Apify Client

2.5.0 · active · verified Sat Apr 11

The Apify Client for Python allows users to interact with the Apify platform, managing actors, datasets, key-value stores, and more. It provides both synchronous and asynchronous interfaces for web scraping and automation tasks. The current stable version is 2.5.0, with regular updates.

Warnings

breaking Breaking Change: V1 to V2 API Client Initialization. In `v2.x`, the `ApifyClient` constructor directly accepts `token='YOUR_TOKEN'`, unifying the previous `ApifyClientSync` and `ApifyClientAsync` classes from `v1.x`. Users migrating from `v1.x` must adapt to this single client and direct token parameter.
Fix: Replace `ApifyClientSync(token='...')` or `ApifyClientAsync(token='...')` with `ApifyClient(token='...')`.
breaking Breaking Change: V1 to V2 Resource Access Patterns. Accessing Apify resources (Actors, Datasets, Key-Value Stores, etc.) moved from methods like `client.actors().get_or_create(...)` in `v1.x` to a more direct chainable pattern like `client.actor(...).get_or_create()` in `v2.x`.
Fix: Update resource access calls. For example, `client.actors().get('id')` becomes `client.actor('id').get()`.
gotcha API Token Requirement: Most Apify API operations require an API token for authentication. If the `APIFY_TOKEN` environment variable is not set or the `token` parameter is not explicitly passed to the `ApifyClient` constructor, operations will typically result in authentication errors (e.g., 401 Unauthorized).
Fix: Set the `APIFY_TOKEN` environment variable or pass `token='YOUR_APIFY_TOKEN'` directly to `ApifyClient()`.
gotcha Pagination for Large Datasets: When retrieving a large number of items from datasets or key-value stores, fetching all items at once can lead to memory exhaustion. Always use iterative methods or pagination parameters.
Fix: Use `list_items()` with `limit` and `offset` parameters or, preferably, `iterate_items()` for efficient, paginated access to large datasets.

Install

pip install apify-client Install latest version

Imports

ApifyClient
```
from apify_client import ApifyClient
```

Quickstart

This quickstart demonstrates how to initialize the Apify client, run a 'Hello World' actor, and retrieve its results from the default dataset. It emphasizes secure API token handling via environment variables.

import os
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token.
# It's recommended to store your token in an environment variable named APIFY_TOKEN.
# Alternatively, pass it directly: apify_client = ApifyClient(token='YOUR_APIFY_TOKEN')
apify_client = ApifyClient(token=os.environ.get('APIFY_TOKEN', ''))

# Ensure an API token is provided
if not apify_client.token:
    print("Warning: APIFY_TOKEN not found. Most operations will fail without authentication.")
    # In a real application, you might raise an error or exit here.
    # For this quickstart, we'll continue but expect failures for authenticated calls.

# Run a specific Actor (replace 'apify/hello-world' with your Actor ID)
try:
    actor_run = apify_client.actor('apify/hello-world').call()

    # Fetch results from the Actor's default dataset
    print(f'Actor run data from dataset: {apify_client.dataset(actor_run["defaultDatasetId"]).list_items().items}')

    # Or to fetch items one by one:
    # for item in apify_client.dataset(actor_run["defaultDatasetId"]).iterate_items():
    #     print(item)
except Exception as e:
    print(f"An error occurred while running the Actor or fetching data: {e}")
    print("Please ensure your APIFY_TOKEN is valid and the 'apify/hello-world' actor is accessible.")

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