Apify Python API Docs | dltHub

Apify is a web scraping and automation platform that provides REST APIs to run and manage Actors (scrapers), and to store and retrieve results in datasets and key-value stores. The REST API base URL is https://api.apify.com/v2 and all requests generally require a Bearer API token for authentication (query token allowed but less secure).

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Apify data in under 10 minutes.

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What data can I load from Apify?

Here are some of the endpoints you can load from Apify:

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How do I authenticate with the Apify API?

Use your Apify API token in the Authorization header: Authorization: Bearer YOUR_API_TOKEN. Alternatively the token may be passed as ?token=YOUR_API_TOKEN (not recommended).

1. Get your credentials

1. Sign in to Apify Console (https://console.apify.com). 2) Open Settings > Integrations (or Account > Integrations). 3) Create/copy a secret API token; give it a descriptive name and permissions. 4) Use that token in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.apify_platform_source]
api_token = "your_apify_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Apify API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python apify_platform_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline apify_platform_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset apify_platform_data
The duckdb destination used duckdb:/apify_platform.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline apify_platform_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads datasets_items and acts_runs from the Apify API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def apify_platform_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.apify.com/v2",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "datasets_items", "endpoint": {"path": "datasets/:datasetId/items", "data_selector": "data.items"}},
            {"name": "acts_runs", "endpoint": {"path": "acts/:actorId/runs", "data_selector": "data.items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apify_platform_pipeline",
        destination="duckdb",
        dataset_name="apify_platform_data",
    )
    load_info = pipeline.run(apify_platform_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("apify_platform_pipeline").dataset()
sessions_df = data.datasets_items.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM apify_platform_data.datasets_items LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("apify_platform_pipeline").dataset()
data.datasets_items.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Apify data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 or 403 responses, verify you are sending Authorization: Bearer YOUR_API_TOKEN and that the token has required permissions. Passing token as ?token= is supported but less secure; prefer the Authorization header.

Rate limits

Apify enforces per‑resource rate limits (default 60 req/s per resource). Some endpoints have higher limits (200–400 req/s). Rate‑limit headers such as X-RateLimit-Limit may be present. Implement retries with exponential backoff on 429 or other transient errors.

Pagination

List endpoints are paginated. Most use offset and limit and return { "data": { "total": ..., "offset": ..., "limit": ..., "count": ..., "items": [...] }}. Some use key‑based pagination (exclusiveStartKey/nextExclusiveStartKey) with a similar structure and items in data.items. Always read the returned data.limit and count rather than assuming a fixed maximum.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack