Plasmic CMS Python API Docs | dltHub

Plasmic CMS is a headless CMS integrated with Plasmic Studio that provides a RESTful API for reading and writing structured CMS data. The REST API base URL is https://data.plasmic.app/api/v1 and all CMS API requests require an x-plasmic-api-cms-tokens header containing CMS_ID and token..

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 Plasmic CMS data in under 10 minutes.

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What data can I load from Plasmic CMS?

Here are some of the endpoints you can load from Plasmic CMS:

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How do I authenticate with the Plasmic CMS API?

The API uses a single custom header x-plasmic-api-cms-tokens with value <CMS_ID>:. Use the public token for read-only GET endpoints and the secret token for write operations or fetching drafts.

1. Get your credentials

1. Open https://studio.plasmic.app and sign in. 2) Select your workspace, then select the CMS. 3) Go to the CMS Settings tab. 4) Copy the CMS ID, Public Token, and Secret Token listed there. 5) For model IDs, open CMS -> Edit models -> select a model and note its Unique identifier.

2. Add them to .dlt/secrets.toml

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[sources.plasmic_cms_source]
# place inside [sources.plasmic_source]
# CMS ID and token (public for read; secret for write/draft)
cms_id = "your_cms_id_here"
cms_token = "your_public_or_secret_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 Plasmic CMS 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 plasmic_cms_pipeline.py

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

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Pipeline plasmic_cms_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset plasmic_cms_data
The duckdb destination used duckdb:/plasmic_cms.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 plasmic_cms_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 cms_items and cms_row from the Plasmic CMS 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 plasmic_cms_source(cms_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://data.plasmic.app/api/v1",
            "auth": {
                "type": "api_key",
                "token": cms_token,
            },
        },
        "resources": [
            {"name": "cms_items", "endpoint": {"path": "cms/databases/{CMS_ID}/tables/{MODEL_ID}/query", "data_selector": "rows"}},
            {"name": "cms_row", "endpoint": {"path": "cms/rows/{ROW_ID}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="plasmic_cms_pipeline",
        destination="duckdb",
        dataset_name="plasmic_cms_data",
    )
    load_info = pipeline.run(plasmic_cms_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("plasmic_cms_pipeline").dataset()
sessions_df = data.cms_items.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM plasmic_cms_data.cms_items LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("plasmic_cms_pipeline").dataset()
data.cms_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 Plasmic CMS 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 x-plasmic-api-cms-tokens header is present and formatted as CMS_ID:TOKEN. Use the Public Token for read/query endpoints; use the Secret Token for write or draft access. If you get 401/403, verify CMS_ID, token, and that the token type (public vs secret) matches the operation.

Pagination and limits

The /query endpoint accepts q.limit (default 100) and q.offset for paging. If you need more records, page through using q.limit and q.offset. Large limits may be capped by the service—use repeated queries.

Draft content and previewing

To fetch draft data, pass draft=1 and use the secret token. Do not expose the secret token to clients. For previews in web apps, configure server-side fetch using the secret token and enable draft usage per model.

Rate limiting and errors

The docs do not publish explicit rate limits. On 429 or transient 5xx errors, implement exponential backoff and retries. For 4xx errors, validate request headers, JSON body, and query q param structure.

Caveats: The primary list response for model queries is in the JSON key rows. The /count endpoint returns {"count": }.

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