Facebook-pixel Python API Docs | dltHub

Meta Pixel (Meta Pixel) is a tool used to track events on a website, and the Conversions API is a collection of Graph API endpoints and other features that help advertise across Meta technologies. The REST API base URL is https://graph.facebook.com/{API_VERSION} and All requests require an access token for 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 pip install "dlt[workspace]" and start loading Facebook-pixel data in under 10 minutes.

---

What data can I load from Facebook-pixel?

Here are some of the endpoints you can load from Facebook-pixel:

---

How do I authenticate with the Facebook-pixel API?

Authentication requires an access token, which should be attached as an access_token query parameter to the request.

1. Get your credentials

To obtain API credentials (Pixel ID and access token), you need access to Facebook Ads Manager. The Pixel base code or ID is available in Events Manager.

2. Add them to .dlt/secrets.toml

Copy
[sources.facebook_pixel_source]
access_token = "your_access_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Facebook-pixel 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
python facebook_pixel_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline facebook_pixel_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.

---

Python pipeline example

This example loads events and ad_sets from the Facebook-pixel 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 facebook_pixel_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com/{API_VERSION}",
            "auth": {
                "type": "api_key",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "events", "endpoint": {"path": "{pixel_id}/events", "data_selector": "data"}},
            {"name": "ad_sets", "endpoint": {"path": "act_{ad_account_id}/adsets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facebook_pixel_pipeline",
        destination="duckdb",
        dataset_name="facebook_pixel_data",
    )
    load_info = pipeline.run(facebook_pixel_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("facebook_pixel_pipeline").dataset()
sessions_df = data.events.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM facebook_pixel_data.events LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("facebook_pixel_pipeline").dataset()
data.events.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 Facebook-pixel 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.

---

Troubleshooting

Event Deduplication

When implementing both the Meta Pixel and the Conversions API, it is recommended to include eventID to deduplicate events. This helps prevent duplicate event reporting when the same event is sent via both methods.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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