datarobot-genai 0.15.17

Generic helpers for GenAI

DataRobot GenAI Library

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A toolkit for building agents on DataRobot.

• Unified LLM layer (DataRobot-compatible)—you use one get_llm() entry point per integration (LangGraph, LlamaIndex, CrewAI, NAT), all backed by the same LiteLLM-based routing to the DataRobot LLM Gateway, LLM deployments, NIM, or external providers.
• Library of agentic tools and DataRobot-compatible MCP server—use drtools and drmcp to give your agent first-class capabilities to interact with the world.
• AG-UI integration—your agents expose a standard AG-UI event stream (RunAgentInput in, lifecycle + text + tool-call events out), so your UIs and the DataRobot platform render runs consistently without bespoke adapters per framework.
• Multi-agent systems out of the box—you get first-class patterns for planner/writer crews, LangGraph multi-node graphs, and LlamaIndex AgentWorkflow handoffs; wrap them with one helper and keep the same streaming contract.
• Orchestration—you build agents from universal pieces in the low-code workflow.yaml interface. Combine and reuse LLMs, tools, agents, and evaluators. The design stays compatible with and draws inspiration from NeMo Agentic Toolkit.
• Serving and evaluating with DRAgent—you run a front-end server to plug your agent into a real-world application. DRAgent supports distributed tracing, generation and evaluation endpoints, async generations, and two-way communication over WebSockets.

Use

Installation

• You need Python 3.11–3.13.
• Install the extra that matches the framework you use:

pip install "datarobot-genai[crewai]"
pip install "datarobot-genai[langgraph]"
pip install "datarobot-genai[llamaindex]"
pip install "datarobot-genai[nat]"

You can also install:

• datarobot-genai[dragent]—serve and orchestrate your agent with DRAgent.
• datarobot-genai[drtools]—use the standard library of agentic tools DataRobot provides.
• datarobot-genai[drmcp]—host a custom MCP server in DataRobot.

Credentials

You need a DataRobot account to use DataRobot-backed features. Export these environment variables:

# Set your DataRobot API token (replace the placeholder).
export DATAROBOT_API_TOKEN=YOUR_DATAROBOT_API_TOKEN
export DATAROBOT_ENDPOINT=https://app.datarobot.com/api/v2

Standalone end-to-end examples

Follow quickstart.ipynb to walk through an experience of setting a LangGraph agent with DataRobot:

• LLM Gateway
• drtools
• Prompt Management
• Conversion to DataRobot agent format
• Running the agent with an AG-UI interface.

In-depth documentation

See docs/README.md for guides on every framework and feature in datarobot-genai.

Develop

You need Python 3.11–3.13, uv, Task CLI, and pre-commit.

uv sync --all-extras --dev
pre-commit install
task test

Semantic versioning

When you change the library, bump the patch version and add an entry to CHANGELOG.md. When you introduce a backward-incompatible change, bump the minor version.

TestPyPI

Comment /build on your PR to build and publish a dev version of the package to TestPyPI.

Publishing

• Same-repo PRs—comment /build on your PR to publish dev builds to TestPyPI (.devN).
• Merge to main—the release flow creates tag v{version} and publishes to PyPI automatically.
• Version tags—when you push a v* tag, PyPI publish runs as well.
• Local release—optionally run task release:tag-and-push to create and push v{version} from your machine.

Links

• Repository—source and issues.
• PyPI—released packages.
• TestPyPI—dev builds.

License

Apache-2.0—see LICENSE.