360learning Python API Docs | dltHub

360Learning is a learning management platform that provides REST APIs to manage users, groups, courses, paths, sessions and related L&D resources. The REST API base URL is https://app.360learning.com/api/v2 and All requests require OAuth2 Client Credentials (Bearer token) and a version 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 add "dlt[hub]" and start loading 360learning data in under 10 minutes.

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What data can I load from 360learning?

Here are some of the endpoints you can load from 360learning:

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How do I authenticate with the 360learning API?

Exchange client_id and client_secret at POST https://app.360learning.com/api/v2/oauth2/token to obtain an access token (expires in 1 hour). Include Authorization: Bearer <access_token> and 360-api-version: v2.0 in all requests.

1. Get your credentials

1. Sign in to 360Learning as a platform owner or admin. 2) Navigate to the Admin / API keys section. 3) Create new API credentials (client_id and client_secret) and assign the required scopes. 4) Store the client_id and client_secret securely and use them to request an access token via the token endpoint.

2. Add them to .dlt/secrets.toml

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[sources._360learning_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_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 init
uv add "dlt[hub]"

1. Install the dlt AI Workbench:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 360learning 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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uv run python _360learning_pipeline.py

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

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Pipeline _360learning_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset _360learning_data
The duckdb destination used duckdb:/_360learning.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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uv run dlthub 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 360learning 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 _360learning_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.360learning.com/api/v2",
            "auth": {
                "type": "bearer",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "data"}},
            {"name": "courses", "endpoint": {"path": "courses", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_360learning_pipeline",
        destination="duckdb",
        dataset_name="_360learning_data",
    )
    load_info = pipeline.run(_360learning_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("_360learning_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM _360learning_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("_360learning_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 360learning 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 credentials are missing or invalid the API returns HTTP 401 and an error JSON such as "error": "missing_company_id" or "error": "invalid_token". Ensure you exchange client_id/client_secret for an access token and include Authorization: Bearer <token> and 360-api-version: v2.0 headers; refresh tokens every hour.

Rate limits

GET requests: up to 50 read operations/sec per company ID and 10 read operations/sec per IP. Exceeding limits returns HTTP 429. Pace requests using batching or sleep/backoff.

Pagination

Most list endpoints are paginated and return items under the data key. Use pagination parameters (page, per_page or next cursor depending on endpoint) to iterate through results.

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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-runtime

• 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