Teachable Python API Docs | dltHub

Teachable is a platform that enables creators to build and sell online courses, offering a public API for accessing course, user, and transaction data. The REST API base URL is https://api.teachable.com 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 pip install "dlt[workspace]" and start loading Teachable data in under 10 minutes.

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What data can I load from Teachable?

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

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How do I authenticate with the Teachable API?

The API uses a personal access token (API key) supplied as a Bearer token in the Authorization header; JSON requests should include Content-Type: application/json.

1. Get your credentials

1. Log in to your Teachable dashboard (Pro or Business plan).\n2. Navigate to Site > Settings > Code & API or the Developer / API section.\n3. Click Generate Personal Access Token.\n4. Copy the generated token and store it securely; it will be used as the Bearer token in API calls.

2. Add them to .dlt/secrets.toml

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[sources.teachable_source]
api_key = "your_teachable_personal_access_token"

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 Teachable 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 teachable_pipeline.py

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

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Pipeline teachable_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset teachable_data
The duckdb destination used duckdb:/teachable.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 teachable_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 courses and users from the Teachable 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 teachable_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.teachable.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "courses", "endpoint": {"path": "v1/courses", "data_selector": "data"}},
            {"name": "users", "endpoint": {"path": "v1/users", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="teachable_pipeline",
        destination="duckdb",
        dataset_name="teachable_data",
    )
    load_info = pipeline.run(teachable_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("teachable_pipeline").dataset()
sessions_df = data.courses.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM teachable_data.courses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("teachable_pipeline").dataset()
data.courses.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 Teachable 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

• 401 Unauthorized: Returned when the Bearer token is missing, malformed, or belongs to a school that does not have API access. Verify that the token is correct and that the account is on a Pro or Business plan.

Plan / permission errors

• API access is limited to Pro and Business plan schools. Requests from free‑tier schools will be rejected with a permission error.

Rate limiting

• The API may respond with 429 Too Many Requests. honor the Retry‑After header and implement exponential back‑off before retrying.

Pagination

• List endpoints return paginated results. The response includes pagination metadata with next and prev links or page/per_page parameters. Continue fetching pages until no next link is provided.

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