Taskuary
Your inbox, staffed by AI agents.
Taskuary brings your mail, chats, issues, and scheduled reports into one local conversation: a live work pipe on the left and chat on the right. AI triage decides what is real work, your coding CLI works the tasks in your own repos, and replies wait for your approval. Nothing sends or ships without you.
Taskuary is early—currently v0.3.3.0—and moving fast. The funnel, review queue, agent sessions, and reports pipeline are in daily use; breaking changes are still possible before 1.0.
The real app over invented work — the unified pipe and chat, triage, a coding session mid-run, and reports with charts. Nothing to install and nothing connects to anything.
One timeline, one chat
Outlook, Gmail, Teams, Slack, Telegram, WhatsApp, GitHub, Jira, alerts, and reports all arrive in the pipe beside the same conversation. Unread is the prioritized walkthrough; All is the recent timeline. Click any row—or name a sender, subject, or task in chat—to pull it into the conversation with its context, triage reasoning, draft, and available actions.
Everything shown is generated by the built-in demo. Dana Whitfield, Northwind Facilities, the people, messages, repositories, tasks, and reports are all fictional.
The same conversation also speaks up when something falls between tasks: a reply went unanswered, a meeting needs context, a task went quiet, or a pattern appears across the week's work. Each observation carries its evidence and can be acted on, dismissed, snoozed, or taught away with Not this. These observations now appear in this same chat instead of as separate Assistant posts on the Timeline.
These checks can watch the systems you run on, not only incoming messages. Point one at a Sage Intacct query, a database, a file, a REST or MCP tool, a cloud log group, or an agent skill—no saved report has to stand behind it—and Taskuary reads it silently on schedule. Describe what a check should watch and AI can write the source cards using only connected systems, after reading their real schema and asking rather than guessing a filter. The same help sits on every source card in the report builder. Voice, schedule, model, and thresholds remain yours to change; a check stays silent when there is nothing useful to add to the chat.
Coding work can go straight to Claude Code, Codex, Gemini, Cursor, Copilot, or any CLI that accepts a prompt on stdin. You watch the live terminal, answer questions, review the diff, and approve what happens next. Research, marketing, and other general work belong in a conversational workspace built with assistant-ui and your existing Taskuary AI connections. Chat and terminal are two views of the same session: model, history, queued instructions, image attachments, and the side-by-side browser stay put when you switch. The same workspace also appears on the Wall.
Install
Windows app
Download the latest single-file Taskuary.exe and open it. No Python or installer is required.
Python
Python 3.10 or newer works on Windows, macOS, and Linux:
pip install taskuary
taskuary
Taskuary opens at http://127.0.0.1:7787. For a native desktop
window instead, install pip install "taskuary[desktop]" and run taskuary-desktop.
Docker
git clone https://github.com/ldbumble/taskuary
cd taskuary
docker compose up
Then open http://127.0.0.1:7787. Docker runs the web app; coding CLIs and the optional WhatsApp bridge remain on the host.
On first run, connect an AI provider or local Ollama model, add at least one inbound channel, then choose the coding CLI that should receive tasks. The setup wizards test each connection before it goes live.
Try it without installing anything
taskuary --demo # or: docker compose --profile demo up
A full Taskuary over invented work — a morning's mail and chats already triaged with the
reasons attached, drafts waiting in Review, agents on the board with their transcripts
playing, live task handoffs on the Board, and the durable, high-signal company Hub.
Every Hub post names its human or agent author and originating task; Board handoffs are short-lived
checkout coordination that rolls up nightly. The AI answers from a script and every door
to the outside world is shut at the API layer (taskuary/demo.py): nothing sends, no
connection can be made or edited, no tool runs, and no CLI starts. Nobody's real data is in
it — Dana Whitfield and Northwind Facilities are invented.
Installs
Redrawn every morning by downloads.yml from PyPI's own
numbers, with mirror traffic excluded — a full PyPI mirror pulls every release, so counting it
would let a handful of real users read as hundreds. What it does not claim is that CI
installs have been removed: the field that would separate pip install in a build runner from
one on somebody's laptop is in PyPI's BigQuery dataset, not in the public API. The daily series
is docs/downloads.csv.
Documentation
- Getting started—installation, first-run setup, Docker, and data
- Product guide—the workflow, learning loop, agents, and operator documents
- Integrations—channels, AI providers, work systems, and report sources
- Reports and proactive checks—the report pipeline, AI-written source cards, and what Taskuary watches
- Status and roadmap—what works today and what is next
- Contributing—development setup and contribution guide
Taskuary is free and open source under the MIT License. Issues and pull requests are welcome; security reports belong in SECURITY.md.
1 maintainer
Release files for taskuary 0.3.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| taskuary-0.3.3.0.tar.gz | 1.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| taskuary-0.3.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.4 MB
Release files / taskuary-0.3.3.0.tar.gz
| Download URL | taskuary-0.3.3.0.tar.gz |
|---|---|
| Size | 1.9 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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PyPI Publish Attestation
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Signed by GitHub Actions, verified by PyPI on Sep 4, 2026.
Transparency logRelease files / taskuary-0.3.3.0-py3-none-any.whl
| Download URL | taskuary-0.3.3.0-py3-none-any.whl |
|---|---|
| Size | 1.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 4, 2026.
Transparency log
