dbt Cloud Python API Docs | dltHub

dbt Cloud API is a REST API for administrating and interacting with dbt Cloud accounts, projects, jobs, runs, and metadata. The REST API base URL is https://{dbt_cloud_host}/api/v2 and all requests require an API token (personal access token or service account token) provided 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 pip install "dlt[workspace]" and start loading dbt Cloud data in under 10 minutes.

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What data can I load from dbt Cloud?

Here are some of the endpoints you can load from dbt Cloud:

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How do I authenticate with the dbt Cloud API?

Requests must include an API token (personal or service account) in the Authorization header. Use the token issued by dbt Cloud; the token type is documented as personal access tokens or service account tokens.

1. Get your credentials

1. Sign in to dbt Cloud (cloud.getdbt.com). 2) Open Account settings > API Access (or User settings > API Access). 3) Create a new Personal Access Token or Service Account token, give it an appropriate name and scope. 4) Copy the token value and store it securely; it will be provided once. 5) Note your account_id (shown in account settings) and dbt_cloud_host (e.g. cloud.getdbt.com).

2. Add them to .dlt/secrets.toml

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[sources.dbt_cloud_source]
api_token = "your_dbt_cloud_api_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.

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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 dbt Cloud 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 dbt_cloud_pipeline.py

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

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Pipeline dbt_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dbt_cloud_data
The duckdb destination used duckdb:/dbt_cloud.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 dbt_cloud_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 jobs and runs from the dbt Cloud 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 dbt_cloud_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{dbt_cloud_host}/api/v2",
            "auth": {
                "type": "api_key",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "accounts/{account_id}/jobs/", "data_selector": "results"}},
            {"name": "runs", "endpoint": {"path": "accounts/{account_id}/runs/", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dbt_cloud_pipeline",
        destination="duckdb",
        dataset_name="dbt_cloud_data",
    )
    load_info = pipeline.run(dbt_cloud_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("dbt_cloud_pipeline").dataset()
sessions_df = data.jobs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dbt_cloud_data.jobs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dbt_cloud_pipeline").dataset()
data.jobs.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 dbt Cloud 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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Troubleshooting

Authentication failures

If you get 401/403 responses, verify your API token is correct, has not expired, and is a Personal Access or Service Account token with sufficient scope. Ensure the Authorization header includes the token and that you are using the correct dbt_cloud_host and account_id.

Rate limiting and errors

The API may return 429 on rate limits; implement exponential backoff and retries. Other common errors include 400 for invalid parameters and 404 when resources (account, project, job, run) are not found.

Pagination

List endpoints are paginated and return results in a results array (and include pagination metadata). Use the provided page and page_size (or limit/offset) query parameters to iterate pages.

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

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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