Facebook-lead-ads Python API Docs | dltHub

Facebook Lead Ads is a service that lets advertisers create lead generation forms on Facebook and Instagram and retrieve leads via the Graph API or Webhooks. The REST API base URL is https://graph.facebook.com and All requests require an access token (Page or User); Page access tokens are recommended for lead retrieval..

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-lead-ads data in under 10 minutes.

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What data can I load from Facebook-lead-ads?

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

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How do I authenticate with the Facebook-lead-ads API?

The Graph API uses OAuth access tokens. Include a Page or User access token either as the access_token query parameter or in the Authorization: Bearer header. Use Page access tokens and the leads_retrieval permission for full lead data and ads_management/pages permissions for ad-level fields.

1. Get your credentials

1. Create a Meta App in App Dashboard. 2) Add Facebook Login and Webhooks as needed. 3) Request necessary permissions (leads_retrieval, pages_read_engagement, pages_show_list, pages_manage_ads, ads_management) and submit for App Review. 4) Have a Page admin log in to your app and request a Page access token (exchange short-lived token for long-lived and generate a Page token if needed). 5) Install the app on the Page and, if using webhooks, subscribe the Page to the leadgen field.

2. Add them to .dlt/secrets.toml

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[sources.facebook_lead_ads_source]
access_token = "your_page_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.

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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 Facebook-lead-ads 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 facebook_lead_ads_pipeline.py

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

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Pipeline facebook_lead_ads_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset facebook_lead_ads_data
The duckdb destination used duckdb:/facebook_lead_ads.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 facebook_lead_ads_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 leadgen_forms and leads from the Facebook-lead-ads 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 facebook_lead_ads_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "leadgen_forms", "endpoint": {"path": "{page_id}/leadgen_forms", "data_selector": "data"}},
            {"name": "leads", "endpoint": {"path": "{form_id}/leads", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facebook_lead_ads_pipeline",
        destination="duckdb",
        dataset_name="facebook_lead_ads_data",
    )
    load_info = pipeline.run(facebook_lead_ads_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("facebook_lead_ads_pipeline").dataset()
sessions_df = data.leads.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM facebook_lead_ads_data.leads LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("facebook_lead_ads_pipeline").dataset()
data.leads.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 Facebook-lead-ads 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 your access token is missing, invalid, expired or lacks required scopes (for example leads_retrieval, pages_read_engagement, ads_management), the Graph API returns OAuth errors. Use a Page access token requested by a Page admin who can advertise on the Page for lead retrieval; exchange short-lived tokens for long-lived tokens and generate a long-lived Page token.

Rate limits

Lead retrieval rate limit: 200 * 24 * (number of leads created in past 90 days) calls per 24-hour period for a Page. Exceeding this returns rate-limit errors; batch or pace requests and prefer Webhooks for real-time ingestion.

Pagination and cursors

List responses (lead lists and form lists) use the top-level "data" array and a "paging" object with cursors (before/after). Use the paging.cursors.after value to page forward or the provided next URL when present.

Webhooks and real-time reliability

Subscribe the Page to the leadgen field on the Webhooks product and install the app on the Page. Webhook notifications include an entry array with changes and leadgen_id values; use leadgen_id to call GET /<LEAD_ID>?fields=... to fetch lead details. Monitor RTU delivery status in the dashboard and handle retry/backoff for failed deliveries.

Common API errors:

• OAuthException: Invalid or expired token or missing permissions.
• (#4) Application request limit reached: rate limiting.
• (#1) Unknown error: transient Graph API errors; retry with backoff.
• 10, 2: Permission or server errors related to ads/lead access.

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