Alegra Python API Docs | dltHub

Alegra is an invoicing, accounting and inventory platform that exposes REST endpoints to manage invoices, contacts, items, companies, payments and related resources. The REST API base URL is https://api.alegra.com/api/v1 and All requests use HTTP Basic authentication (email + API token)..

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 Alegra data in under 10 minutes.

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What data can I load from Alegra?

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

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How do I authenticate with the Alegra API?

Alegra uses HTTP Basic auth. Add header 'Authorization: Basic <base64(email:api_token)>' and 'Accept: application/json'. Some sandbox/e-provider variants use different base URLs.

1. Get your credentials

1. Log in to your Alegra account.
2. Go to Settings or API section (API keys / Integrations).
3. Create or view your API token.
4. Use your account email as username and the API token as password when building Basic auth credentials.

2. Add them to .dlt/secrets.toml

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[sources.alegra_source]
username = "your_account_email@example.com"
password = "your_api_token_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 Alegra 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 alegra_pipeline.py

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

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Pipeline alegra_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset alegra_data
The duckdb destination used duckdb:/alegra.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 alegra_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 invoices and contacts from the Alegra 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 alegra_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.alegra.com/api/v1",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "invoices", "endpoint": {"path": "invoices"}},
            {"name": "contacts", "endpoint": {"path": "contacts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="alegra_pipeline",
        destination="duckdb",
        dataset_name="alegra_data",
    )
    load_info = pipeline.run(alegra_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("alegra_pipeline").dataset()
sessions_df = data.invoices.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM alegra_data.invoices LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("alegra_pipeline").dataset()
data.invoices.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 Alegra 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 401/403 responses, verify you are sending Authorization: Basic <base64(email:api_token)> with your account email as username and API token as password. Ensure token is active and not revoked.

Pagination and limits

List endpoints default to limit=30 and the API enforces a maximum of 30; requests with limit > 30 will return 404 or an error. Use start and limit to page through results; alternatively use metadata=true to receive {"metadata":..., "data":[...]}.

Rate limits and errors

Respect provider rate limits; on 429 back off and retry. Common API errors include 400/404 for bad parameters (e.g., limit>30), 401 for auth errors, 403 for permission issues, and 500 for server errors.

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