Open Food Facts Python API Docs | dltHub

Open Food Facts is a crowdsourced open database of food products (ingredients, nutrition, labels, images) and provides a public REST API to search, read and contribute product data. The REST API base URL is https://world.openfoodfacts.org and Read (GET) requests require no authentication but must use a custom User-Agent; write requests require a user account (session cookie or credentials)..

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 Open Food Facts data in under 10 minutes.

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What data can I load from Open Food Facts?

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

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How do I authenticate with the Open Food Facts API?

All GET/read operations do not require credentials; include a custom User-Agent header of the form 'AppName/Version (contact@example.com)'. Write operations require authentication: preferred method is login to obtain a session cookie (or include user_id and password parameters for POST/PUT). Staging uses HTTP Basic auth (username: off, password: off).

1. Get your credentials

1. Create an account at https://world.openfoodfacts.org (signup). 2) Use those user_id (username) and password for write operations or perform the login flow to obtain a session cookie. 3) For production write usage, fill the API usage form so the team can identify your app.

2. Add them to .dlt/secrets.toml

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[sources.open_food_facts_source]

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 Open Food Facts 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 open_food_facts_pipeline.py

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

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Pipeline open_food_facts_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset open_food_facts_data
The duckdb destination used duckdb:/open_food_facts.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 open_food_facts_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 product_by_barcode and search_products from the Open Food Facts 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 open_food_facts_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://world.openfoodfacts.org",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "product_by_barcode", "endpoint": {"path": "api/v2/product/{barcode}", "data_selector": "product"}},
            {"name": "search_products", "endpoint": {"path": "api/v2/search", "data_selector": "products"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="open_food_facts_pipeline",
        destination="duckdb",
        dataset_name="open_food_facts_data",
    )
    load_info = pipeline.run(open_food_facts_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("open_food_facts_pipeline").dataset()
sessions_df = data.product_by_barcode.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM open_food_facts_data.product_by_barcode LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("open_food_facts_pipeline").dataset()
data.product_by_barcode.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 Open Food Facts 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

Rate limits

Open Food Facts enforces rate limits: 100 req/min for product GETs, 10 req/min for search queries, and 2 req/min for facet queries. Exceeding limits can lead to IP bans; for bulk needs use the static data exports.

Authentication / Write failures

Read requests should return 200 without auth if User-Agent is set. Write requests without valid session or user credentials will fail; use the login flow to obtain a session cookie or include user_id/password for POSTs. Staging requires HTTP Basic auth (off:off).

Pagination and selectors

Search responses include top-level keys: 'count', 'page', 'page_count', 'page_size', and 'products' (array). Use page and page_size (or skip/limit variants) as supported query params. Single-product lookups return top-level 'code','product','status','status_verbose' — the actual product object is under the 'product' key.

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