---
title: "Consuming messages"
description: "Consuming messages — Python BabelQueue SDK (1.x)."
source: https://babelqueue.com/docs/babelqueue-python/1.x/consuming-messages/
updated: 2026-06-06T00:00:00.000Z
---

# Consuming messages

## With the runtime

Register a handler for a URN with the `@app.handler` decorator, then start the
worker with `app.run()`. Your function receives the decoded `data` dict and the
`meta` block — regardless of which language produced the message. Add a third
`message` parameter to get the full envelope (including `trace_id`).

```python
from babelqueue import BabelQueue

app = BabelQueue("redis://localhost:6379/0", queue="orders")

@app.handler("urn:babel:orders:created")
def on_order_created(data, meta, message):
    trace_id = message["trace_id"]  # cross-service correlation
    print(f"[{trace_id}] order {data['order_id']} for {data['amount']}")
    # ... run scoring / enrichment / ML pipeline

app.run()  # consume forever (Ctrl-C to stop)
```

## With the codec only

Managing the broker yourself? Pull the bytes from your client, then decode and
route on the URN:

```python
from babelqueue import EnvelopeCodec

incoming = EnvelopeCodec.decode(body)   # body pulled from your broker
urn      = incoming["job"]              # "urn:babel:orders:created"
data     = incoming["data"]            # {"order_id": 1042, ...}
trace_id = incoming["trace_id"]        # correlate across services
```

Python is the **AI/ML & data** consumer in a polyglot topology: a Laravel app can
publish `urn:babel:orders:created`, and this Python worker enriches it or feeds it
into a model — reading the same canonical envelope, carrying the same `trace_id`
end to end.

That's the whole loop: any producer, any consumer, one schema.