Nusii Python API Docs | dltHub

Nusii is a contract and proposal management platform that provides a REST API for accessing proposals, clients, sections, line items, users, and webhook endpoints. The REST API base URL is https://app.nusii.com/api/v2/ and All requests require a token passed in the Authorization header as Token token=YOUR_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 Nusii data in under 10 minutes.

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What data can I load from Nusii?

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

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How do I authenticate with the Nusii API?

Authentication uses a token based scheme. Include the header Authorization: Token token=YOUR_API_KEY with each request.

1. Get your credentials

1. Log in to your Nusii account.
2. Click on your profile avatar and select Settings.
3. In the Settings menu choose API Keys (or similar).
4. Click Generate New Token and give it a name.
5. Copy the generated token and store it securely; it will be used as YOUR_API_KEY in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.nusii_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.

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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 Nusii 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 nusii_pipeline.py

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

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Pipeline nusii_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nusii_data
The duckdb destination used duckdb:/nusii.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 nusii_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 proposals and clients from the Nusii 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 nusii_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.nusii.com/api/v2/",
            "auth": {
                "type": "api_key",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "proposals", "endpoint": {"path": "proposals", "data_selector": "data"}},
            {"name": "clients", "endpoint": {"path": "clients", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nusii_pipeline",
        destination="duckdb",
        dataset_name="nusii_data",
    )
    load_info = pipeline.run(nusii_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("nusii_pipeline").dataset()
sessions_df = data.proposals.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM nusii_data.proposals LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("nusii_pipeline").dataset()
data.proposals.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 Nusii 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 Errors

• 401 Unauthorized – The token is missing, malformed, or invalid. Verify that the Authorization: Token token=YOUR_API_KEY header is present and the API key is correct.

Rate Limiting

• 429 Too Many Requests – The API permits 100 calls per 30 seconds. When this limit is exceeded, a 429 response is returned. Implement retry‑after logic based on the Retry-After header or pause for at least 30 seconds before retrying.

Pagination

• Collection endpoints return a top‑level data array and a meta object containing pagination fields (current-page, next-page, prev-page, total-pages, total-count). Use these fields to iterate through pages until next-page is absent.

Common HTTP Errors

• 400 Bad Request – Invalid parameters.
• 403 Forbidden – Insufficient permissions for the requested resource.
• 404 Not Found – Resource does not exist.
• 405 Method Not Allowed – Wrong HTTP method used.
• 406 Not Acceptable – Requested format not supported.
• 410 Gone – Resource has been permanently removed.
• 500 Internal Server Error / 503 Service Unavailable – Server‑side issues; retry after a short delay.

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