Core Lightning Python API Docs | dltHub

Core Lightning REST API is a REST server that exposes functionalities of a Core Lightning node, allowing interaction with the node via HTTP requests. The REST API base URL is https://127.0.0.1:3010/ and Authentication requires either a rune for CLNRest or a macaroon for c-lightning-REST, passed in the HTTP header..

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 Core Lightning data in under 10 minutes.

---

What data can I load from Core Lightning?

Here are some of the endpoints you can load from Core Lightning:

---

How do I authenticate with the Core Lightning API?

For CLNRest, a rune token is required in the rune HTTP header. For c-lightning-REST, a macaroon token (base64 or hex encoded) is required in the macaroon HTTP header.

1. Get your credentials

For CLNRest, a new rune can be created using the createrune command. For c-lightning-REST, the access.macaroon file needs to be read, converted to base64 or hex, and used as the macaroon token.

2. Add them to .dlt/secrets.toml

Copy
[sources.core_lightning_source]
auth_token = "your_rune_or_macaroon_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 Core Lightning 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 core_lightning_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline core_lightning_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 getinfo and listfunds from the Core Lightning 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 core_lightning_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://127.0.0.1:3010/",
            "auth": {
                "type": "bearer",
                "rune or macaroon": auth_token,
            },
        },
        "resources": [
            {"name": "getinfo", "endpoint": {"path": "v1/getinfo"}},
            {"name": "list_funds", "endpoint": {"path": "v1/listFunds", "data_selector": "outputs, channels"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="core_lightning_pipeline",
        destination="duckdb",
        dataset_name="core_lightning_data",
    )
    load_info = pipeline.run(core_lightning_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("core_lightning_pipeline").dataset()
sessions_df = data.getinfo.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM core_lightning_data.getinfo LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("core_lightning_pipeline").dataset()
data.getinfo.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 Core Lightning 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.

---

Troubleshooting

Authentication Failures

Authentication failures typically occur due to incorrect or missing rune (for CLNRest) or macaroon (for c-lightning-REST) in the HTTP headers. Ensure that the token is correctly generated and included in the rune or macaroon header, respectively. For CLNRest, verify the rune has the necessary permissions for the requested operation (e.g., readonly for websocket connections). For c-lightning-REST, ensure the macaroon is base64 or hex encoded as required.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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