VWO Python API Docs | dltHub

VWO is a feature experimentation and server‑side feature flagging platform for running experiments, managing feature flags, and targeting rules across environments. The REST API base URL is https://api.vwo.com/v2 and All requests require an API key / management token provided in request headers..

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 VWO data in under 10 minutes.

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What data can I load from VWO?

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

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How do I authenticate with the VWO API?

VWO uses API keys / management tokens. Include the key in the Authorization header (Authorization: Bearer ) or in the designated header as described in the VWO docs for personal/app tokens.

1. Get your credentials

1. Log in to your VWO account. 2) Navigate to Account Settings or Integrations → Developer/API. 3) Create a new Management API token or copy an existing one for Feature Experimentation. 4) Assign the required scopes (read/write) and store the token securely.

2. Add them to .dlt/secrets.toml

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[sources.vwo_source]
api_key = "your_vwo_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 VWO 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 vwo_pipeline.py

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

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Pipeline vwo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset vwo_data
The duckdb destination used duckdb:/vwo.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 vwo_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 feature_flags and projects from the VWO 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 vwo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.vwo.com/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "feature_flags", "endpoint": {"path": "accounts/{account_id}/features", "data_selector": "features"}},
            {"name": "projects", "endpoint": {"path": "accounts/{account_id}/projects", "data_selector": "projects"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vwo_pipeline",
        destination="duckdb",
        dataset_name="vwo_data",
    )
    load_info = pipeline.run(vwo_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("vwo_pipeline").dataset()
sessions_df = data.feature_flags.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM vwo_data.feature_flags LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("vwo_pipeline").dataset()
data.feature_flags.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 VWO 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 see 401/403 responses, confirm your API key/management token is correct and included in the Authorization header as 'Authorization: Bearer '. Ensure the token has the required scopes for the endpoint.

Rate limiting

The VWO docs reference API rate limits for REST endpoints. If you receive 429 Too Many Requests, implement exponential backoff and retry. Check response headers for rate‑limit details.

Pagination

Some list endpoints may be paginated. Use query parameters (page, limit, offset) where supported by the endpoint. If a response includes pagination metadata (total, page, per_page), use those to iterate until all records are retrieved.

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