Decodo Python API Docs | dltHub

Decodo is a web‑scraping platform that provides managed scraping endpoints (including YouTube transcript, subtitles, metadata, and video downloader) delivering structured JSON outputs for AI and data pipelines. The REST API base URL is https://scraper-api.decodo.com/v2 and All requests require a Basic Authorization header with an API token..

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 Decodo data in under 10 minutes.

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What data can I load from Decodo?

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

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How do I authenticate with the Decodo API?

Authentication uses an HTTP Basic‑style token in the Authorization header; set the header Authorization: Basic <TOKEN> for every request.

1. Get your credentials

1. Sign up or log in at https://dashboard.decodo.com.
2. Navigate to Dashboard → API / Scrapers or API Playground.
3. Choose a plan (Web API Advanced required for some targets).
4. Copy your API key/token from the dashboard and use it as the Authorization header value (Basic TOKEN VALUE).

2. Add them to .dlt/secrets.toml

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[sources.decodo_web_scraping_api_source]
api_key = "your_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 Decodo 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 decodo_web_scraping_api_pipeline.py

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

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Pipeline decodo_web_scraping_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset decodo_web_scraping_api_data
The duckdb destination used duckdb:/decodo_web_scraping_api.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 decodo_web_scraping_api_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 youtube_transcript and youtube_metadata from the Decodo 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 decodo_web_scraping_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://scraper-api.decodo.com/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "youtube_transcript", "endpoint": {"path": "scrape"}},
            {"name": "youtube_metadata", "endpoint": {"path": "scrape"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="decodo_web_scraping_api_pipeline",
        destination="duckdb",
        dataset_name="decodo_web_scraping_api_data",
    )
    load_info = pipeline.run(decodo_web_scraping_api_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("decodo_web_scraping_api_pipeline").dataset()
sessions_df = data.youtube_transcript.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM decodo_web_scraping_api_data.youtube_transcript LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("decodo_web_scraping_api_pipeline").dataset()
data.youtube_transcript.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 Decodo 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 401/403 errors, verify your Authorization header uses the dashboard token exactly with the header name Authorization and value format Basic TOKEN VALUE. Ensure your account and plan allow the requested target (some targets require Web API Advanced).

Missing transcript / 404

For transcript targets, a 404 occurs if the requested language_code or transcript_origin doesn't exist for the video. Omit or correct language_code or try alternate transcript_origin values.

Rate limits and plan restrictions

Requests may be limited by plan; some templates (e.g., youtube_transcript) require Web API Advanced or specific subscription. Check dashboard for quotas and retry after backoff if receiving 429.

Asynchronous job handling

For large or batch jobs use asynchronous requests: submit the job (POST /v2/scrape) and poll jobs/{job_id} (GET) for result; use the result field in the job status response to retrieve output.

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