D2l brightspace Python API Docs | dltHub

Valence API is Brightspace's RESTful API for accessing learning platform data. The REST API base URL is https://{yourBrightspaceHost} and All requests require an OAuth2 Bearer token (or legacy ID/Key signing)..

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 D2l brightspace data in under 10 minutes.

---

What data can I load from D2l brightspace?

Here are some of the endpoints you can load from D2l brightspace:

---

How do I authenticate with the D2l brightspace API?

OAuth2 is the recommended method: include an Authorization: Bearer <access_token> header with each request. Legacy authentication can be performed by adding x_a (Application ID) and x_b (Application Signature) query parameters or signed query strings.

1. Get your credentials

1. Log in to your Brightspace tenant as an administrator.
2. Navigate to Admin Tools → Developer Keys.
3. Click Create New Key, give it a name, and select OAuth2 as the authentication type.
4. After saving, record the Client ID and Client Secret – these are your credentials.
5. (Optional) Enable the legacy Application ID / Application Key fields if you need ID‑Key signing.
6. Use the Client ID/Secret to request an access token via the /d2l/auth/api/token endpoint.

2. Add them to .dlt/secrets.toml

Copy
[sources.d2l_brightspace_source]
client_id = "your_client_id"
client_secret = "your_client_secret"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 D2l brightspace 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:

Copy
python d2l_brightspace_pipeline.py

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

Copy
Pipeline d2l_brightspace_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset d2l_brightspace_data
The duckdb destination used duckdb:/d2l_brightspace.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline d2l_brightspace_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.

---

Python pipeline example

This example loads versions and classlist from the D2l brightspace API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def d2l_brightspace_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{yourBrightspaceHost}",
            "auth": {
                "type": "bearer",
                "token": client_id,
            },
        },
        "resources": [
            {"name": "versions", "endpoint": {"path": "d2l/api/versions/"}},
            {"name": "classlist", "endpoint": {"path": "d2l/api/le/{version}/{orgUnitId}/classlist/"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="d2l_brightspace_pipeline",
        destination="duckdb",
        dataset_name="d2l_brightspace_data",
    )
    load_info = pipeline.run(d2l_brightspace_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.

---

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):

Copy
import dlt

data = dlt.pipeline("d2l_brightspace_pipeline").dataset()
sessions_df = data.classlist.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM d2l_brightspace_data.classlist LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("d2l_brightspace_pipeline").dataset()
data.classlist.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load D2l brightspace 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.

---

Troubleshooting

Authentication errors

• 401 Unauthorized – Occurs when the Bearer token is missing, expired, or invalid. Refresh the token via /d2l/auth/api/token.
• 403 Forbidden – The token is valid but the user does not have permission for the requested resource.

Rate limiting

• 429 Too Many Requests – The API has throttled the client. Wait the period indicated in the Retry-After header before retrying.

Pagination quirks

• Endpoints that return a paged result set include a bookmark query parameter. Use the bookmark value from the response to request the next page. The response contains an items array with the records.

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

---

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.

Copy
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