vercel-workers
Python SDK for Vercel Workers
Decision gist · record as of 2026-08-14
Yes, if you are building on Vercel and need a lightweight queue integration. The low install friction, recent maintenance, MIT license, and native support for popular task frameworks make it a practical choice. No, if you are not using Vercel or require a self-hosted queue solution—this SDK is tightly coupled to Vercel's infrastructure.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later.
- Running outside Vercel requires VERCEL_QUEUE_TOKEN environment variable.
- Low install friction with a pure Python wheel distribution.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-06-20 (55 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,332,680 downloads/mo, #4,042 on PyPI
Alternatives
Verify before relying
pip install vercel-workers
from vercel_workers import subscribe
@subscribe(topic="default")
def handle_message(body: dict):
print(body)- Whether the package is actively maintained by Vercel or community-driven (repo metadata unavailable).
- Performance characteristics and throughput limits for queue operations.
- Whether optional adapter extras (celery, dramatiq, django) are automatically installed or must be specified.
What it is and what it does
vercel-workers is a Python SDK that bridges your application to Vercel's queue and worker service infrastructure. It provides primitives for publishing messages to Vercel Queues and subscribing to them via decorators, plus drop-in adapters for existing task systems like Celery, Dramatiq, and Django. The package depends on httpx, anyio, pydantic, python-dotenv, and the vercel client library.
Typical usage involves defining a producer (e.g., a FastAPI endpoint) that publishes messages, and a worker service that consumes them. The SDK handles authentication via VERCEL_QUEUE_TOKEN and can be deployed as a Vercel Worker Service alongside your main application. It supports both direct message handling and integration with established task queue frameworks.
Use it for
- Publish background tasks from a FastAPI or web service to Vercel Queues for asynchronous processing.
- Migrate existing Celery or Dramatiq task systems to Vercel infrastructure with minimal code changes.
- Implement Django task callbacks routed through Vercel's queue infrastructure.
- Build event-driven microservices that consume messages from Vercel Queues in dedicated worker services.
- Decouple request handling from long-running operations using the @subscribe decorator pattern.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building on Vercel and need a lightweight queue integration.
The low install friction, recent maintenance, MIT license, and native support for popular task frameworks make it a practical choice. No, if you are not using Vercel or require a self-hosted queue solution—this SDK is tightly coupled to Vercel's infrastructure.
Install
vercel-workers on PyPI
Before you install
Low install friction with a pure Python wheel distribution. Marked as active with a recent release cycle (55 days since last update). Requires Python 3.12 or later.
Requires Python 3.12 or later. Running outside Vercel requires VERCEL_QUEUE_TOKEN environment variable.
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install vercel-workers
from vercel_workers import subscribe
@subscribe(topic="default")
def handle_message(body: dict):
print(body)
Verify before relying
- Whether the package is actively maintained by Vercel or community-driven (repo metadata unavailable).
- Performance characteristics and throughput limits for queue operations.
- Whether optional adapter extras (celery, dramatiq, django) are automatically installed or must be specified.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packageshttpxanyiopydanticpython-dotenvvercel |
| Maintenance | Actively maintained 55 days since the last release |
| First released | |
| Downloads | 1,332,680 / month, #4,042 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: vercel_workers-0.0.25-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “vercel queues python sdk”
- vercel-workersPython SDK for publishing and consuming messages on Vercel Queues,…
- vercelPython SDK for Vercel that provides modules for interacting with…
- upstash-redisHTTP-based Redis client for Python that connects to Upstash Redis via…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also dramatiq · taskiq · celery · django-dramatiq · dvc-task · vercel_blob · judoscale · vercel · kafka · apache-airflow-providers-celery