Facebook-workplace Python API Docs | dltHub

Workplace is a programmatic Graph API for accessing and managing Workplace (Meta) community objects (groups, posts, members, comments, reactions). The REST API base URL is https://graph.facebook.com and All requests require an access token (app or installation) and appsecret_proof for server‑side calls..

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-workplace data in under 10 minutes.

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What data can I load from Facebook-workplace?

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

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How do I authenticate with the Facebook-workplace API?

Workplace Graph API uses access tokens obtained via app installation or app access token; include access_token (as query parameter or Authorization: Bearer header) and, for server‑side calls, appsecret_proof and appsecret_time.

1. Get your credentials

1. Create an app or custom integration at https://developers.facebook.com/apps/.
2. Configure the Workplace product and set Valid OAuth Redirect URIs in the app settings.
3. Use the OAuth install flow: when an admin installs your app you receive a temporary code at your redirect URI.
4. Exchange the code for an access token via GET https://graph.facebook.com/v{version}/oauth/access_token?client_id={app-id}&redirect_uri={redirect-uri}&client_secret={app-secret}&code={code}.
5. Store the returned installation access_token securely (tokens for Workplace do not expire).
6. Optionally compute appsecret_proof = HMAC_SHA256(access_token, app_secret) and send with requests plus appsecret_time.

2. Add them to .dlt/secrets.toml

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[sources.facebook_workplace_source]
access_token = "your_installation_access_token_here"
app_id = "your_app_id_here"
app_secret = "your_app_secret_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-workplace 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_workplace_pipeline.py

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

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Pipeline facebook_workplace_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset facebook_workplace_data
The duckdb destination used duckdb:/facebook_workplace.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_workplace_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 groups and group_feed from the Facebook-workplace 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_workplace_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com",
            "auth": {
                "type": "api_key",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "groups", "endpoint": {"path": "{community_id}/groups", "data_selector": "data"}},
            {"name": "group_feed", "endpoint": {"path": "{group_id}/feed", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facebook_workplace_pipeline",
        destination="duckdb",
        dataset_name="facebook_workplace_data",
    )
    load_info = pipeline.run(facebook_workplace_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_workplace_pipeline").dataset()
sessions_df = data.group_feed.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM facebook_workplace_data.group_feed LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("facebook_workplace_pipeline").dataset()
data.group_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-workplace 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

401/400 responses occur when access_token is missing/invalid or appsecret_proof mismatches. Verify access_token from the install flow, ensure app_id/app_secret are correct, and compute appsecret_proof (HMAC‑SHA256) when required.

Rate limits and throttling

Graph API enforces rate limits per app and per endpoint. On heavy usage you may receive error responses indicating rate limiting; implement exponential backoff and respect the paging links rather than increasing page size.

Pagination quirks

Most list endpoints return paginated responses with the top‑level "data" array and a "paging" object containing cursors and "next"/"previous" URLs. Use cursor‑based or time‑based pagination as appropriate and stop when "next" is absent. Some edges use time‑based pagination (feed) and some may return empty pages with a "next" link—stop only when "next" is missing.

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