Facebook-messenger Python API Docs | dltHub

Facebook Messenger Platform is a set of APIs that enable sending and receiving messages, managing conversations, and configuring page messenger settings. The REST API base URL is https://graph.facebook.com and All requests require a Page Access Token passed as the access_token query 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 pip install "dlt[workspace]" and start loading Facebook-messenger data in under 10 minutes.

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What data can I load from Facebook-messenger?

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

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How do I authenticate with the Facebook-messenger API?

Authentication is performed using a Page Access Token supplied as the access_token query parameter on every request.

1. Get your credentials

1. Log in to the Facebook Developer portal (https://developers.facebook.com).
2. Select your app or create a new one.
3. In the left menu, go to Products → Facebook Login → Settings and ensure the pages_messaging permission is approved.
4. Navigate to Tools → Graph API Explorer.
5. Choose your app, select the Page you manage, and click Generate Access Token with the pages_messaging permission.
6. Copy the generated token; it is the Page Access Token required for the Messenger API.
7. (Optional) Extend the short‑lived token to a long‑lived token via the Access Token Debugger.

2. Add them to .dlt/secrets.toml

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[sources.facebook_messenger_source]
access_token = "your_page_access_token"

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-messenger 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_messenger_pipeline.py

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

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Pipeline facebook_messenger_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset facebook_messenger_data
The duckdb destination used duckdb:/facebook_messenger.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_messenger_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 conversations and messages from the Facebook-messenger 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_messenger_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com",
            "auth": {
                "type": "api_key",
                "api_key": access_token,
            },
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "{page-id}/conversations", "data_selector": "data"}},
            {"name": "messages", "endpoint": {"path": "{conversation-id}", "data_selector": "messages.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facebook_messenger_pipeline",
        destination="duckdb",
        dataset_name="facebook_messenger_data",
    )
    load_info = pipeline.run(facebook_messenger_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_messenger_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM facebook_messenger_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("facebook_messenger_pipeline").dataset()
data.conversations.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-messenger 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 Errors

• Error 190: Invalid OAuth access token – ensure the Page Access Token is correct and not expired.
• Error 200: Permissions error – the token must have the pages_messaging permission.

Rate Limits

• The Messenger Platform enforces a limit of 20 messages per second per Page. Exceeding this returns error code 613 (Rate limit exceeded). Implement exponential backoff.

Pagination

• List endpoints (e.g., conversations, messages) use cursor‑based pagination. Include the after cursor from the response paging.next URL to retrieve the next page of results.

General API Errors

• Error 100: Parameter validation error – verify required query parameters like access_token are present.
• Error 4: Application request limit reached – spread requests over time or request higher limits via Facebook.

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