Open Exchange Rates Python API Docs | dltHub

Open Exchange Rates is a JSON REST API providing live and historical foreign exchange (FX) rates for 200+ currencies. The REST API base URL is https://openexchangerates.org/api and All production endpoints require an App ID (API key) passed as the app_id query parameter (currencies.json optional)..

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 Open Exchange Rates data in under 10 minutes.

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What data can I load from Open Exchange Rates?

Here are some of the endpoints you can load from Open Exchange Rates:

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How do I authenticate with the Open Exchange Rates API?

Authentication is performed via an App ID (API key). Include app_id=YOUR_APP_ID as a query parameter on requests (e.g. https://openexchangerates.org/api/latest.json?app_id=YOUR_APP_ID). Some public endpoints like /currencies.json do not require authentication.

1. Get your credentials

1. Sign up / log in at https://openexchangerates.org. 2) Open Dashboard -> Apps / API Keys. 3) Create or view your App and copy the App ID (API key). 4) Use that App ID as the app_id query parameter in API requests.

2. Add them to .dlt/secrets.toml

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[sources.open_exchange_rates_source]
app_id = "your_app_id_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 Open Exchange Rates 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 open_exchange_rates_pipeline.py

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

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Pipeline open_exchange_rates_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset open_exchange_rates_data
The duckdb destination used duckdb:/open_exchange_rates.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 open_exchange_rates_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 currencies from the Open Exchange Rates 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 open_exchange_rates_source(app_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://openexchangerates.org/api",
            "auth": {
                "type": "api_key",
                "app_id": app_id,
            },
        },
        "resources": [
            {"name": "latest", "endpoint": {"path": "latest.json", "data_selector": "rates"}},
            {"name": "currencies", "endpoint": {"path": "currencies.json"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="open_exchange_rates_pipeline",
        destination="duckdb",
        dataset_name="open_exchange_rates_data",
    )
    load_info = pipeline.run(open_exchange_rates_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("open_exchange_rates_pipeline").dataset()
sessions_df = data.rates.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM open_exchange_rates_data.rates LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("open_exchange_rates_pipeline").dataset()
data.rates.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 Open Exchange Rates 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 Unauthorized or responses indicating a missing/invalid App ID, verify you are supplying the correct app_id query parameter (app_id=YOUR_APP_ID). Ensure the App ID is active and not revoked in your dashboard.

Rate limits and usage

Requests are rate-limited according to your plan. Monitor /usage.json and your dashboard. Exceeding limits will result in HTTP 429 responses; backoff and reduce request frequency or upgrade your plan.

Time-series & plan restrictions

Time-series (time-series.json) and bulk/time-range queries are restricted to Enterprise/Unlimited plans; requests from lower-tier accounts will be rejected or return limited data. Verify your plan if you receive permission errors.

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