ERCOT Python API Docs | dltHub

ERCOT Public API provides access to Texas energy market data through RESTful web services for registered developers. The REST API base URL is https://api.ercot.com and all requests require a Bearer token in the Authorization header and an Ocp-Apim-Subscription-Key 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 ERCOT data in under 10 minutes.

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What data can I load from ERCOT?

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

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How do I authenticate with the ERCOT API?

Requests require two headers: an 'Authorization' header with a 'Bearer <id_token>' value and an 'Ocp-Apim-Subscription-Key' header containing the user's primary subscription key. The ID token is obtained via a POST request to the authentication endpoint using the ROPC flow.

1. Get your credentials

To obtain ERCOT Public API credentials, follow these steps: 1. Navigate to the ERCOT API Explorer (https://apiexplorer.ercot.com/) and register for an account. 2. Sign in to the API Explorer and go to the 'Products' page. 3. Select the desired API product (e.g., 'Public API') and click 'Subscribe'. 4. Once subscribed, navigate to your Profile page to view active subscriptions. 5. Click the 'Show' button next to your subscription to reveal and copy your 'Primary key' (Subscription Key). 6. For authentication, you must also obtain an ID token by sending a POST request to the ERCOT B2C OAuth2 token endpoint using your registered credentials (username and password) as described in the ERCOT developer documentation.

2. Add them to .dlt/secrets.toml

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[sources.ercot_source]
subscription_key = "REPLACE_ME"

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 ERCOT 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 ercot_pipeline.py

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

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Pipeline ercot_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ercot_data
The duckdb destination used duckdb:/ercot.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 https://ercotb2c.b2clogin.com/ercotb2c.onmicrosoft.com/B2C_1_PUBAPI-ROPC-FLOW/oauth2/v2.0/token and https://api.ercot.com/api/public-reports from the ERCOT 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 ercot_source(subscription_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.ercot.com",
            "auth": {"type": "bearer", "token": subscription_key},
        },
        "resources": [
            {"name": "dam_hourly_lmp", "endpoint": {"path": "np4-183-cd/dam_hourly_lmp", "data_selector": "data"}},
            {"name": "dam_as_plan", "endpoint": {"path": "np4-33-cd/dam_as_plan", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ercot_pipeline",
        destination="duckdb",
        dataset_name="ercot_data",
    )
    load_info = pipeline.run(ercot_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("ercot_pipeline").dataset()
sessions_df = data.dam_hourly_lmp.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ercot_data.dam_hourly_lmp LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ercot_pipeline").dataset()
data.dam_hourly_lmp.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 ERCOT 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