CoinGecko Python API Docs | dltHub

CoinGecko is the world's largest independent crypto data aggregator providing RESTful JSON endpoints for price, market, metadata, historical, exchange and onchain DEX data. The REST API base URL is Demo: https://api.coingecko.com/api/v3/ | Pro: https://pro-api.coingecko.com/api/v3/ and Demo endpoints are public (no key); Pro endpoints require an API key via 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 CoinGecko data in under 10 minutes.

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What data can I load from CoinGecko?

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

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How do I authenticate with the CoinGecko API?

Pro API keys must be supplied either in header x-cg-pro-api-key or as query param x_cg_pro_api_key; demo API keys use x-cg-demo-api-key / x_cg_demo_api_key when provided.

1. Get your credentials

1. Sign up or sign in at https://www.coingecko.com/en/api/pricing. 2) Open Developer Dashboard (https://www.coingecko.com/en/developers/dashboard). 3) Click + Add New Key. 4) Copy the generated API key. 5) For production, use the header x-cg-pro-api-key; avoid query params for security.

2. Add them to .dlt/secrets.toml

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[sources.coin_gecko_source]
api_key = "YOUR_CG_PRO_API_KEY"

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 CoinGecko 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 coin_gecko_pipeline.py

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

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Pipeline coin_gecko_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset coin_gecko_data
The duckdb destination used duckdb:/coin_gecko.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 coin_gecko_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 coins_markets and simple_price from the CoinGecko 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 coin_gecko_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Demo: https://api.coingecko.com/api/v3/  |  Pro: https://pro-api.coingecko.com/api/v3/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "coins_markets", "endpoint": {"path": "coins/markets"}},
            {"name": "simple_price", "endpoint": {"path": "simple/price"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="coin_gecko_pipeline",
        destination="duckdb",
        dataset_name="coin_gecko_data",
    )
    load_info = pipeline.run(coin_gecko_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("coin_gecko_pipeline").dataset()
sessions_df = data.coins_markets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM coin_gecko_data.coins_markets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("coin_gecko_pipeline").dataset()
data.coins_markets.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 CoinGecko 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 call Pro endpoints without a valid x-cg-pro-api-key (header) or x_cg_pro_api_key (query) you will receive 401/403 style errors and the call will not count toward monthly credits. Ensure the correct header name and key value are used.

Rate limits and credits

Pro API enforces per-minute rate limits and monthly call credits defined by your plan. Each successful 200 response consumes 1 monthly credit; all requests (including errors) count toward the minute rate limit. Monitor usage in Developer Dashboard -> Usage Report.

Pagination and large result sets

Endpoints such as /coins/markets support pagination via page and per_page query parameters; default per_page and max limits are documented per endpoint—use repeated requests to iterate pages. Some endpoints return top-level arrays (no wrapper key) so data selector should be empty when configuring ingestion.

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