Adept ID Python API Docs | dltHub

Adept ID is a skills- and jobs-focused API platform that parses, normalizes and searches job postings and educational program data. The REST API base URL is https://api.adept-id.com and all requests require credentials sent in request headers (API key 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 Adept ID data in under 10 minutes.

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What data can I load from Adept ID?

Here are some of the endpoints you can load from Adept ID:

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How do I authenticate with the Adept ID API?

Provide your API key in request headers for all requests (the docs show a 'Credentials / Header' area for endpoints). Use HTTPS. Example header: X-API-KEY: <your_api_key> or a provider-specified header per your account agreement.

1. Get your credentials

Contact your AdeptID Partner Success team or your AdeptID account admin to obtain API credentials (API key). For partner feed access (job_feed_id) contact Partner Success to get the job_feed_id and access rights.

2. Add them to .dlt/secrets.toml

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[sources.adept_id_source]
api_key = "your_api_key_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 Adept ID 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 adept_id_pipeline.py

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

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Pipeline adept_id_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset adept_id_data
The duckdb destination used duckdb:/adept_id.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 adept_id_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 program_of_study_search and job_feed_posting from the Adept ID 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 adept_id_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.adept-id.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "program_of_study_search", "endpoint": {"path": "program-of-study/v2"}},
            {"name": "job_feed_posting", "endpoint": {"path": "jobs/v2/feeds/{job_feed_id}/postings/{posting_id}", "data_selector": "job"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="adept_id_pipeline",
        destination="duckdb",
        dataset_name="adept_id_data",
    )
    load_info = pipeline.run(adept_id_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("adept_id_pipeline").dataset()
sessions_df = data.job_feed_posting.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM adept_id_data.job_feed_posting LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("adept_id_pipeline").dataset()
data.job_feed_posting.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 Adept ID 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 401/403 responses, verify that your API key is present in request headers and that you used the account-specific credentials provided by AdeptID Partner Success. For partner feed endpoints also confirm you have been granted access to the job_feed_id required by the endpoint.

Missing job_feed_id or feed access

Partner-supplied feed endpoints require an assigned job_feed_id. If you see 404 or access denied errors when calling /jobs/v2/feeds/{job_feed_id}/postings/{posting_id}, contact AdeptID Partner Success to obtain and enable your job_feed_id.

Rate limiting and retry

The docs do not show explicit rate limit headers. If you encounter 429 responses, back off and retry with exponential backoff.

Pagination quirks

Search endpoints in the docs are POST operations — inspect the search response body for the returned collection key (the docs expose examples in the interactive try-it responses). If a pagination token or page/limit fields are present in the search response, use those fields for subsequent requests.

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