Twitch Python API Docs | dltHub

Twitch is a live streaming platform and API that provides programmatic access to Twitch resources (streams, users, clips, games, channels, etc.) via the Helix REST API. The REST API base URL is https://api.twitch.tv/helix and All requests require a Client-Id header and most requests require an OAuth 2.0 Bearer token (app access token or user access token) in the Authorization 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 Twitch data in under 10 minutes.

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What data can I load from Twitch?

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

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How do I authenticate with the Twitch API?

Send Client-Id: for all requests. For endpoints that require authorization, include Authorization: Bearer <access_token>. Use app access tokens for application-level endpoints and user access tokens (with required scopes) for user-scoped endpoints.

1. Get your credentials

1. Register an application at https://dev.twitch.tv/console/apps to get a Client ID and Client Secret. 2) For app access token: request an OAuth token via client credentials flow (POST to https://id.twitch.tv/oauth2/token?client_id=...&client_secret=...&grant_type=client_credentials). 3) For user access token: use an OAuth authorization code or implicit flow to obtain a user access token with the scopes you need. 4) Provide Client-Id header and Authorization: Bearer in API requests.

2. Add them to .dlt/secrets.toml

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[sources.twitch_source]
client_id = "your_client_id_here"
token = "your_app_or_user_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 Twitch 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 twitch_pipeline.py

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

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Pipeline twitch_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset twitch_data
The duckdb destination used duckdb:/twitch.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 twitch_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 streams and users from the Twitch 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 twitch_source(client_id_and_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.twitch.tv/helix",
            "auth": {
                "type": "bearer",
                "token": client_id_and_token,
            },
        },
        "resources": [
            {"name": "streams", "endpoint": {"path": "streams", "data_selector": "data"}},
            {"name": "users", "endpoint": {"path": "users", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="twitch_pipeline",
        destination="duckdb",
        dataset_name="twitch_data",
    )
    load_info = pipeline.run(twitch_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("twitch_pipeline").dataset()
sessions_df = data.streams.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM twitch_data.streams LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("twitch_pipeline").dataset()
data.streams.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 Twitch 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

Ensure you send Client-Id header and Authorization: Bearer . 401 Unauthorized occurs when token is missing, expired, or lacks required scopes. If token invalid, reauthorize or refresh (for refreshable flows).

Rate limits

Twitch enforces token-bucket rate limits per client ID and per-user for user access tokens; exceeding bucket returns 429 Too Many Requests. Some endpoints have non-default point costs — check endpoint docs.

Pagination quirks

List responses return a top-level 'data' array and a 'pagination' object with 'cursor' for forward pagination. To page forward set ?after=. Some endpoints only support forward pagination; check the endpoint docs.

Common error responses

401 Unauthorized — missing/invalid token or wrong Client-Id. 403 Forbidden — insufficient scope or permissions. 404 Not Found — invalid id. 429 Too Many Requests — rate limit exceeded. 422/400 — invalid parameters.

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