Mina Protocol Python API Docs | dltHub

Mina Protocol's REST API documentation is found at https://docs.minaprotocol.com/zkapps/o1js. The o1js framework allows for creating zero-knowledge proofs. The tutorial on oracles demonstrates how to use REST APIs within smart contracts. The REST API base URL is http://localhost:3085 and no token-based auth; access controlled by localhost binding and daemon flags.

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 Mina Protocol data in under 10 minutes.

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What data can I load from Mina Protocol?

Here are some of the endpoints you can load from Mina Protocol:

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How do I authenticate with the Mina Protocol API?

The daemon exposes GraphQL/REST on localhost by default; no API key or Bearer token is needed. Restrict access by keeping the ports local or using limited‑port flags.

1. Get your credentials

1. Install the Mina daemon.
2. Start the daemon with default settings (the GraphQL API will listen on localhost port 3085).
3. If remote access is needed, restart with --rest-port <port> --insecure-rest-server or --open-limited-graphql-port to expose only selected ports.
4. No API key or secret is generated; access is controlled by the daemon configuration.

2. Add them to .dlt/secrets.toml

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[sources.mina_protocol_source]

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 Mina Protocol 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 mina_protocol_pipeline.py

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

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Pipeline mina_protocol_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mina_protocol_data
The duckdb destination used duckdb:/mina_protocol.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 mina_protocol_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 bestChain and account from the Mina Protocol 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 mina_protocol_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:3085",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "best_chain", "endpoint": {"path": "graphql", "data_selector": "data.bestChain"}},
            {"name": "account", "endpoint": {"path": "graphql", "data_selector": "data.account"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mina_protocol_pipeline",
        destination="duckdb",
        dataset_name="mina_protocol_data",
    )
    load_info = pipeline.run(mina_protocol_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("mina_protocol_pipeline").dataset()
sessions_df = data.best_chain.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mina_protocol_data.best_chain LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mina_protocol_pipeline").dataset()
data.best_chain.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 Mina Protocol 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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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