Bullhorn Invenias Python API Docs | dltHub

Invenias is a REST API exposing Bullhorn Invenias CRM and ATS data for programmatic create/read/update/delete access. The REST API base URL is https://{subdomain}.invenias.com/api/v1 and All requests require an OAuth2 access token (Bearer) in the Authorization header..

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 Bullhorn Invenias data in under 10 minutes.

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What data can I load from Bullhorn Invenias?

Here are some of the endpoints you can load from Bullhorn Invenias:

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How do I authenticate with the Bullhorn Invenias API?

The Invenias API uses OAuth 2.0 (Resource Owner Password Credentials and Authorization Code flows supported). Obtain client_id and client_secret for a third-party application, exchange credentials (and user credentials for ROPC or authorization code) at the identity/token endpoints to receive an access_token; send requests with Authorization: Bearer {access_token}. Access tokens expire (24h); refresh tokens are available for the authorization code flow.

1. Get your credentials

1. In Invenias admin, register a third-party application via POST /api/v1/thirdpartyapplications in the Swagger interface (or Admin UI) to obtain client_id and client_secret. 2) For ROPC flow create/choose a licensed Invenias user (recommend username-api@...) in System Administrator group. 3) Use client_id, client_secret and username/password against the token endpoint (identity/connect/token) with grant_type=password to obtain access_token. 4) For authorization-code flow, register redirect URI, follow /identity/connect/authorize then exchange code at /identity/connect/token to receive access_token and refresh_token.

2. Add them to .dlt/secrets.toml

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[sources.bullhorn_invenias_source]
token = "your_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 Bullhorn Invenias 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 bullhorn_invenias_pipeline.py

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

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Pipeline bullhorn_invenias_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bullhorn_invenias_data
The duckdb destination used duckdb:/bullhorn_invenias.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 bullhorn_invenias_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 people_list and companies_list from the Bullhorn Invenias 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 bullhorn_invenias_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.invenias.com/api/v1",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "people_list", "endpoint": {"path": "api/v1/people/list", "data_selector": "Items"}},
            {"name": "companies_list", "endpoint": {"path": "api/v1/companies/list", "data_selector": "Items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bullhorn_invenias_pipeline",
        destination="duckdb",
        dataset_name="bullhorn_invenias_data",
    )
    load_info = pipeline.run(bullhorn_invenias_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("bullhorn_invenias_pipeline").dataset()
sessions_df = data.people_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bullhorn_invenias_data.people_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bullhorn_invenias_pipeline").dataset()
data.people_list.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 Bullhorn Invenias 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 responses indicate missing/invalid credentials or expired tokens. Ensure you exchange client_id/client_secret and (for ROPC) user credentials for an access_token at the identity/token endpoint and send Authorization: Bearer {token}. Tokens expire (~24h); refresh via refresh_token (authorization code flow) or re-authenticate for ROPC.

Rate limits (429)

The API uses a fixed-window rate limit: 3000 calls per 5-minute window. Monitor X-Request-Quota-Remaining response header. If you receive 429, back off until the window resets. Excessive 429s (many within 5 minutes) can cause the account to be disabled.

Pagination and list endpoints

List endpoints support PageSize (max 1000), PageIndex and UsePaging and return results in an Items array. For large datasets iterate PageIndex to retrieve all rows. POST body list endpoints accept Select/Filter/Sort to limit columns and improve performance.

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