vercel-sdk 0.0.8

Python SDK for Vercel

vercel-sdk

Python SDK for Vercel.

Installation

pip install vercel-sdk

Requirements

• Python 3.10+

Usage

Headers and request context

from vercel.headers import geolocation, ip_address

# In a framework handler, pass request.headers
city_info = geolocation(request)
client_ip = ip_address(request.headers)

Runtime Cache

The SDK talks to Vercel’s Runtime Cache when the following env vars are present; otherwise it falls back to an in-memory cache.

• RUNTIME_CACHE_ENDPOINT: base URL of the runtime cache API (e.g. https://cache.vercel.com/...)
• RUNTIME_CACHE_HEADERS: JSON object of headers to send (e.g. '{"authorization": "Bearer "}')
• Optional: SUSPENSE_CACHE_DEBUG=true to log fallback behavior

from vercel.cache import get_cache

def main():
    cache = get_cache(namespace="demo")

    cache.delete("greeting")
    cache.set("greeting", {"hello": "world"}, {"ttl": 60, "tags": ["demo"]})
    value = cache.get("greeting")  # dict or None
    cache.expire_tag("demo")        # invalidate by tag

main()

OIDC (Vercel project tokens)

If the VERCEL_OIDC_TOKEN header is not present, the SDK will try to refresh a token using the local Vercel CLI session and your project configuration.

from vercel.oidc import get_vercel_oidc_token, decode_oidc_payload

def main():
    token = get_vercel_oidc_token()
    payload = decode_oidc_payload(token)
    sub = payload.get("sub")

main()

Notes:

• Requires a valid Vercel CLI login on the machine running the code for refresh.
• Project info is resolved from .vercel/project.json.

Blob Storage

The SDK includes a Blob client for uploading, listing, downloading, copying, and deleting objects in Vercel Blob.

• Required: set BLOB_READ_WRITE_TOKEN or pass token when constructing a client

Async usage:

import asyncio
from vercel.blob import AsyncBlobClient

async def main():
    client = AsyncBlobClient()  # uses BLOB_READ_WRITE_TOKEN from env

    # Upload bytes
    uploaded = await client.put(
        "examples/assets/hello.txt",
        b"hello from python",
        access="public",
        content_type="text/plain",
    )

    # Inspect metadata, list, download bytes, then delete
    meta = await client.head(uploaded.url)
    listing = await client.list_objects(prefix="examples/assets/")
    content = await client.get(uploaded.url)
    await client.delete([b.url for b in listing.blobs])

asyncio.run(main())

Synchronous usage:

from vercel.blob import BlobClient

client = BlobClient()  # or BlobClient(token="...")

# Create a folder entry, upload a local file, list, then download
client.create_folder("examples/assets", overwrite=True)
uploaded = client.upload_file(
    "./README.md",
    "examples/assets/readme-copy.txt",
    access="public",
    content_type="text/plain",
)
listing = client.list_objects(prefix="examples/assets/")
client.download_file(uploaded.url, "/tmp/readme-copy.txt", overwrite=True)

Examples

See examples/ for runnable scripts:

• runtime_cache_basic.py: set/get with fallback to in-memory
• cache_tags.py: tag-based invalidation
• build_cache_env.py: shows behavior when cache env vars are set
• blob_storage.py: end-to-end blob operations (sync and async)
• fastapi_oidc_plus_cache/: small FastAPI demo wiring headers/oidc

Development

• Lint/typecheck/tests:

uv pip install -e .[dev]
uv run ruff format --check && uv run ruff check . && uv run mypy src && uv run pytest -v

• CI runs lint, typecheck, examples as smoke tests, and builds wheels.
• Publishing: push a tag (vX.Y.Z) that matches project.version to publish via PyPI Trusted Publishing.

License

MIT