Currency API Python API Docs | dltHub

CurrencyAPI is a REST API that provides real-time and historical foreign exchange rates, currency metadata, and conversion endpoints. The REST API base URL is https://api.currencyapi.com/v3 and all requests require an API key provided as an HTTP header or query parameter.

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 Currency API data in under 10 minutes.

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What data can I load from Currency API?

Here are some of the endpoints you can load from Currency API:

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How do I authenticate with the Currency API API?

Authenticate by including your API key either in the HTTP header named 'apikey' (recommended) or as a query parameter 'apikey' (or 'key' in older docs). Example header: apikey: YOUR-API-KEY.

1. Get your credentials

1. Register or sign in at https://app.currencyapi.com/register or https://app.currencyapi.com/dashboard.
2. Create or view your API key in the Dashboard.
3. Copy the API key and store it securely (environment variable or secrets store).
4. Use it in requests via header 'apikey: ' or '?apikey='.

2. Add them to .dlt/secrets.toml

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[sources.currency_api_source]
apikey = "your_api_key_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 Currency API 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 currency_api_pipeline.py

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

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Pipeline currency_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset currency_api_data
The duckdb destination used duckdb:/currency_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 currency_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 latest and convert from the Currency API 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 currency_api_source(apikey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.currencyapi.com/v3",
            "auth": {
                "type": "api_key",
                "apikey": apikey,
            },
        },
        "resources": [
            {"name": "latest", "endpoint": {"path": "v3/latest", "data_selector": "data"}},
            {"name": "convert", "endpoint": {"path": "v3/convert", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="currency_api_pipeline",
        destination="duckdb",
        dataset_name="currency_api_data",
    )
    load_info = pipeline.run(currency_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("currency_api_pipeline").dataset()
sessions_df = data.latest.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM currency_api_data.latest LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("currency_api_pipeline").dataset()
data.latest.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 Currency API 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 requests return 4xx auth errors: ensure you include your API key either as header apikey: YOUR-API-KEY or as query parameter ?apikey=YOUR-API-KEY. Check the Dashboard to confirm the key is active; regenerated keys will invalidate the old one.

Rate limits and quotas

Requests are subject to plan‑dependent rate limits and update intervals (free plans may return daily updates, paid plans may update more frequently). Check /v3/status or your Dashboard subscription page for quota and rate‑limit details and monitor usage to avoid throttling.

Response format and data selectors

Endpoints return JSON with top‑level keys meta and data. For latest, historical, convert, the data key is an object mapping three‑letter currency codes to objects with fields such as code and value. For /v3/currencies, data maps currency codes to metadata objects (symbol, name, decimal_digits, countries, type). Use JSON selector data to extract the records.

Pagination / large responses

There is no cursor‑based pagination for the typical latest/historical endpoints; results are returned in full under the data object. Use the currencies query parameter to limit returned currencies.

Common error responses

• 400 Bad Request: missing required parameters (e.g., date for /historical) or malformed query.
• 401/403 Unauthorized: missing or invalid API key.
• 429 Too Many Requests: rate limit exceeded; back off and retry according to your plan.
• 5xx Server Errors: transient server issues; retry with exponential backoff.

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