Facebook Pages Python API Docs | dltHub

Facebook Pages API is a collection of Graph API endpoints used for creating and managing a Page's settings, content, and engagement. The REST API base URL is https://graph.facebook.com and all requests require an access token passed as a parameter.

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 Facebook Pages data in under 10 minutes.

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What data can I load from Facebook Pages?

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

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How do I authenticate with the Facebook Pages API?

Authentication is handled by passing a Page Access Token as an 'access_token' parameter in the query string or request body. No specific HTTP header is required for the token itself.

1. Get your credentials

1. Create a Meta App at the Meta for Developers dashboard. 2. Configure Facebook Login for Business to request necessary Page permissions (e.g., pages_manage_posts, pages_manage_metadata, pages_read_engagement). 3. Use the Graph API Explorer or your OAuth flow to generate a User Access Token. 4. Query the /me/accounts endpoint using the User Access Token to retrieve a list of pages you manage and their respective Page Access Tokens. 5. Capture the required Page Access Token for your target Page.

2. Add them to .dlt/secrets.toml

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[sources.facebook_pages_source]
access_token = "your_page_access_token_here"
# If using a specific resource requiring account ID
account_id = "your_page_id_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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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:

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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 Facebook Pages 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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uv run python facebook_pages_pipeline.py

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

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Pipeline facebook_pages_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset facebook_pages_data
The duckdb destination used duckdb:/facebook_pages.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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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.

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Python pipeline example

This example loads /me/accounts and /{page-id}/feed from the Facebook Pages 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_pages_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "page_feed", "endpoint": {"path": "{page-id}/feed", "data_selector": "data"}},
            {"name": "page_comments", "endpoint": {"path": "{object-id}/comments", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facebook_pages_pipeline",
        destination="duckdb",
        dataset_name="facebook_pages_data",
    )
    load_info = pipeline.run(facebook_pages_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_pages_pipeline").dataset()
sessions_df = data.page_feed.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM facebook_pages_data.page_feed LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("facebook_pages_pipeline").dataset()
data.page_feed.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 Pages 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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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.

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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