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StarPulse's star map: tasks, workflow runs and agents moving live

StarPulse

Every task, workflow run and agent in flight, on one live page.

CI status PyPI version Python version License: MIT

Live demo · Configure · Sources · Agent CLI · Hub · Adapters · Consumers · Security · Compatibility · Development loop · Contributing

What it is and why

I wanted to see where my agents were in their sessions and which skills they were using. Then I watched a video that mapped agent actions onto state machines, and I wanted to map the whole agent lifecycle and pair it with the Dagu workflows I already run, so scripts and DAGs could take over manual AI commands and cut the step count.

StarPulse is what I built. It shows tasks, workflow runs and agents on one live page. It produces data and leaves what to do with that data to you. It is not tied to one agent harness or one lifecycle: you define the states, and it draws and records them.

Quick start

uvx starpulse serve   # then open http://localhost:8766

The board starts empty.

Working with AI agents

An agent works the board through the starpulse CLI, every verb printing JSON (Agent CLI). uvx starpulse skills install --claude --codex copies the bundled skills into the project. For any other agent, add a section to AGENTS.md or CLAUDE.md:

## Tasks

This project tracks work on a StarPulse board. Run `uvx starpulse help --agent` for every verb, its
arguments and exit codes. Read the board with `uvx starpulse board` and open a task with
`uvx starpulse task create "<title>"`.

Working without AI agents

The same verbs work typed by hand, and New task on the Kanban adds a task from the page. Both write the same board.

Views

Kanban Orbit Workflow runs
Kanban view Orbit view Workflow runs view

Reads tasks from its own Markdown board, Backlog.md or Jira; runs from Dagu, GitHub Actions or systemd; agents from Claude Code and Copilot.

Status

StarPulse is early. This is my first open source project, and I am still learning how to run one. What is rough today:

  • The UI is messy and not fully implemented.
  • The data it captures is not yet acted on in full; the feedback loop from captured signal to changed behavior is incomplete.
  • Performance is good but could be better.

Feedback

Something broke, confused you or is missing? Open an issue. Say what you ran and what you expected. A confusing first run is a bug I want to hear about.

MIT licensed.

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