BlockCypher Ethereum API Python API Docs | dltHub

BlockCypher provides a RESTful API for interacting with Ethereum and other blockchains, enabling queries for chain state, addresses, transactions, and contracts. The REST API base URL is https://api.blockcypher.com/v1 and All non-read-only requests require an API token passed as a URL 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 add "dlt[hub]" and start loading BlockCypher Ethereum API data in under 10 minutes.

---

What data can I load from BlockCypher Ethereum API?

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

---

How do I authenticate with the BlockCypher Ethereum API API?

Authentication is performed by passing a user token as a URL query parameter named 'token'. The API does not support authentication via standard HTTP Authorization headers.

1. Get your credentials

To obtain credentials for the BlockCypher Ethereum API: 1. Navigate to the BlockCypher accounts portal at https://accounts.blockcypher.com. 2. Create a developer account and verify your email address. 3. Log in to the dashboard. 4. Locate the section labeled API Tokens. 5. Click the Create Token button, provide a name for your token, and save the generated value securely.

2. Add them to .dlt/secrets.toml

Copy
[sources.blockcypher_ethereum_api_source]
token = "your_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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:

Copy
uv run dlthub ai toolkit install rest-api-pipeline

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 BlockCypher Ethereum 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:

Copy
uv run python blockcypher_ethereum_api_pipeline.py

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

Copy
Pipeline blockcypher_ethereum_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset blockcypher_ethereum_api_data
The duckdb destination used duckdb:/blockcypher_ethereum_api.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads addrs/{address} and blocks/{block_hash} from the BlockCypher Ethereum API API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def blockcypher_ethereum_api_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.blockcypher.com/v1",
            "auth": {"type": "api_key", "api_key": api_token, "name": "token", "location": "query"},
        },
        "resources": [
            {"name": "address_full", "endpoint": {"path": "v1/eth/main/addrs/{address}/full", "data_selector": "txs"}},
            {"name": "tx_unconfirmed", "endpoint": {"path": "v1/eth/main/txs", "data_selector": "txs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="blockcypher_ethereum_api_pipeline",
        destination="duckdb",
        dataset_name="blockcypher_ethereum_api_data",
    )
    load_info = pipeline.run(blockcypher_ethereum_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.

---

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):

Copy
import dlt

data = dlt.pipeline("blockcypher_ethereum_api_pipeline").dataset()
sessions_df = data.tx_unconfirmed.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM blockcypher_ethereum_api_data.tx_unconfirmed LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("blockcypher_ethereum_api_pipeline").dataset()
data.tx_unconfirmed.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load BlockCypher Ethereum 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.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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