Qonto Python API Docs | dltHub

Qonto is a business banking API platform that provides programmatic access to accounts, transactions, payments, cards, invoices and onboarding flows. The REST API base URL is Production: https://thirdparty.qonto.com/v2 (Sandbox: https://thirdparty-sandbox.staging.qonto.co/v2) and All requests require API authentication (API key or OAuth 2.0) sent from your backend..

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 Qonto data in under 10 minutes.

---

What data can I load from Qonto?

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

---

How do I authenticate with the Qonto API?

Qonto supports API key (secret) authentication for Business API integrations and OAuth 2.0 for delegated access; include credentials in HTTPS requests from your backend. All requests must be made over HTTPS; authentication is required for all endpoints.

1. Get your credentials

1. Sign in to Qonto Developer Portal (developers.qonto.com). 2) Create a new application/integration in the Developer Dashboard. 3) For API key access, generate the API credentials/secret in the app settings (copy securely). For OAuth, register redirect URI and obtain client_id and client_secret. 4) Use sandbox credentials in the sandbox base URL; use production credentials with the production base URL.

2. Add them to .dlt/secrets.toml

Copy
[sources.qonto_source]
api_key = "your_qonto_api_key_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 Qonto 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 qonto_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline qonto_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 accounts and transactions from the Qonto 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 qonto_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Production: https://thirdparty.qonto.com/v2 (Sandbox: https://thirdparty-sandbox.staging.qonto.co/v2)",
            "auth": {
                "type": "api_key (or oauth)",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "accounts", "data_selector": "accounts"}},
            {"name": "transactions", "endpoint": {"path": "transactions", "data_selector": "transactions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="qonto_pipeline",
        destination="duckdb",
        dataset_name="qonto_data",
    )
    load_info = pipeline.run(qonto_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("qonto_pipeline").dataset()
sessions_df = data.transactions.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM qonto_data.transactions LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("qonto_pipeline").dataset()
data.transactions.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 Qonto 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 failures

If you receive 401/403 responses ensure you are calling the correct base URL (sandbox vs production), that requests are made over HTTPS from your backend, and that the API key or OAuth token is valid and not expired. Regenerate credentials in the Developer Dashboard if necessary.

Rate limits

Qonto enforces rate limits. On 429 responses, back off and retry with exponential backoff. Consult the API response headers for rate limit details and remaining quota.

Pagination and large result sets

Most list endpoints use standard pagination (page/per_page or limit/offset). Inspect the endpoint reference response examples to confirm parameter names; follow pagination links or query parameters to iterate all pages.

Common errors

Responses are JSON for both success and errors. Expect 400 for validation errors, 401/403 for auth/permission issues, 404 for not found resources, and 429 for rate limiting. Handle error payloads according to the API Reference error example structures.

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