OpenCage Data Python API Docs | dltHub

OpenCage Data is a geocoding service that provides worldwide forward and reverse geocoding via a REST API using open data. The REST API base URL is https://api.opencagedata.com and all requests require an API key passed as a 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 OpenCage Data data in under 10 minutes.

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What data can I load from OpenCage Data?

Here are some of the endpoints you can load from OpenCage Data:

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How do I authenticate with the OpenCage Data API?

Authentication is via a 30-32 character API key that must be included on every request as the key query parameter (key=YOUR-API-KEY). No special HTTP Authorization header is required.

1. Get your credentials

1. Sign up or sign in at https://opencagedata.com/; 2) Open your account dashboard > Geocoding API; 3) Create or copy an existing API key; 4) (Optional for subscriptions) manage IP restrictions or create additional keys in the Geocoding API settings.

2. Add them to .dlt/secrets.toml

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[sources.opencage_data_source]
api_key = "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 OpenCage Data 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 opencage_data_pipeline.py

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

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Pipeline opencage_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset opencage_data_data
The duckdb destination used duckdb:/opencage_data.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 opencage_data_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 geocode and ping from the OpenCage Data 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 opencage_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.opencagedata.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "geocode", "endpoint": {"path": "geocode/v1/json", "data_selector": "results"}},
            {"name": "ping", "endpoint": {"path": "ping"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="opencage_data_pipeline",
        destination="duckdb",
        dataset_name="opencage_data_data",
    )
    load_info = pipeline.run(opencage_data_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("opencage_data_pipeline").dataset()
sessions_df = data.geocode.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM opencage_data_data.geocode LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("opencage_data_pipeline").dataset()
data.geocode.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 OpenCage Data 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 get HTTP 401, 403 or status.code != 200, verify the key parameter value. 401 indicates missing/invalid key. 403 can indicate a disabled key or IP address rejected (if IP restrictions configured). Use the dashboard to re-enable or create a new key.

Rate limits and quota errors

Free trial accounts are limited (e.g., 2,500/day) and 1 request/sec; exceeding per-second rate may return 429 Too Many Requests. If daily quota exceeded you will receive 402 Quota Exceeded; repeated abuse may lead to 403 disabled. Responses for accounts with hard limits include a rate object and X-RateLimit-* headers: rate.limit, rate.remaining, rate.reset.

Common response codes and where to look

Check the top-level status object (status.code and status.message) and total_results. For accounts with hard limits the response includes a top-level rate object with current usage. Error responses include HTTP status codes (400,401,402,403,404,405,408,410,426,429,503) and explanatory message in status.message.

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