Linkedin-lead-forms Python API Docs | dltHub

LinkedIn Lead Gen Forms is a REST API to create, retrieve, and manage lead generation forms and fetch their responses for LinkedIn Ads (sponsored) and lead-sync integrations. The REST API base URL is https://api.linkedin.com/rest and All requests require an OAuth 2.0 Bearer token with LinkedIn Marketing scopes..

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-lead-forms data in under 10 minutes.

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What data can I load from Linkedin-lead-forms?

Here are some of the endpoints you can load from Linkedin-lead-forms:

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How do I authenticate with the Linkedin-lead-forms API?

LinkedIn uses OAuth 2.0; include Authorization: Bearer {ACCESS_TOKEN} header and required marketing headers: Linkedin-Version (YYYYMM) and X-Restli-Protocol-Version: 2.0.0.

1. Get your credentials

1. Create or log into LinkedIn Developer account.
2. Create an app in the LinkedIn Developer Portal and note Client ID/Secret.
3. Request Marketing/Advertising API access and request scopes including r_marketing_leadgen_automation and rw_ads; submit app access review if required.
4. Implement OAuth 2.0 authorization code flow to exchange code for a Bearer token.
5. Use the token in Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.linkedin_lead_forms_source]
access_token = "your_oauth_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 Linkedin-lead-forms 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_lead_forms_pipeline.py

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

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Pipeline linkedin_lead_forms_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset linkedin_lead_forms_data
The duckdb destination used duckdb:/linkedin_lead_forms.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_lead_forms_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 lead_forms and lead_form_responses from the Linkedin-lead-forms 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_lead_forms_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.linkedin.com/rest",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "lead_forms", "endpoint": {"path": "leadForms", "data_selector": "elements"}},
            {"name": "lead_form_responses", "endpoint": {"path": "leadFormResponses", "data_selector": "elements"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="linkedin_lead_forms_pipeline",
        destination="duckdb",
        dataset_name="linkedin_lead_forms_data",
    )
    load_info = pipeline.run(linkedin_lead_forms_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_lead_forms_pipeline").dataset()
sessions_df = data.lead_form_responses.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM linkedin_lead_forms_data.lead_form_responses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("linkedin_lead_forms_pipeline").dataset()
data.lead_form_responses.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-lead-forms 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

If you receive 401 Unauthorized or "Member doesn't have lead access permission", verify Bearer token validity, required scopes (r_marketing_leadgen_automation, rw_ads), and that the token's member has access to the owner (organization or sponsoredAccount) referenced.

Rate limits and versioning

LinkedIn Marketing APIs are versioned (use Linkedin-Version header YYYYMM). Respect rate limits returned in response headers; migrate to the latest supported Marketing version per LinkedIn migration guides to avoid deprecation.

Pagination and request params

Lead Lists support pagination via count and start parameters; batch GET endpoints accept ids=List(...). For owner‑scoped queries include q=owner and owner=(organization:urn:li:organization:123) (URNs must be URL‑encoded). For lead responses include leadType (e.g., (leadType:SPONSORED)).

common_api_errors: 401 Member is not authorized to fetch LeadGenForms under a SponsoredAccount/Organization; 401 The member doesn't have lead access permission; 400 Please specify a concrete LeadType and Owner for your request; 400 Failed to generate Lead Form identifier; 204 No Content for successful edits/deletes.

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