Adjust Python API Docs | dltHub

Adjust is a mobile attribution and analytics platform that provides a REST API for accessing campaign performance reports. The REST API base URL is https://api.adjust.com and All requests require HTTP Basic authentication using your Adjust token as the username..

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 Adjust data in under 10 minutes.

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What data can I load from Adjust?

Here are some of the endpoints you can load from Adjust:

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How do I authenticate with the Adjust API?

Authentication is performed via HTTP Basic. Provide your Adjust token as the username and leave the password empty (or any placeholder).

1. Get your credentials

1. Log in to your Adjust dashboard.
2. Navigate to Settings → API Tokens.
3. Click Create new token (or use an existing token).
4. Copy the generated token; this is the value you will use as the HTTP Basic username.
5. Store the token securely for use in dlt configuration.

2. Add them to .dlt/secrets.toml

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[sources.adjust_source]
api_token = "your_adjust_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 Adjust 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 adjust_pipeline.py

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

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Pipeline adjust_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset adjust_data
The duckdb destination used duckdb:/adjust.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 adjust_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 kpis and sessions from the Adjust 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 adjust_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.adjust.com",
            "auth": {
                "type": "http_basic",
                "username": api_token,
            },
        },
        "resources": [
            {"name": "kpis", "endpoint": {"path": "kpis/v1", "data_selector": "kpis"}},
            {"name": "sessions", "endpoint": {"path": "sessions/v1", "data_selector": "sessions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="adjust_pipeline",
        destination="duckdb",
        dataset_name="adjust_data",
    )
    load_info = pipeline.run(adjust_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("adjust_pipeline").dataset()
sessions_df = data.kpis.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM adjust_data.kpis LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("adjust_pipeline").dataset()
data.kpis.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 Adjust 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

Adjust returns 401 Unauthorized when the token is missing or invalid. The error body is JSON with fields error and message describing the problem.

Rate limiting

When you exceed the allowed request quota Adjust responds with 429 Too Many Requests. The response includes a Retry-After header indicating when you may retry.

Pagination quirks

The RS API uses limit and offset query parameters for pagination. If an invalid offset is supplied the API returns 400 Bad Request with an error message. Ensure limit does not exceed the maximum allowed value (usually 1000).

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