Plaid - Check Python API Docs | dltHub

Plaid's Check product provides consumer financial reports. It includes account balances, transaction insights, and spending categories. The API requires client ID and secret for authentication. The REST API base URL is https://production.plaid.com and All requests require client_id and secret (sent in body or 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 Plaid - Check data in under 10 minutes.

---

What data can I load from Plaid - Check?

Here are some of the endpoints you can load from Plaid - Check:

---

How do I authenticate with the Plaid - Check API?

Plaid authenticates requests using your client_id and secret. Include them in the JSON request body or set headers PLAID-CLIENT-ID and PLAID-SECRET; set Content-Type: application/json.

1. Get your credentials

1. Sign in to the Plaid Dashboard (https://plaid.com). 2) Create or select your Plaid application. 3) Navigate to the API keys / Keys section to find client_id and secret for Sandbox and Production. 4) Use sandbox keys for development and production keys for live requests.

2. Add them to .dlt/secrets.toml

Copy
[sources.plaid_check_source]
client_id = "your_client_id"
secret = "your_client_secret"

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 Plaid - Check 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 plaid_check_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline plaid_check_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 cra_check_report_base_report and cra_check_report_create from the Plaid - Check 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 plaid_check_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://production.plaid.com",
            "auth": {
                "type": "api_key",
                "secret": client_id,
            },
        },
        "resources": [
            {"name": "cra_check_report_base_report", "endpoint": {"path": "cra/check_report/base_report/get", "data_selector": "report"}},
            {"name": "cra_check_report_income_insights", "endpoint": {"path": "cra/check_report/income_insights/get", "data_selector": "report"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="plaid_check_pipeline",
        destination="duckdb",
        dataset_name="plaid_check_data",
    )
    load_info = pipeline.run(plaid_check_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("plaid_check_pipeline").dataset()
sessions_df = data.cra_check_report_base_report.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM plaid_check_data.cra_check_report_base_report LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("plaid_check_pipeline").dataset()
data.cra_check_report_base_report.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 Plaid - Check 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.

---

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