Vtex Python API Docs | dltHub

Vtex is a commerce platform providing REST APIs to manage catalog, orders, master data, logistics, profiles and other commerce services. The REST API base URL is https://{accountName}.{environment} and All requests require VTEX account AppKey and AppToken sent as 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 Vtex data in under 10 minutes.

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What data can I load from Vtex?

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

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How do I authenticate with the Vtex API?

Authentication uses an AppKey/AppToken pair passed in headers: 'X-VTEX-API-AppKey' and 'X-VTEX-API-AppToken'. Some endpoints (VTEX ID) may return tokens for other flows. Include 'Accept: application/json' and appropriate Content-Type for body requests.

1. Get your credentials

1. Log into VTEX Admin for your account. 2) Go to License Manager / Account settings (or App Keys section). 3) Create a new AppKey/AppToken (name the key and set permissions/scopes for APIs needed). 4) Copy the AppKey and AppToken — store securely; AppToken is shown once. 5) Use these values as X-VTEX-API-AppKey and X-VTEX-API-AppToken in requests.

2. Add them to .dlt/secrets.toml

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[sources.vtex_source]
app_key = "your_app_key_here"
app_token = "your_app_token_here"
account_name = "your_account_name"
environment = "vtexcommercestable.com.br"

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 Vtex 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 vtex_pipeline.py

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

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Pipeline vtex_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset vtex_data
The duckdb destination used duckdb:/vtex.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 vtex_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 orders and master_data_records from the Vtex 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 vtex_source(app_key_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{accountName}.{environment}",
            "auth": {
                "type": "api_key",
                "app_token": app_key_token,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "api/oms/pvt/orders", "data_selector": "list"}},
            {"name": "master_data_records", "endpoint": {"path": "api/dataentities/{entity}/search"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vtex_pipeline",
        destination="duckdb",
        dataset_name="vtex_data",
    )
    load_info = pipeline.run(vtex_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("vtex_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM vtex_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("vtex_pipeline").dataset()
data.orders.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 Vtex 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 get 401/403, verify X-VTEX-API-AppKey and X-VTEX-API-AppToken headers are present and have correct scopes/permissions. AppToken is shown only once at creation.

Rate limits and throttling

VTEX APIs may enforce rate limits per account and per endpoint; on 429 responses, implement exponential backoff and retry.

Pagination quirks

Master Data search and many list endpoints return full arrays or use offset/limit query params. Orders API returns 'list' along with pagination metadata; use provided query params (page, per_page/size) where documented.

Data protection / PII endpoints

Profile System endpoints that return unmasked PII require additional reason header and may be restricted; ensure Data Protection Plus / VTEX Shield permissions and include 'reason' header when calling unmask endpoints.

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