Soundcloud Python API Docs | dltHub

SoundCloud is an online audio distribution platform that lets users upload, promote, and share music and audio tracks. The REST API base URL is https://api.soundcloud.com and OAuth2 (authorization_code or client_credentials) with Bearer access tokens; legacy client_id supported for public endpoints..

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 Soundcloud data in under 10 minutes.

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What data can I load from Soundcloud?

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

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How do I authenticate with the Soundcloud API?

SoundCloud uses OAuth2 for authenticated requests: obtain an access token via the authorization_code flow (user authorization) or client_credentials flow (server-to-server). Include the token in the Authorization header as 'Authorization: Bearer <access_token>'. Some public endpoints historically accept a client_id query parameter for unauthenticated access but this is limited and deprecated for private/user data.

1. Get your credentials

1. Sign in at https://soundcloud.com/you/apps (or create a SoundCloud account). 2) Create a new app to receive a client_id and client_secret. 3) For user‑scoped access, implement the OAuth2 authorization_code flow: redirect user to /connect with your client_id and requested scopes, obtain authorization code, then exchange code for an access_token at the token endpoint. 4) For server access, use the client_credentials flow to exchange client_id and client_secret for an access_token. 5) Store the access_token securely and use it in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.soundcloud_source]
access_token = "your_oauth_access_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 Soundcloud 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 soundcloud_pipeline.py

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

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Pipeline soundcloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset soundcloud_data
The duckdb destination used duckdb:/soundcloud.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 soundcloud_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 tracks and users from the Soundcloud 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 soundcloud_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.soundcloud.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "tracks", "endpoint": {"path": "tracks"}},
            {"name": "users", "endpoint": {"path": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="soundcloud_pipeline",
        destination="duckdb",
        dataset_name="soundcloud_data",
    )
    load_info = pipeline.run(soundcloud_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("soundcloud_pipeline").dataset()
sessions_df = data.tracks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM soundcloud_data.tracks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("soundcloud_pipeline").dataset()
data.tracks.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 Soundcloud 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 your access token is valid and not expired, ensure the Authorization header is 'Bearer '. If using client_id, note many endpoints no longer accept client_id for private data.

Rate limits

SoundCloud enforces rate limits; requests returning 429 Too Many Requests indicate you must back off. Check response headers for limit info (varies). Implement exponential backoff and respect Retry-After header if present.

Pagination quirks

Many endpoints return a top-level array for list endpoints (/tracks, /users). Some authenticated or newer endpoints return a paginated collection with 'next_href' or 'collection'/'items' wrapper and support 'limit' and 'offset' or 'linked_partitioning=1' to receive 'next_href' for cursor‑style paging. Inspect the specific endpoint response to determine whether the list is the top‑level array or nested in a field like 'collection' or 'tracks' (playlists contain 'tracks').

Common errors

• 400 Bad Request: malformed parameters.
• 401 Unauthorized: invalid/expired token.
• 403 Forbidden: insufficient scope or access to resource.
• 404 Not Found: resource does not exist.
• 429 Too Many Requests: rate limited.
• 500/502/503: server errors; retry with 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