Taskuary
Your inbox, staffed by AI agents
Taskuary turns incoming messages into organized work. It sorts what matters, hands tasks to your agents, and brings decisions back to you. Nothing sends or ships without your approval.
Taskuary is early—currently v0.3.4.11—so breaking changes are still possible before 1.0.
The real app with invented data. Nothing connects, sends, or runs.
What Taskuary can do
One request, from arrival to your approval. Follow Ruth's request for the latest vendor spend numbers through the real app, using fictional demo data.
1. Connect every system. Keep control.
Mail, chats, issue trackers, alerts, and reports land on one Timeline. See what arrived, what became a task, and what needs you without opening every system in turn.
2. Turn incoming work into tasks
Ruth asks for the August total, the change from July, and a breakdown by category. Taskuary creates a task with the original request and assigns it to an agent.
3. Watch the agent work
Open the task to follow the analysis. Here, the general agent prepares the numbers, checks that the categories add up, and drafts a reply.
4. Approve the outcome
The reply waits in Review, beside the request that started it. Read it, edit it, and choose Approve & send when it is ready.
5. Let the Assistant walk you through it
Choose Walk me through my tasks. The Assistant brings one item into the conversation, explains what needs your attention, and puts the next action within reach.
6. Start your day with the daily digest
Open your morning brief to see what people need, what is in flight, and what is on your calendar. The day's meetings sit above the digest, so Ruth's request has a clear deadline: the operations review.
Key features
Use your coding CLI
Connect Claude Code, Codex, Gemini, Cursor, Copilot, Muse Code, or another CLI. Set up the connection once, then give your agents profiles with their own instructions. Follow their sessions, answer questions, and review the result from Taskuary.
A shared Hub for what agents learn
Keep discoveries, decisions, and useful warnings by topic. Agents can find what earlier work uncovered, discuss it, and correct it instead of starting from scratch.
Agents leave notes for each other
The Board's Live handoffs show what agents are working on, what is blocked, and what is ready. An agent leaves a note; the next one reads it before picking up the work.
What leaves your machine
Taskuary runs locally. The one thing that goes out is the prompt—whatever your AI provider or coding CLI needs to do the work you asked for.
Credentials are taken out of that prompt first. If a colleague mails an API key, a connection
string, or a private key, it is replaced with a labelled placeholder ([redacted:aws-key]) at
each of the three doors a prompt can leave by: the hosted models, a headless CLI run, and the
first prompt of an agent pane. Your mail itself is never altered—the scrub is on the way out,
not on the way in—so a vendor's one-time code stays readable where it arrived. Nothing Taskuary
sends carries a placeholder either: a reply still holding one is refused, not delivered.
The rules are deterministic rather than a model's judgement, because by the time a model could judge, the credential would already be in a prompt. So they catch credentials with a recognizable shape—provider keys, tokens, credentialed URLs, connection strings, private keys—and they will not catch a sentence like "the wifi password is bluefish17". Report anything you find through SECURITY.md.
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
The demo is the real interface with fictional work and scripted replies. It cannot connect to outside systems, send messages, run tools, or start agents. Its changes reset when you reload.
Installs
Updated daily from PyPI with mirror traffic excluded. The raw 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 0.3.4.11
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-0.3.4.11.tar.gz | 2.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| taskuary-0.3.4.11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.9 MB
Release files / taskuary-0.3.4.11.tar.gz
| Download URL | taskuary-0.3.4.11.tar.gz |
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| Size | 2.8 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Yes |
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / taskuary-0.3.4.11-py3-none-any.whl
| Download URL | taskuary-0.3.4.11-py3-none-any.whl |
|---|---|
| Size | 2.1 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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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 15, 2026.
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