Open Food Facts Robotoff Python API Docs | dltHub

Robotoff is an AI-powered service for Open Food Facts that generates, fetches, and manages product data insights and predictions. The REST API base URL is https://robotoff.openfoodfacts.org/api/v1 and some endpoints require HTTP Basic Authentication for registered users, while others are publicly accessible without authentication..

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 add "dlt[hub]" and start loading Open Food Facts Robotoff data in under 10 minutes.

---

What data can I load from Open Food Facts Robotoff?

Here are some of the endpoints you can load from Open Food Facts Robotoff:

---

How do I authenticate with the Open Food Facts Robotoff API?

Robotoff uses HTTP Basic Authentication for registered users. Provide an 'Authorization' header with the value 'Basic {base64_encoded_credentials}', where the string is 'username

' encoded in Base64.

1. Get your credentials

Open Food Facts Robotoff does not use a traditional API key dashboard for general access. To perform authenticated actions (like annotating insights), you must use your existing Open Food Facts user account credentials. To authenticate, generate a Base64-encoded string of 'username

' and include it in your request as an 'Authorization: Basic ' header. For administrative or internal batch job tasks, specific secure keys (e.g., 'BATCH_JOB_KEY') may be required, which are managed via environment variables in the service configuration rather than a user dashboard.

2. Add them to .dlt/secrets.toml

Copy
[sources.open_food_facts_robotoff_source]
# For user-based authentication
# off_username = "your_username"
# off_password = "your_password" # For batch job imports (only if managing a Robotoff instance)
# batch_job_key = "your_secure_batch_job_key"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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
uv run dlthub ai toolkit install rest-api-pipeline

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 Open Food Facts Robotoff 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
uv run python open_food_facts_robotoff_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub 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 /api/v1/insights and /api/v1/predict from the Open Food Facts Robotoff 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 open_food_facts_robotoff_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://robotoff.openfoodfacts.org/api/v1",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": auth},
        },
        "resources": [
            {"name": "questions", "endpoint": {"path": "questions/{{barcode}}", "data_selector": "questions"}},
            {"name": "insights", "endpoint": {"path": "insights", "data_selector": "insights"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="open_food_facts_robotoff_pipeline",
        destination="duckdb",
        dataset_name="open_food_facts_robotoff_data",
    )
    load_info = pipeline.run(open_food_facts_robotoff_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("open_food_facts_robotoff_pipeline").dataset()
sessions_df = data.questions.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM open_food_facts_robotoff_data.questions LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("open_food_facts_robotoff_pipeline").dataset()
data.questions.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 Open Food Facts Robotoff 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 harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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