Mempool Python API Docs | dltHub

Mempool.space provides a REST API for accessing Bitcoin data, including transactions, addresses, blocks, and mining information. The API supports various endpoints for real-time and historical data. Use the API to query details about the Bitcoin network. The REST API base URL is https://mempool.space/api and optional header‑based X‑Mempool‑Auth for accelerator endpoints (no API key required for public endpoints).

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 Mempool data in under 10 minutes.

---

What data can I load from Mempool?

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

---

How do I authenticate with the Mempool API?

The public endpoints require no authentication. Private accelerator endpoints require an X-Mempool-Auth header containing a provider‑issued token.

1. Get your credentials

1. Contact the mempool.space team or the specific accelerator service (e.g., Stacksats) to request an X‑Mempool‑Auth token.
2. Provide your account details and the intended use case.
3. Receive the token (e.g., "stacksats") which you will include in the X-Mempool-Auth header for accelerator endpoints.
4. Store the token in your secrets.toml as shown in the secrets_toml_example section.

2. Add them to .dlt/secrets.toml

Copy
[sources.mempool_source]
x_mempool_auth = "your_x_mempool_auth_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 venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Mempool 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
python mempool_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline mempool_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.

---

Python pipeline example

This example loads mempool_recent and tx from the Mempool 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 mempool_source(x_mempool_auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://mempool.space/api",
            "auth": {
                "type": "api_key",
                "x_mempool_auth": x_mempool_auth,
            },
        },
        "resources": [
            {"name": "mempool_recent", "endpoint": {"path": "api/mempool/recent"}},
            {"name": "tx", "endpoint": {"path": "api/tx/:txid"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mempool_pipeline",
        destination="duckdb",
        dataset_name="mempool_data",
    )
    load_info = pipeline.run(mempool_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("mempool_pipeline").dataset()
sessions_df = data.mempool_recent.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM mempool_data.mempool_recent LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("mempool_pipeline").dataset()
data.mempool_recent.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 Mempool 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 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.

Copy
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