Axle Energy Python API Docs | dltHub

Axle Energy is a platform providing APIs for managing energy assets, meters, and integration with energy markets. The REST API base URL is https://api.axle.energy and all requests require a Bearer token in the Authorization 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 add "dlt[hub]" and start loading Axle Energy data in under 10 minutes.

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What data can I load from Axle Energy?

Here are some of the endpoints you can load from Axle Energy:

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How do I authenticate with the Axle Energy API?

Axle Energy uses OAuth2 password flow authentication. Users exchange credentials (username and password) via a POST request to /auth/token-form to receive an access token, which must then be included in the Authorization header of subsequent requests using the 'Bearer' scheme.

1. Get your credentials

Axle Energy uses OAuth2-style password authentication rather than a static API key. To obtain an access token, send a POST request with 'application/x-www-form-urlencoded' data to the /auth/token-form endpoint. Include your 'username' and 'password' in the request body, ensuring the 'grant_type' field is set to 'password'. The API returns an 'access_token' valid for 1 hour, which must be passed in subsequent requests as a 'Bearer' token in the 'Authorization' header. For end-user specific operations, you may use the /auth/component-token endpoint to obtain a site-scoped token.

2. Add them to .dlt/secrets.toml

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[sources.axle_energy_source]
axle_username = "your_username_here"
axle_password = "your_password_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Axle Energy 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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uv run python axle_energy_pipeline.py

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

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Pipeline axle_energy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset axle_energy_data
The duckdb destination used duckdb:/axle_energy.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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uv run dlthub 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 /auth/token-form and /auth/component-token from the Axle Energy 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 axle_energy_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.axle.energy",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "entities/asset", "data_selector": "items"}},
            {"name": "sites", "endpoint": {"path": "entities/site", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="axle_energy_pipeline",
        destination="duckdb",
        dataset_name="axle_energy_data",
    )
    load_info = pipeline.run(axle_energy_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("axle_energy_pipeline").dataset()
sessions_df = data.assets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM axle_energy_data.assets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("axle_energy_pipeline").dataset()
data.assets.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 Axle Energy 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 harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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