Facebook SDK Python API Docs | dltHub

Facebook SDK is a Python client library for interacting with the Facebook Graph API. The REST API base URL is https://graph.facebook.com and Requests require an access token (app, user, or page) sent as a query parameter or Authorization header..

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 SDK data in under 10 minutes.

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What data can I load from Facebook SDK?

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

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How do I authenticate with the Facebook SDK API?

The SDK expects an access_token string passed to facebook.GraphAPI(access_token=...), which the client adds as the access_token query parameter or via an Authorization: Bearer header. If an app_secret is provided, the SDK can generate an appsecret_proof for extra security.

1. Get your credentials

1. Create a Facebook App at https://developers.facebook.com/apps. 2) In the App Dashboard, note the App ID and App Secret. 3) Use Facebook Login or the Graph API Explorer to generate a user or page access token with the required permissions. 4) For long‑lived tokens, exchange the short‑lived token via the OAuth token endpoint as described in Facebook's login documentation.

2. Add them to .dlt/secrets.toml

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[sources.facebook_sdk_source]
access_token = "your_facebook_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 SDK 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_sdk_pipeline.py

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

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Pipeline facebook_sdk_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset facebook_sdk_data
The duckdb destination used duckdb:/facebook_sdk.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_sdk_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 get_object and get_connections from the Facebook SDK 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_sdk_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "connections", "endpoint": {"path": "{id}/{connection_name}", "data_selector": "data"}},
            {"name": "object", "endpoint": {"path": "{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facebook_sdk_pipeline",
        destination="duckdb",
        dataset_name="facebook_sdk_data",
    )
    load_info = pipeline.run(facebook_sdk_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_sdk_pipeline").dataset()
sessions_df = data.connections.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM facebook_sdk_data.connections LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("facebook_sdk_pipeline").dataset()
data.connections.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 SDK 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

Ensure your access_token is valid, not expired, and has the required scopes. If using app secret proof, provide the correct app_secret or let the SDK compute appsecret_proof. Typical error: (OAuthException) Invalid or expired token.

Rate limits and throttling

Facebook enforces app‑ and user‑level rate limits. On rate‑limit errors you receive error codes (e.g., 4, 613) and should implement exponential backoff and respect Retry‑After when provided.

Pagination and cursors

Many endpoints (connections, search) return paginated results in the "data" array plus a "paging" object with cursors (next, previous). Use the SDK's get_all_connections helper to iterate across pages or follow "paging.next" URLs until exhausted.

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