Facturama Python API Docs | dltHub

Facturama is a Mexican electronic invoicing (CFDI) REST API service. The REST API base URL is https://apisandbox.facturama.mx and All requests require a Basic authentication token in the Authorization 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 Facturama data in under 10 minutes.

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What data can I load from Facturama?

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

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How do I authenticate with the Facturama API?

Provide a Base64‑encoded "username:password" string after the word Basic in the Authorization header of every request.

1. Get your credentials

1. Log in to the Facturama sandbox portal.
2. Navigate to the "API" or "Developers" section.
3. Copy the provided "Username" and "Password" (or generate new ones if needed).
4. Encode "username:password" in Base64 to create the token used in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.facturama_source]
username = "your_username"
password = "your_password"

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 Facturama 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 facturama_pipeline.py

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

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Pipeline facturama_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset facturama_data
The duckdb destination used duckdb:/facturama.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 facturama_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 clients and products from the Facturama 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 facturama_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://apisandbox.facturama.mx",
            "auth": {
                "type": "http_basic",
                "token": username,
            },
        },
        "resources": [
            {"name": "clients", "endpoint": {"path": "api/clients"}},
            {"name": "products", "endpoint": {"path": "api/products"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facturama_pipeline",
        destination="duckdb",
        dataset_name="facturama_data",
    )
    load_info = pipeline.run(facturama_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("facturama_pipeline").dataset()
sessions_df = data.clients.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM facturama_data.clients LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("facturama_pipeline").dataset()
data.clients.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 Facturama 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 – Occurs when the Authorization header is missing or the Base64 token is malformed. Ensure the username and password are correct and encoded as Basic <base64>.

Pagination Limits

• The API returns a maximum of 100 records per page. Use the page (or offset/limit for logs) query parameters to iterate through all data.
• Example: GET https://apisandbox.facturama.mx/cfdi?type=issued&page=0 returns the first 100 CFDIs.

Rate Limiting / Throttling

• The documentation does not specify a strict rate limit, but excessive rapid calls may result in temporary blocking. Implement exponential back‑off on HTTP 429 responses.

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