LendAPI Python API Docs | dltHub

The LendAPI amortization endpoint calculates loan schedules, breaking payments into principal and interest, with APR and periodic payment details. The API is part of LendAPI's suite of lending solutions. For more details, visit the official documentation. The REST API base URL is https://app.lendapi.com/api/v1 and all requests require an API key provided in request headers (test vs live mode determined by the API key).

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 LendAPI data in under 10 minutes.

---

What data can I load from LendAPI?

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

---

How do I authenticate with the LendAPI API?

LendAPI uses API keys to authenticate requests. Include your API key in request headers as required by the developer docs (the API key determines test or live mode).

1. Get your credentials

1. Sign up / sign in at https://www.lendapi.com or the LendAPI dashboard.
2. Open the Developers / API keys section in your account.
3. Create a new API key (choose test or live).
4. Copy the API key and store it securely; use it in request headers when calling the API.

2. Add them to .dlt/secrets.toml

Copy
[sources.lendapi_source]
api_key = "your_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 LendAPI 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 lendapi_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline lendapi_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 application and pricing_engines from the LendAPI 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 lendapi_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.lendapi.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "application", "endpoint": {"path": "application/{id}"}},
            {"name": "pricing_engines", "endpoint": {"path": "pricing_engines"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="lendapi_pipeline",
        destination="duckdb",
        dataset_name="lendapi_data",
    )
    load_info = pipeline.run(lendapi_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("lendapi_pipeline").dataset()
sessions_df = data.application.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM lendapi_data.application LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("lendapi_pipeline").dataset()
data.application.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 LendAPI 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