Merge ATS Python API Docs | dltHub

Merge ATS is a unified API for Applicant Tracking Systems. The REST API base URL is https://api.merge.dev/api/ats/v1 and All requests require a Bearer token for authentication, along with an X-Account-Token header for linked account scoping..

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 Merge ATS data in under 10 minutes.

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What data can I load from Merge ATS?

Here are some of the endpoints you can load from Merge ATS:

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How do I authenticate with the Merge ATS API?

Authentication requires a Bearer token in the 'Authorization' header and an 'X-Account-Token' header for requests scoped to a linked account. The 'Accept: application/json' header is also recommended.

1. Get your credentials

Obtain a Merge API key from the Merge dashboard by navigating to 'Generate Key' or 'API keys'. For linked accounts, use the Link Token flow or Account tokens as described in the Merge documentation.

2. Add them to .dlt/secrets.toml

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[sources.merge_ats_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 Merge ATS 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 merge_ats_pipeline.py

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

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Pipeline merge_ats_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset merge_ats_data
The duckdb destination used duckdb:/merge_ats.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 merge_ats_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 candidates and applications from the Merge ATS 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 merge_ats_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.merge.dev/api/ats/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "candidates", "endpoint": {"path": "candidates", "data_selector": "results"}},
            {"name": "applications", "endpoint": {"path": "applications", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="merge_ats_pipeline",
        destination="duckdb",
        dataset_name="merge_ats_data",
    )
    load_info = pipeline.run(merge_ats_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("merge_ats_pipeline").dataset()
sessions_df = data.candidates.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM merge_ats_data.candidates LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("merge_ats_pipeline").dataset()
data.candidates.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 Merge ATS 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

Rate Limits

Merge ATS API requests are rate limited per Linked Account. The limits are:

• Launch: 100 requests per minute
• Professional: 400 requests per minute
• Enterprise: 600 requests per minute

Common Error Codes

• 401 Unauthorized: Returned for authentication failures.
• 429 Too Many Requests: Indicates that the rate limit has been exceeded.
• 404 Not Found: Returned when a requested resource does not exist.
• 400 Bad Request: Indicates an invalid request, often due to incorrect parameters or body format.

Linked Account Scoping

When making requests scoped to a linked account, ensure you include the X-Account-Token header.

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