Scrapely API client for Python

The official Python client for the Scrapely REST API.

scrapely-client-python lets you talk to the Scrapely platform from Python — run Actors, manage storages (datasets, key-value stores, request queues), schedule tasks, configure webhooks, and use everything else exposed by the Scrapely API. It ships both synchronous and asynchronous clients, fully typed responses, automatic retries with exponential backoff, tiered timeouts, pagination helpers, streaming, and a pluggable HTTP layer.

If you want to build Actors in Python rather than consume the API, use the Scrapely SDK for Python instead — it bundles this client and adds Actor-side primitives.

Table of contents

• Installation
• Quick start
• Features
• Usage examples
• Documentation
• Related projects
• Support and community
• Contributing
• License

Installation

scrapely-client-python requires Python 3.11 or higher and is published on PyPI.

• From PyPI, it can be installed for example with pip:

  pip install scrapely-client-python
  

  or with uv:

  uv add scrapely-client-python
  

  or any other Python package manager that consumes PyPI.

  The client compresses request bodies with gzip by default (no extra dependencies required). To opt in to brotli (better compression ratio), install the optional extra and pass compression='brotli':

  pip install "scrapely-client-python[brotli]"
  # or
  uv add "scrapely-client-python[brotli]"
  

Quick start

You'll need a Scrapely API token — find yours in the integrations section of the Scrapely Console. Pass it to the client and you're ready to go.

Synchronous client

from scrapely_client import ScrapelyClient

client = ScrapelyClient('MY-SCRAPELY-TOKEN')

# Start an Actor and wait for it to finish.
run = client.actor('my-user/hello-world').call(
    run_input={'message': 'Hello, Scrapely!'},
)
if run is None:
    raise RuntimeError('Actor run was not found.')

# Iterate items from the run's default dataset.
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

Asynchronous client

import asyncio

from scrapely_client import ScrapelyClientAsync


async def main() -> None:
    client = ScrapelyClientAsync('MY-SCRAPELY-TOKEN')

    run = await client.actor('my-user/hello-world').call(
        run_input={'message': 'Hello, Scrapely!'},
    )
    if run is None:
        raise RuntimeError('Actor run was not found.')

    # Iterate items from the run's default dataset.
    async for item in client.dataset(run.default_dataset_id).iterate_items():
        print(item)


asyncio.run(main())

Keep your token secret. It authorizes requests on your behalf and can incur usage costs. Never commit it to source control or expose it to client-side code.

Features

• Synchronous and asynchronous clients — pick ScrapelyClient or ScrapelyClientAsync to match your codebase; both expose the same API (Asyncio support).
• Fully typed responses — every method returns a Pydantic model generated from the platform's OpenAPI spec, with IDE autocomplete and runtime validation (Typed models).
• Automatic retries — exponential backoff for network errors, HTTP 429, and 5xx responses, configurable per client (Retries).
• Tiered timeouts — short / medium / long tiers picked per endpoint, overridable per call (Timeouts).
• Pagination and streaming — iterate datasets, key-value store keys, or live logs without manual paging or buffering (Pagination, Streaming).
• Convenience methods — call(), wait_for_finish(), nested resource access, and other shortcuts that hide platform quirks (Convenience methods).
• Pluggable HTTP layer — swap the default Impit-based HTTP client for httpx, requests, aiohttp, or any custom implementation (Custom HTTP clients).
• Structured errors — every API error surfaces as a ScrapelyApiError with HTTP-specific subclasses for precise handling (Error handling).
• Debug logging — opt-in structured logging on the scrapely_client logger captures request URLs, status codes, retry attempts, and more (Logging).

Usage examples

The client mirrors the platform's resource model. Each entry point returns either a single-resource client for an individual item or a collection client for listing and creating items (Single and collection clients).

List Actors and create one

actors = client.actors()
print(actors.list(limit=10).items)

new_actor = actors.create(name='my-actor')

Stream live logs while a run is in progress

run = client.actor('my-user/web-scraper').start(run_input={...})

with client.run(run.id).log().stream() as log_stream:
    for chunk in log_stream.iter_bytes():
        print(chunk.decode(), end='')

Read and write key-value store records

store = client.key_value_store('STORE-ID')
store.set_record('greeting', {'message': 'Hello!'})
record = store.get_record('greeting')

Iterate dataset items with automatic pagination

for item in client.dataset('DATASET-ID').iterate_items(fields=['title', 'url']):
    process(item)

Tune retries and timeouts

from datetime import timedelta

from scrapely_client import ScrapelyClient

client = ScrapelyClient(
    token='MY-SCRAPELY-TOKEN',
    max_retries=8,
    min_delay_between_retries=timedelta(milliseconds=500),
    timeout_long=timedelta(minutes=10),
)

For end-to-end recipes — passing input, managing tasks for reusable input, retrieving and merging Actor data, integrating with Pandas, plugging in a custom HTTP client — see the Guides.

Documentation

The full documentation lives at https://api.scrape.ly.

Related projects

• Scrapely SDK for Python — toolkit for building Actors in Python (this client is bundled with it).
• Crawlee for Python — high-level web scraping and browser automation framework that powers many Actors.
• Scrapely API client for JavaScript / TypeScript — equivalent Scrapely API client for Node.js.
• Scrapely SDK for JavaScript / TypeScript — equivalent Scrapely SDK for Node.js.
• Crawlee for JavaScript / TypeScript — the Node.js implementation of the Crawlee framework.
• Scrapely CLI — command-line tool for interacting with the Scrapely platform: managing Actors, runs, storages, local development, and deployment.

Support and community

• GitHub issues — report a bug or request a feature in the repository's issue tracker.

Contributing

Bug reports, fixes, and improvements are welcome! See CONTRIBUTING.md for the development setup, coding standards, testing, and the release process. The repo uses uv for project management and Poe the Poet as a task runner; the typical loop is:

uv run poe install-dev   # install dev deps and git hooks
uv run poe check-code    # lint, type-check, unit tests, docstring check

License

Released under the Apache License 2.0.