OBS Studio Python API Docs | dltHub

OBS Studio is a streaming/recording application whose official documentation describes libobs C APIs for embedding and plugin development; it does not provide a built-in HTTP REST API. The REST API base URL is N/A — OBS Studio does not expose an official REST HTTP base URL in the provided documentation. and No HTTP REST authentication (no official REST API). Use plugin-specific auth (e.g., obs-websocket uses password-based auth over WebSocket) when using third-party remote plugins..

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 OBS Studio data in under 10 minutes.

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What data can I load from OBS Studio?

Here are some of the endpoints you can load from OBS Studio:

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How do I authenticate with the OBS Studio API?

OBS core exposes C library APIs and plugin interfaces; there is no HTTP header-based auth described in the official docs. For remote control via third-party plugins (e.g., obs-websocket), authentication is plugin-specific (obs-websocket uses a challenge-response password over WebSocket).

1. Get your credentials

Not applicable for core OBS (no REST credentials). If using obs-websocket plugin: install the plugin, enable a server password in plugin settings, then use that password when connecting via WebSocket client (follow obs-websocket documentation).

2. Add them to .dlt/secrets.toml

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[sources.obs_studio_source]
password = "your_obs_websocket_password"

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 OBS Studio 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 obs_studio_pipeline.py

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

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Pipeline obs_studio_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset obs_studio_data
The duckdb destination used duckdb:/obs_studio.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 obs_studio_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 N/A from the OBS Studio 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 obs_studio_source(password (when using obs-websocket plugin)=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "N/A — OBS Studio does not expose an official REST HTTP base URL in the provided documentation.",
            "auth": {
                "type": "none (core); for obs-websocket use 'api_key'/'password' handled by plugin (not part of core REST).",
                "password": password (when using obs-websocket plugin),
            },
        },
        "resources": [
            {"name": "N/A", "endpoint": {"path": "N/A"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="obs_studio_pipeline",
        destination="duckdb",
        dataset_name="obs_studio_data",
    )
    load_info = pipeline.run(obs_studio_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("obs_studio_pipeline").dataset()
sessions_df = data.N/A.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM obs_studio_data.N/A LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("obs_studio_pipeline").dataset()
data.N/A.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 OBS Studio 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 and connectivity

OBS core does not provide HTTP REST auth because it does not provide a REST API. If you are using a third-party plugin (e.g., obs-websocket), consult that plugin’s docs for authentication details (common issues: wrong password, plugin not running, firewall blocking localhost ports).

Plugin vs core API confusion

The official OBS documentation pages reference libobs C APIs and are intended for plugin/module authors (C interfaces). They do not document HTTP endpoints. Do not expect REST-style URLs or JSON HTTP responses from core OBS.

No pagination/rate-limits

Since there is no official REST API in the core documentation, there are no documented HTTP pagination or rate-limit behaviors. Third-party plugins may impose their own limits; consult plugin docs.

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