Webflow Python API Docs | dltHub

Webflow is a visual web-development platform that provides a RESTful Data API to programmatically manage sites, CMS collections, items, fields, webhooks and related resources. The REST API base URL is https://api.webflow.com/v2 and All requests require a Bearer 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 Webflow data in under 10 minutes.

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What data can I load from Webflow?

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

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How do I authenticate with the Webflow API?

Webflow uses bearer tokens (Site, Workspace or OAuth access tokens). Include Authorization: Bearer YOUR_TOKEN and Accept: application/json on each request.

1. Get your credentials

1. For quick access use Site Tokens: open Site Settings in Webflow → Integrations → Generate Site API token. 2) For multi‑site or workspace access use a Workspace Token from Workspace settings. 3) For public apps or delegated access, register an OAuth App in the Webflow Developer dashboard and complete the OAuth flow to obtain an access token.

2. Add them to .dlt/secrets.toml

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[sources.webflow_source]
access_token = "your_webflow_bearer_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 Webflow 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 webflow_pipeline.py

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

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Pipeline webflow_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset webflow_data
The duckdb destination used duckdb:/webflow.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 webflow_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 sites and collection_items from the Webflow 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 webflow_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.webflow.com/v2",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "sites", "endpoint": {"path": "v2/sites", "data_selector": "sites"}},
            {"name": "collection_items", "endpoint": {"path": "v2/collections/{collection_id}/items", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="webflow_pipeline",
        destination="duckdb",
        dataset_name="webflow_data",
    )
    load_info = pipeline.run(webflow_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("webflow_pipeline").dataset()
sessions_df = data.collection_items.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM webflow_data.collection_items LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("webflow_pipeline").dataset()
data.collection_items.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 Webflow 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 the Authorization header is present and correctly formatted: Authorization: Bearer <token>. Tokens may be site‑scoped, workspace‑scoped, or OAuth‑scoped; missing required scopes result in 401/403 responses with error codes such as invalid_credentials or missing_scopes.

Rate limits

Webflow returns X-RateLimit-* headers and a 429 Too Many Requests status when limits are exceeded. Observe X-RateLimit-Remaining and implement exponential backoff before retrying.

Pagination and large collections

Collection items are paginated. Use query parameters page and limit as documented and continue requesting pages until the response contains no further items.

Common error format

Errors are JSON objects with keys: code, message, externalReference, details (array). Example codes include invalid_credentials, missing_scopes, too_many_requests, resource_not_found, internal_error.

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