Load Instagram Graph data to DuckDB

Instagram Graph API allows businesses and creators to manage their presence and data on Instagram through authorized applications. Everything needed to build a working Instagram Graph → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Instagram Graph to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Instagram Graph to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Instagram Graph API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Instagram Graph API at a glance

These values come from the Instagram Graph API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Instagram Graph API?

Authentication requires a short or long-lived User Access Token. Requests are authenticated by passing the token as a Bearer token in the Authorization header (e.g., Authorization: Bearer <access_token>).

1. Get your credentials

To obtain credentials, first create a Meta App in the Meta App Dashboard at developers.facebook.com. Add the 'Instagram' product to your app. For apps using 'Instagram API setup with Instagram Login', navigate to the left-side menu under 'Instagram' > 'API setup with Instagram business login'. From there, you can configure business login settings or use the dashboard to generate a long-lived access token by clicking 'Generate token' next to the desired account. Note that for broader production access, you must implement a formal OAuth login flow (Business Login for Instagram or Facebook Login for Business) to obtain persistent access tokens.

2. Add them to .dlt/secrets.toml

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[sources.instagram_graph_source]
access_token = "your_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Instagram Graph data can I load into DuckDB?

These are the Instagram Graph endpoints dlt can load into DuckDB:

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How do I load only new Instagram Graph records?

Instagram Graph exposes after on {ig_user_id}/media, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "ig_media", "endpoint": {
    "path": "{ig_user_id}/media",
    "data_selector": "data",
    "incremental": {"cursor_path": "after", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Instagram Graph pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading me and media from the Instagram Graph API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def instagram_graph_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.instagram.com or https://graph.facebook.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "ig_media", "endpoint": {"path": "{ig_user_id}/media", "data_selector": "data"}},
            {"name": "ig_mentions", "endpoint": {"path": "{ig_user_id}/mentions", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_instagram_graph_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="instagram_graph_pipeline",
        destination="duckdb",
        dataset_name="instagram_graph_data",
    )
    load_info = pipeline.run(instagram_graph_source())
    print(load_info)


if __name__ == "__main__":
    load_instagram_graph_to_duckdb()

Run it with python instagram_graph_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Instagram Graph data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("instagram_graph_pipeline").dataset()
df = data.media.df()
print(df.head())

SQL:

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SELECT * FROM instagram_graph_data.media LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Instagram Graph to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Instagram Graph loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Instagram Graph data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack