LinkedIn Advertising Python API Docs | dltHub

LinkedIn Advertising API is a RESTful service that enables developers to manage ad accounts, campaigns, creatives, analytics, and targeting on LinkedIn. The REST API base URL is https://api.linkedin.com/v2 and All requests require a Bearer token obtained via OAuth 2.0 Authorization Code flow..

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 LinkedIn Advertising data in under 10 minutes.

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What data can I load from LinkedIn Advertising?

Here are some of the endpoints you can load from LinkedIn Advertising:

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How do I authenticate with the LinkedIn Advertising API?

Authentication uses OAuth 2.0 Authorization Code flow; each request must include an Authorization: Bearer <access_token> header (and optionally X-Restli-Protocol-Version: 2.0.0).

1. Get your credentials

1. Sign in to the LinkedIn Developer Portal at https://www.linkedin.com/developers/apps.
2. Click Create app and fill in required details.
3. In the app settings, add the Marketing (Advertising) product.
4. Submit the required access request for the Advertising API if prompted.
5. Note the Client ID and Client Secret displayed.
6. Implement the OAuth 2.0 Authorization Code flow using the redirect URI you configured to obtain an Authorization Code.
7. Exchange the Authorization Code for an access token (and refresh token) via LinkedIn's token endpoint.
8. Store the tokens securely for use in API requests.

2. Add them to .dlt/secrets.toml

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[sources.linkedin_ads_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
redirect_uri = "https://your.redirect.uri"
access_token = "your_access_token"
refresh_token = "your_refresh_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 LinkedIn Advertising 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 linkedin_ads_pipeline.py

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

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Pipeline linkedin_ads_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset linkedin_ads_data
The duckdb destination used duckdb:/linkedin_ads.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 linkedin_ads_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 ad_accounts and ad_campaigns from the LinkedIn Advertising 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 linkedin_ads_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.linkedin.com/v2",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "ad_accounts", "endpoint": {"path": "adAccounts", "data_selector": "elements"}},
            {"name": "ad_campaigns", "endpoint": {"path": "adCampaigns", "data_selector": "elements"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="linkedin_ads_pipeline",
        destination="duckdb",
        dataset_name="linkedin_ads_data",
    )
    load_info = pipeline.run(linkedin_ads_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("linkedin_ads_pipeline").dataset()
sessions_df = data.ad_campaigns.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM linkedin_ads_data.ad_campaigns LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("linkedin_ads_pipeline").dataset()
data.ad_campaigns.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 LinkedIn Advertising 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

401 Unauthorized

• Cause: Missing, expired, or invalid Bearer token, or required scopes not granted.
• Fix: Refresh the access token or re‑authorize the app.

403 Forbidden

• Cause: Application lacks the Advertising product access or the user does not have permission to the requested resource.
• Fix: Ensure the app is approved for the Marketing (Advertising) product and the user has appropriate rights.

429 Too Many Requests

• Cause: Rate limit exceeded (default: 100 requests per second per app).
• Fix: Implement exponential back‑off and respect the X-Restli-Header-RateLimit-Limit response header.

Pagination quirks

• The API uses start and count query parameters. The response contains a paging object with total, start, and count. Continue requesting until start + count >= total.
• Certain analytics endpoints restrict date ranges (e.g., max 92 days). Split larger queries into multiple calls.

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