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DataRobot API resources > API reference documentation

API reference documentation

The table below outlines the reference documentation available for DataRobot's API, SDKs, and code-first tools.

Resource Description
REST API The DataRobot REST API provides a programmatic alternative to the UI for creating and managing DataRobot assets. It allows you to automate processes and iterate more quickly, and lets you use DataRobot with scripted control. The API provides an intuitive modeling and prediction interface.
OpenAPI specification Reference the OpenAPI specification for the DataRobot REST API, which helps automate the generation of a client for languages that DataRobot doesn't directly support. It also assists with the design, implementation, and testing integration with DataRobot's REST API using a variety of automated OpenAPI-compatible tools. Note that accessing the OpenAPI spec requires you to be logged into the DataRobot application.
Python API client Installation, configuration, and usage guidelines for working with the Python client library.
R client Installation, configuration, and reference documentation for working with the R client library.
Blueprint workshop Construct and modify DataRobot blueprints and their tasks using a programmatic interface.
DataRobot Model Metrics The DataRobot Model Metrics library provides the tools necessary to compute model metrics over time and produce aggregated metrics.
Prediction API Generate predictions with a deployment by submitting JSON or CSV input data via a POST request.
Batch Prediction API Score large datasets with flexible options for intake and output using the prediction servers you have deployed via the Batch Prediction API.
Code-first tools Review the various programmatic tools DataRobot has to offer in addition to the APIs.
API changelogs Changelogs contain curated, ordered lists of notable changes for each versioned release for DataRobot's SDKs and REST API.
Declarative API A Terraform-native declarative API used to programmatically provision DataRobot entities such as models, deployments, applications, and more.
Troubleshoot the Python client Outlines cases that can cause issues with using the Python client and provides known fixes.
Updated February 27, 2025