JokeAPI Python API Docs | dltHub

JokeAPI is a public REST API that provides random jokes across various categories and languages. The REST API base URL is https://v2.jokeapi.dev and No authentication required for standard use; optional token can be provided via Authorization header..

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 JokeAPI data in under 10 minutes.

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What data can I load from JokeAPI?

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

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How do I authenticate with the JokeAPI API?

The API does not require authentication for standard requests; an optional API token can be sent in the Authorization header for higher rate limits or whitelisting.

1. Get your credentials

1. Visit the JokeAPI documentation page at https://sv443.net/jokeapi/v2/.
2. No sign‑up is required for public access; you can start making requests immediately.
3. If you need higher rate limits or want to be whitelisted, request an API token from the maintainer (contact information is provided on the site).
4. When you receive a token, include it in the request header as Authorization: <your‑token>.
5. Store the token in your dlt secrets.toml as instructed below.

2. Add them to .dlt/secrets.toml

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[sources.joke_api_source]
api_key = "your_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 JokeAPI 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 joke_api_pipeline.py

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

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Pipeline joke_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset joke_api_data
The duckdb destination used duckdb:/joke_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 joke_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 joke and info from the JokeAPI 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 joke_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://v2.jokeapi.dev",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "joke", "endpoint": {"path": "joke/Any"}},
            {"name": "info", "endpoint": {"path": "info"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="joke_api_pipeline",
        destination="duckdb",
        dataset_name="joke_api_data",
    )
    load_info = pipeline.run(joke_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("joke_api_pipeline").dataset()
sessions_df = data.joke.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM joke_api_data.joke LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("joke_api_pipeline").dataset()
data.joke.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 JokeAPI 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 errors

If you supply an invalid or revoked API token, the response will contain "error": true with code indicating an authentication issue (e.g., 401). Include the token in the Authorization header exactly as received.

Rate limiting

The API enforces a hard limit of 120 requests per minute for unauthenticated callers. Exceeding this limit returns HTTP 429 Too Many Requests and an error object with code 429. Upgrade to a whitelisted token to increase limits.

Pagination & amount limits

When requesting multiple jokes using the amount parameter, the response includes a top‑level "jokes" array. If amount exceeds the server's maximum, the API may compress the payload (Brotli, Gzip) or return an error. Always check the "error" flag before processing the data.

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