Load MetaSleuth BlockSec AML API data to DuckDB

MetaSleuth BlockSec AML API provides endpoints for address label queries and wallet risk assessment services. Everything needed to build a working MetaSleuth BlockSec AML API → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your MetaSleuth BlockSec AML API to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

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Run uvx dlthub-init@latest to build a pipeline from MetaSleuth BlockSec AML API to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the MetaSleuth BlockSec AML API API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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MetaSleuth BlockSec AML API API at a glance

These values come from the MetaSleuth BlockSec AML API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MetaSleuth BlockSec AML API API?

Authentication requires an API key passed in the custom 'API-KEY' HTTP header for every request.

1. Get your credentials

To obtain your API credentials, first register for a BlockSec account at https://account.blocksec.com/signup. After registration, log in and navigate to the APIs panel within the Settings section (https://metasleuth.io/settings?type=apis) to generate your API key. If you have a customized enterprise subscription, contact your dedicated service representative to acquire your credentials.

2. Add them to .dlt/secrets.toml

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[sources.metasleuth_blocksec_aml_api_source]
api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What MetaSleuth BlockSec AML API data can I load into DuckDB?

These are the MetaSleuth BlockSec AML API endpoints dlt can load into DuckDB:

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How do I load only new MetaSleuth BlockSec AML API records?

The MetaSleuth BlockSec AML API API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "supported_chains", "endpoint": {
    "path": "chain-list",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated MetaSleuth BlockSec AML API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading address-label and wallet-screening from the MetaSleuth BlockSec AML API API into DuckDB:

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

@dlt.source
def metasleuth_blocksec_aml_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://aml.blocksec.com",
            "auth": {"type": "api_key", "api_key": api_key, "name": "API-KEY", "location": "header"},
        },
        "resources": [
            {"name": "supported_chains", "endpoint": {"path": "chain-list", "data_selector": "data"}},
            {"name": "address_labels", "endpoint": {"path": "labels", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_metasleuth_blocksec_aml_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="metasleuth_blocksec_aml_api_pipeline",
        destination="duckdb",
        dataset_name="metasleuth_blocksec_aml_api_data",
    )
    load_info = pipeline.run(metasleuth_blocksec_aml_api_source())
    print(load_info)


if __name__ == "__main__":
    load_metasleuth_blocksec_aml_api_to_duckdb()

Run it with python metasleuth_blocksec_aml_api_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query MetaSleuth BlockSec AML API data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("metasleuth_blocksec_aml_api_pipeline").dataset()
df = data.supported_chains.df()
print(df.head())

SQL:

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SELECT * FROM metasleuth_blocksec_aml_api_data.supported_chains LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the MetaSleuth BlockSec AML API to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw MetaSleuth BlockSec AML API loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load MetaSleuth BlockSec AML API data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack