursor, opencode
taskr connects your models into one hive without becoming the thing that runs them. There is no orchestrator, no runtime, no graph to hand your agents to — only a shared record they read before they start and write before they stop. The strong model’s plan is still sitting there when a cheaper one picks it up, and neither had to be running at the same time.
curl -fsSL https://aitaskr.com/install.sh | shmacOS and Linux, no Go toolchain needed. Windows and other options on the releases page.
Why it isn’t an orchestrator
Every multi-agent framework asks for the same thing first: hand us your agents. Declare the graph, run them inside our loop, rewrite the tool calls our way. That buys coordination at the price of owning the execution — and the moment a run dies, so does everything it knew. taskr takes the opposite trade. It owns the record and nothing else, so there is no middle for your agents to be stuck inside.
taskr context where am I, what was I doingtaskr start open a session, print the resume packettaskr offload file what you find, without derailingtaskr park stop by naming the next actionThat is the whole integration. The CLI is an HTTP client and nothing else
— it never runs git, never opens a database, never launches an
agent. Coordination happens because every agent reads the same record, not
because something is standing over them making it happen.
Proof — sessions, not tickets
A coordination layer needs to know who is working right now, on which model, and why the last one stopped. Jira, Linear and GitHub Issues track something else — a body of work, a feature, an epic — and they are good at it. They just cannot answer the question the hive asks. Same eleven days, both records:
Four fields, none of which moved while the work did. Nothing here is wrong — it is answering a different question.
Five spans, each with edges, two of them on different models. “In progress” is not a state anything sits in here — it is whether a session is open right now.
Proof — planned, built, reviewed
The expensive model earns its price on the spec and the plan, and wastes it on the fourth mechanical edit. The hive makes that split practical: the design is attached to the issue, the work is a group of ordered steps, and every step records which model planned it and which model built it — so the review at the end checks work against a document, not against somebody else’s vanished chat log.
Provided by Artificial Analysis via OpenRouter.
illustration · every ref on this page belongs to the same invented project
No agent in that walk needed the one before it to still be running, or to know it existed. Each read the group, took the next open step, and wrote back what it did. That is the entire coordination protocol.
Proof — the thread outlives the chat
A hive is only as good as what its members leave behind. The moment an agent
notices something is the moment it has the context to describe it — and the
moment it is least able to stop. So it doesn’t stop: taskr offload files the finding with a file:line and a definition of
done without leaving the task, and taskr park ends the session by
naming the next action. Same webhook bug, two models, seventeen hours apart:
Move the 200 below the insert in internal/billing/webhook.go:88 and put a unique index on
stripe_events.event_id. Done when the replay test posts one event twice
and gets one invoice.The second agent never saw the first one’s context, was not the same model, and did not run in the same tool. It saw the record, and the record was enough. Note #0731: the trial-end bug was found here and filed here, and it is still open on its own issue rather than dead in a transcript.
export TASKR_REMOTE=$(git remote get-url origin)
export TASKR_ROOT=$(git rev-parse --show-toplevel)
export TASKR_HEAD=$(git rev-parse HEAD)
taskr context Drop the skill file in .claude/skills/ and your agent runs taskr context before it touches anything. taskr never runs git itself — you tell it, and it writes down that you did. A
coordination layer that invents nothing is one every member can trust cold.
Early access
One plan, billed per person. Every agent you point at it writes under your seat at no charge — however many models you run, and whichever tools they run in. The rate is early-access pricing, and it stays yours for as long as the subscription does.
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Start free trialEvery write is stamped by the credential that made it — user when it’s you, agent when it’s one of the things working beside you — and now with the model it was running.