BlockVision Python API Docs | dltHub

BlockVision is a blockchain data platform that provides REST indexing APIs for Sui and Monad networks. The REST API base URL is https://api.blockvision.org/v2 and All requests require an API key supplied in the request headers..

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 BlockVision data in under 10 minutes.

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What data can I load from BlockVision?

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

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How do I authenticate with the BlockVision API?

The API uses an API key that must be sent in request headers, typically as x-api-key: <your_key> or Authorization: Bearer <your_key>.

1. Get your credentials

1. Open https://blockvision.org and sign in or create an account.
2. Navigate to the dashboard and select API Keys from the sidebar.
3. Click Create New API Key, optionally naming it.
4. Copy the generated key displayed – this is the credential you will use.
5. Store the key securely (e.g., in a secrets.toml file) and reference it in your dlt pipeline configuration.

2. Add them to .dlt/secrets.toml

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[sources.blockvision_indexing_api_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 BlockVision 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 blockvision_indexing_api_pipeline.py

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

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Pipeline blockvision_indexing_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset blockvision_indexing_api_data
The duckdb destination used duckdb:/blockvision_indexing_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 blockvision_indexing_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 sui_nft_collection_detail and sui_nft_list from the BlockVision 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 blockvision_indexing_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.blockvision.org/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "sui_nft_collection_detail", "endpoint": {"path": "sui/nft/collectionDetail"}},
            {"name": "sui_nft_list", "endpoint": {"path": "sui/nft/list", "data_selector": "object.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="blockvision_indexing_api_pipeline",
        destination="duckdb",
        dataset_name="blockvision_indexing_api_data",
    )
    load_info = pipeline.run(blockvision_indexing_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("blockvision_indexing_api_pipeline").dataset()
sessions_df = data.sui_nft_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM blockvision_indexing_api_data.sui_nft_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("blockvision_indexing_api_pipeline").dataset()
data.sui_nft_list.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 BlockVision 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 the API key is missing, invalid, or sent with the wrong header, the service returns a response with code set to a non‑zero value and a message such as Invalid API key. Verify that the key is correct and that it is sent in the header as documented.

Rate limiting

BlockVision enforces per‑minute call limits based on your tier. When the limit is exceeded, the response includes code indicating a rate‑limit error and a message like Rate limit exceeded. Reduce request frequency or upgrade your plan.

Pagination issues

List endpoints require the cursor parameter to fetch subsequent pages. Supplying an out‑of‑range cursor or an invalid limit (outside 1‑50) results in an error response with code non‑zero and a descriptive message. Ensure limit respects the documented bounds and store the nextPageCursor from the previous response for the next call.

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