Vercel Python API Docs | dltHub

Vercel is a cloud platform for deploying front‑end frameworks and static sites, providing a REST API to manage projects, deployments, and integrations. The REST API base URL is https://api.vercel.com and All requests require a Bearer token for authentication.

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 Vercel data in under 10 minutes.

---

What data can I load from Vercel?

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

---

How do I authenticate with the Vercel API?

Provide the access token in the request header Authorization: Bearer <token>. The token is issued when the integration is installed via the Upsert Installation API.

1. Get your credentials

1. Log in to the Vercel dashboard.
2. Navigate to Integrations → Create Integration.
3. During the installation flow, Vercel calls the Upsert Installation API (POST https://api.vercel.com/v2/oauth/access_token).
4. The response contains a credentials object with an access_token.
5. Copy the access_token; it will be used as the Bearer token for all API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.vercel_source]
token = "your_access_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Vercel 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:

Copy
python vercel_pipeline.py

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

Copy
Pipeline vercel_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset vercel_data
The duckdb destination used duckdb:/vercel.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline vercel_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.

---

Python pipeline example

This example loads projects and deployments from the Vercel API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def vercel_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.vercel.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "v1/projects", "data_selector": "projects"}},
            {"name": "deployments", "endpoint": {"path": "v4/deployments", "data_selector": "deployments"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vercel_pipeline",
        destination="duckdb",
        dataset_name="vercel_data",
    )
    load_info = pipeline.run(vercel_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.

---

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):

Copy
import dlt

data = dlt.pipeline("vercel_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM vercel_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("vercel_pipeline").dataset()
data.projects.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Vercel 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.

---

Troubleshooting

Authentication Errors

• 403 Forbidden – Returned when the Authorization header is missing, the token is invalid, or required scopes are absent. Ensure the Bearer token is correct and includes the necessary scopes.

Rate Limiting

• Vercel enforces rate limits per token. If a 429 Too Many Requests response is received, back‑off and retry after the Retry-After header duration.

Pagination

• List endpoints return paginated results using cursor query parameters. Include cursor from the previous response to retrieve the next page.

Team ID Requirement

• For resources scoped to a team, the teamId query parameter must be provided. Omitting it may result in 403 errors as noted in the 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.

---

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.

Copy
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