celeryflow 0.1.0

Describe Celery workflows in YAML, JSON, or a Python dict, with optional per-step conditions.

CeleryFlow

CeleryFlow lets you describe a multi-step Celery workflow as a config file (YAML, JSON, or a plain Python dict) instead of wiring chain(), group(), and chord() by hand. Each step can have a condition, so you can skip parts of the flow based on the payload at runtime.

I used a version of this internally for a few years to run order and billing pipelines. This is the cleaned-up open-source version with the business-specific stuff removed.

What you get

• Define flows in YAML, JSON, or a Python dict.
• Reuse named sub-flows inside larger flows.
• Skip steps with conditions like {"sn": {"$eq": "1234"}} — $eq, $ne, $in, $gte, etc. You can add your own.
• Optional structured logging on start / success / failure / retry.
• The flows are still regular Celery tasks. Retries, routing, and monitoring keep working the way you expect.

Install

pip install celeryflow              # core
pip install "celeryflow[yaml]"      # if you want YAML configs
pip install "celeryflow[redis]"     # if you want Redis as the broker

Python 3.11 or newer. During development I use uv, but you don't need it to install the package.

Quickstart

from celeryflow import CeleryFlow, EventTask, FlowBuilder

app = CeleryFlow("my_worker")
app.conf.update(broker_url="redis://localhost:6379/0",
                result_backend="redis://localhost:6379/0")

# 1. Write some tasks.
@app.task(name="order.validate", base=EventTask)
def validate(payload):
    assert "sn" in payload
    return payload

@app.task(name="order.charge", base=EventTask)
def charge(payload):
    payload["charged"] = True
    return payload

@app.task(name="order.notify", base=EventTask)
def notify(payload):
    payload["notified"] = True
    return payload

2. Describe the flow. CeleryFlow accepts the definition in three formats — a Python dict, a YAML file, or a JSON file. Pick whichever fits your project.

Option A — Python dict, inline:

FlowBuilder.from_dict(app, {
    "work-flows": [
        {"name": "checkout", "tasks": ["order.validate", "order.charge"]},
    ],
    "main-flows": [
        {
            "name": "PlaceOrder",
            "flows": [
                {"flow": "checkout"},
                {"task": "order.notify"},
            ],
        },
    ],
})

Option B — YAML file:

# flows.yaml
work-flows:
  - name: checkout
    tasks:
      - order.validate
      - order.charge

main-flows:
  - name: PlaceOrder
    flows:
      - flow: checkout
      - task: order.notify

FlowBuilder.from_yaml(app, "flows.yaml")
# Globs work too — multiple files get merged:
FlowBuilder.from_yaml(app, "config/flows/*.yaml")

Option C — JSON file:

{
  "work-flows": [
    {"name": "checkout", "tasks": ["order.validate", "order.charge"]}
  ],
  "main-flows": [
    {
      "name": "PlaceOrder",
      "flows": [
        {"flow": "checkout"},
        {"task": "order.notify"}
      ]
    }
  ]
}

FlowBuilder.from_json(app, "flows.json")

All three produce the same PlaceOrder task. Then run it:

# 3. Run it.
result = app.execute_flow("PlaceOrder", {"sn": "1234"})
print(result.get())
# -> {"sn": "1234", "charged": True, "notified": True}

Conditional steps

FlowBuilder.from_dict(app, {
    "main-flows": [
        {
            "name": "RefundFlow",
            "flows": [
                {"task": "order.validate"},
                # Only charge when it's a new subscription worth >= 100.
                {"task": "order.charge",
                 "condition": {"buy_action": {"$eq": "NEW_SUBSCRIPTION"},
                               "amount":     {"$gte": 100}}},
            ],
        },
    ],
})

When a condition doesn't match, apply_async raises celeryflow.exceptions.ConditionFailed. Catch it in a link_error or custom callback if you'd rather skip the step quietly instead of failing the chain.

Adding your own operator

from celeryflow.conditions import register_operator

register_operator("$startswith",
                  lambda actual, expected: isinstance(actual, str)
                                            and actual.startswith(expected))

Logging

import logging
from celeryflow import EventTask

EventTask.bind_logger(logging.getLogger("orders"))

Each task call will log Start, End, Retry, or Failed. The log records carry task_id, process_id, parent_id, task_name, and a correlation_id if the payload has one.

Development

git clone https://github.com/ChenYuTingJerry/CeleryFlow.git
cd CeleryFlow
uv sync --all-extras

# Unit tests (no broker needed):
uv run pytest -m "not integration"

# Integration tests (need Redis):
docker compose up -d
uv run pytest -m integration
docker compose down

# Lint:
uv run ruff check src tests

Project layout

celeryflow/
├── src/celeryflow/
│   ├── __init__.py        public API
│   ├── app.py             CeleryFlow Celery subclass
│   ├── builder.py         FlowBuilder — parses and registers flows
│   ├── tasks.py           FlowTask / EventTask base classes
│   ├── conditions.py      condition operators
│   ├── encoding.py        JSON encoder (Decimal / Enum / datetime)
│   ├── utils.py           StringConvert / DictConvert
│   └── exceptions.py
├── tests/
├── docker-compose.yml     Redis for integration tests
└── pyproject.toml

If you already use Celery

CeleryFlow doesn't change anything you already have. Your @app.task definitions, broker, workers, and monitoring all keep working. The three things CeleryFlow adds are:

• CeleryFlow(...) — a Celery subclass. Use it instead of Celery. Takes a worker_name and otherwise behaves the same.
• EventTask — a Task subclass. Pass base=EventTask to tasks you want in a flow. (You can also use it on every task if you just want the logging.)
• FlowBuilder.from_* — registers an entry task per flow. When that entry task runs, it splices the rest of the flow into the chain.

So a minimal switch looks like:

# before
from celery import Celery
app = Celery("my_worker", broker="redis://...")

@app.task
def step_a(payload): ...

@app.task
def step_b(payload): ...

# after
from celeryflow import CeleryFlow, EventTask, FlowBuilder
app = CeleryFlow("my_worker", broker="redis://...")

@app.task(base=EventTask)
def step_a(payload): ...

@app.task(base=EventTask)
def step_b(payload): ...

FlowBuilder.from_dict(app, {
    "main-flows": [
        {"name": "MyFlow",
         "flows": [{"task": "step_a"}, {"task": "step_b"}]},
    ],
})

app.execute_flow("MyFlow", payload)

You can adopt it bit by bit. Flows live alongside any chain() / group() / chord() you already build by hand.

License

MIT. See LICENSE.