Forter Python API Docs | dltHub

Forter is a fraud prevention platform that provides REST APIs for real‑time decisioning and order management. The REST API base URL is https://api.forter-secure.com and All requests require HTTP Basic authentication with the API key as username and an empty password, plus the X-Forter-SiteId header..

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 Forter data in under 10 minutes.

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What data can I load from Forter?

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

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How do I authenticate with the Forter API?

Forter uses HTTP Basic authentication where the API key is supplied as the username and the password is empty. Each request must include the X-Forter-SiteId header with your site ID and an Api-Version header.

1. Get your credentials

1. Log in to the Forter Portal (https://portal.forter.com). 2) Navigate to Integrations → API Credentials. 3) Create or view credentials for the desired environment (test or production). 4) Copy the API key (used as the HTTP Basic username) and the Site ID (used in the X‑Forter‑SiteId header).

2. Add them to .dlt/secrets.toml

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[sources.forter_order_v3_source]
api_key = "your_api_key_here"
site_id = "your_site_id_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 Forter 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 forter_order_v3_pipeline.py

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

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Pipeline forter_order_v3_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset forter_order_v3_data
The duckdb destination used duckdb:/forter_order_v3.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 forter_order_v3_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 order_status from the Forter 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 forter_order_v3_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.forter-secure.com",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "v3/orders/{id}"}},
            {"name": "order_status", "endpoint": {"path": "v3/order-status"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="forter_order_v3_pipeline",
        destination="duckdb",
        dataset_name="forter_order_v3_data",
    )
    load_info = pipeline.run(forter_order_v3_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("forter_order_v3_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM forter_order_v3_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("forter_order_v3_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 Forter 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 receive 401 Unauthorized, verify that you are using HTTP Basic authentication with the API key as the username, an empty password, and that the X‑Forter‑SiteId header is correct. Also ensure the Api‑Version header is set.

Rate limits and retries

Forter enforces rate limits (e.g., 10 requests per second on test sites). Exceeding the limit returns 429 Too Many Requests. For transient 5xx or 429 responses, retry once with exponential back‑off.

Request timeouts

The docs recommend a client‑side timeout of about 2 seconds. If a timeout occurs, retry the request once.

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