Episerver Python API Docs | dltHub

Optimizely Content Delivery API is a REST API that exposes Optimizely (Episerver) CMS content and metadata for headless consumption by SPAs, mobile apps and external systems. The REST API base URL is https://api.cms.optimizely.com/preview3/experimental and all requests require a Bearer (JWT) token unless the API is configured to allow anonymous requests.

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 Episerver data in under 10 minutes.

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What data can I load from Episerver?

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

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How do I authenticate with the Episerver API?

The Content Delivery API uses OpenID Connect / OAuth2 bearer tokens (JWT). Include the token in the Authorization: Bearer header.

1. Get your credentials

1. In your Optimizely CMS environment configure or install EPiServer.OpenIDConnect (or use a hosted OpenID provider).
2. Register an OpenID Connect client (client id/secret) with the appropriate scopes for the Content Delivery API. Use the Client Credentials flow for machine‑to‑machine access.
3. Request an access token from the token endpoint using the client id/secret and required scope.
4. Include the token in your API calls with the header Authorization: Bearer <access_token>.

2. Add them to .dlt/secrets.toml

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[sources.episerver_source]
token = "your_jwt_bearer_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 Episerver 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 episerver_pipeline.py

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

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Pipeline episerver_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset episerver_data
The duckdb destination used duckdb:/episerver.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 episerver_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 content and items from the Episerver 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 episerver_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cms.optimizely.com/preview3/experimental",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "content", "endpoint": {"path": "content/{key}"}},
            {"name": "items", "endpoint": {"path": "content/{key}/items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="episerver_pipeline",
        destination="duckdb",
        dataset_name="episerver_data",
    )
    load_info = pipeline.run(episerver_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("episerver_pipeline").dataset()
sessions_df = data.content.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM episerver_data.content LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("episerver_pipeline").dataset()
data.content.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 Episerver 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 receive 401 Unauthorized, verify the Authorization header: Authorization: Bearer <token>. Ensure the token is issued for the configured OpenID Connect client and includes the required Content Delivery API scope. If running locally you may need development certificates configured as described in the API auth docs.

Scope or permission errors

403 Forbidden usually indicates the token lacks required scopes or the Content Delivery API has scope validation enabled. Ensure the client has the ContentDefinitionsApi (or required) scope and that DisableScopeValidation is false only where appropriate.

Pagination and list responses

List endpoints in examples (items, path, versions) return JSON arrays. If search or other query endpoints are used, results may be paged; consult the specific endpoint reference for paging query parameters and the response shape.

Rate limits and errors

The public docs do not publish global rate limits; handle 429 Too Many Requests by backing off and retrying after the Retry-After header if present. Handle 5xx server errors with retries and exponential backoff.

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