Coassemble Python API Docs | dltHub

Coassemble is a headless learning content platform that provides APIs to list, embed and manage courses, clients, users and tracking records for embedding and headless course creation. The REST API base URL is https://api.coassemble.com and Requests require the Coassemble Headless authorization header (COASSEMBLE-V1-SHA256) containing UserId and UserToken; some examples also show Bearer tokens..

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 Coassemble data in under 10 minutes.

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What data can I load from Coassemble?

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

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How do I authenticate with the Coassemble API?

Headless API requests use an Authorization header in the form: 'Authorization: COASSEMBLE-V1-SHA256 UserId={USER_ID}, UserToken={API_KEY}'. The docs also include examples using a standard 'Authorization: Bearer {TOKEN}' header for some endpoints; include Content-Type: application/json for POST bodies.

1. Get your credentials

1. In your Coassemble workspace, open Settings → Add‑ons → API and enable the API (initially in test mode).
2. Complete any required entry survey to enable API access.
3. The dashboard will display your API credentials: User ID and API key (UserToken). Save them securely.
4. You can regenerate credentials from the same API settings screen if needed.

2. Add them to .dlt/secrets.toml

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[sources.coassemble_source]
user_id = "your_user_id_here"
api_key = "your_api_key_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 Coassemble 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 coassemble_pipeline.py

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

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Pipeline coassemble_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset coassemble_data
The duckdb destination used duckdb:/coassemble.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 coassemble_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 course_url from the Coassemble 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 coassemble_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.coassemble.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "courses", "endpoint": {"path": "api/v1/headless/courses"}},
            {"name": "course_url", "endpoint": {"path": "api/v1/headless/course/url"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM coassemble_data.courses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("coassemble_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 Coassemble 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 401/403 responses, ensure you are sending the Authorization header in the exact format expected by your workspace: 'Authorization: COASSEMBLE-V1-SHA256 UserId={USER_ID}, UserToken={API_KEY}'. Also verify you enabled the API in Settings → Add‑ons and that your API credentials have not been regenerated.

Rate limits and pagination

The public docs do not publish explicit rate limits; treat the API as rate‑limited and implement exponential backoff on 429 responses. Most list endpoints are paginated using 'length' and 'page' query parameters — make sure to iterate pages until no more records are returned.

Missing response wrapper keys

The documentation does not expose explicit sample JSON responses naming the root key that contains the records array for list endpoints. If your client library expects a specific collection key (e.g., 'data' or 'courses'), first inspect a real API response (curl or Postman) and adapt your data selector accordingly. Leave the data selector empty to parse the top‑level array/object if the API returns that structure.

SCORM export and large responses

SCORM export endpoints return a binary zip file; handle streaming responses and large downloads appropriately (do not attempt to JSON‑decode).

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