The limit was applied in `apply_outputs`, which the executor reaches after the item is off the queue — so a subscriber told to publish every 15s still cost a queue entry, a `cascade_started`, a run record and a walk of everything reachable from it per inbound message. Seven relay nodes behind one inverter ran 192 times a minute to publish six. Two halves, matching the two shapes it takes: `trigger()` now keeps a value whose every port is inside its window and journals nothing at all. The window split came out of `_throttled` as a read-only `_window_split`, so the question is asked the same way in both places and the exact split is still made once, at claim time. A cascade carries the names it actually published, and the wave runs only the nodes something in that set feeds. A node whose triggering inputs were all held back is completed without running, which frees its own consumers to be judged the same way — the case where a node re-published 619 messages a minute off inputs that changed six times. Redeliveries and emissions carry no such set and still walk everything, since one has a half-finished wave to finish and the other is the value already being in state. Skipping a node can make one ready that the scheduling pass has already walked past, so `submit_ready` runs to a fixpoint. That also closes the same latent hole on the replay path, where a done-marker skip could strand a join with no future outstanding to come back for it. Measured with the new `scripts/bench_engine.py`, 500 messages through the house's shape: a limited source went from 500 cascades / 3500 node runs / 5009 events to 1 / 7 / 19, publishing the same 8 values; an unlimited source into limited relays took the node reading them from 500 runs to 1. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BpfSinyCBfjuieikyfMPbf
Fluksio App
Fluksio is a node-based automation software that brings trust and reliability to your flow. It just works and looks good.
The core of Fluksio: a node-based, test-driven automation software built to scale. This
repo holds the FastAPI backend, the flow engine, and the dashboard SPA. It is served on
app.${DOMAIN} (SPA) and api.${DOMAIN} (API)
Layout
backend/ FastAPI + SQLModel + Alembic + SQLite
fluksio/flow/ the flow engine (nodes, pipeline, state backends, controller)
fluksio/cli.py `fluksio serve` / `enroll` / `worker`
worker/ the `fluksio-worker` distribution: the agent and the node runner
frontend/ React 19 + TanStack Router + Tailwind 4 + shadcn/ui
docs/ the public documentation site (zensical), served on docs.${DOMAIN}
docker/ compose.yml → compose.dev.yml → compose.local.yml (+ compose.traefik.yml)
scripts/ generate-client.sh, test.sh
Install without Docker
Get started quickly by running
pip install fluksio
fluksio serve
Then, head over to fluksio.com, sign up and add a new installation. Using the code provided, run
fluksio enroll <code>
and you're ready to rock.
Fluksio is distributed at it's heart. A machine that should only run nodes for an engine elsewhere installs less:
pip install fluksio-worker
fluksio-worker --url wss://api.example.com/api/v1/workers/attach --token "$TOKEN" --labels gpu
Getting started
Normally driven from the workspace root (make init once, then make dev). Standalone:
make install # uv sync + bun install
make dev-utils # proxy and mailcatcher only
make dev-backend # FastAPI on :8000, hot reload
make dev-frontend # Vite on :5173
make test # pytest + Playwright (the e2e half needs the stack up)
make lint # ruff + mypy + biome
make generate-client # regenerate the frontend SDK from the OpenAPI schema
make help lists every target.
Documentation
The public site lives in docs/ and is built with zensical:
make docs-serve # live preview on :8000
make docs # static build into ./site
It is served at docs.${DOMAIN} by the docs service in docker/compose.yml,
and .gitea/workflows/docs.yml builds it with --strict on every push.
License
Copyright (C) 2026 Melvin Strobl - GNU Affero General Public License v3.0 or later. See LICENSE.