Absorb lms Python API Docs | dltHub

Absorb LMS is a cloud‑based learning management system exposing a RESTful Integration API to read and manage LMS resources. The REST API base URL is US: https://rest.myabsorb.com CA: https://rest.myabsorb.ca EU: https://rest.myabsorb.eu AU: https://rest.myabsorb.com.au Sandbox (US): https://rest.sandbox.myabsorb.com (and .ca, .eu, .com.au for other regions) and All requests require a short‑lived Bearer token plus the private API key in the 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 Absorb lms data in under 10 minutes.

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What data can I load from Absorb lms?

Here are some of the endpoints you can load from Absorb lms:

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How do I authenticate with the Absorb lms API?

Clients obtain an API token by POSTing admin credentials and the private key to the /authenticate endpoint, including the header "x-api-key: <private_key>". The returned token is sent as "Authorization: Bearer " on all later requests.

1. Get your credentials

1. Purchase or subscribe to the Absorb Integration API via Absorb sales/support.
2. Absorb provides a private API key linked to your portal.
3. Ensure an Admin user exists in your portal (or create one).
4. Generate a token by POSTing { "username": "", "password": "", "privateKey": "<your_private_key>" } to https://rest.myabsorb./authenticate with header x-api-key: <your_private_key> and Content-Type: application/json.

2. Add them to .dlt/secrets.toml

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[sources.absorb_lms_source]
api_token = "your_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 Absorb lms 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 absorb_lms_pipeline.py

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

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Pipeline absorb_lms_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset absorb_lms_data
The duckdb destination used duckdb:/absorb_lms.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 absorb_lms_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 users and courses from the Absorb lms 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 absorb_lms_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "US: https://rest.myabsorb.com
CA: https://rest.myabsorb.ca
EU: https://rest.myabsorb.eu
AU: https://rest.myabsorb.com.au
Sandbox (US): https://rest.sandbox.myabsorb.com (and .ca, .eu, .com.au for other regions)",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users"}},
            {"name": "courses", "endpoint": {"path": "courses"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="absorb_lms_pipeline",
        destination="duckdb",
        dataset_name="absorb_lms_data",
    )
    load_info = pipeline.run(absorb_lms_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("absorb_lms_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM absorb_lms_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("absorb_lms_pipeline").dataset()
data.users.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 Absorb lms 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 a 401 Unauthorized response, verify that you are sending both the Authorization: Bearer <token> header and the x-api-key: <private_key> header. Tokens expire after 4 hours or when a new token is requested for the same username.

Rate limits / 429 Too Many Requests

The API enforces rate limits. On receiving a 429 response, reduce request frequency and implement exponential back‑off before retrying.

Pagination and large datasets

Many open‑ended GET endpoints support pagination using the _limit and _offset query parameters. For endpoints without pagination, use the ModifiedSince filter to sync records incrementally.

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