---
id: kumoai
version: "2.22.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# kumoai — AI on the Modern Data Stack

License: permissive · Maintenance: active · Downloads: 164.3K/mo

## What it is and what it does

Kumo SDK is a Python library that acts as a client for the Kumo machine learning platform, which is positioned as an AI tool for the modern data stack. It lets you programmatically define, configure, and execute ML workflows that integrate with cloud data warehouses and other data infrastructure. The package bundles 12 runtime dependencies covering data manipulation (pandas, pyarrow), visualization (plotly), HTTP communication (requests, aiohttp, urllib3), async task management (tqdm), structured data validation (pydantic), and templating (jinja2), suggesting it handles data pipelines, API interactions, and model orchestration.

The SDK is actively maintained, supports Python 3.10 through 3.14, and carries an MIT license. It installs with low friction as a pure wheel. Optional database connectors for sqlite, duckdb, and snowflake can be added via extras, indicating that data source integration is a core use case. No known security vulnerabilities are recorded.

Use it for:

- Build and deploy ML pipelines that read from and write to cloud data warehouses
- Automate feature engineering and model training workflows on modern data infrastructure
- Integrate graph neural network models with data warehouse queries and transformations
- Orchestrate multi-step ML jobs with progress tracking and async execution

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Kumo SDK provides a Python interface to programmatically interact with the Kumo machine learning platform, enabling users to build and manage ML workflows on modern data infrastructure.

Yes, if you are building ML workflows on a modern data stack and need programmatic control over the Kumo platform. The low install friction, active maintenance, permissive license, and broad Python version support make it a practical choice. No security vulnerabilities are known. Verify that Kumo's capabilities align with your specific ML use case before committing to the platform.

## Install

pip install kumoai
uv add kumoai
poetry add kumoai

## Installing kumoai

Before you install: Low install friction with a pure-wheel distribution. Active maintenance status with a recent release cycle. Supports current Python versions (3.10–3.14) and declares 12 runtime dependencies including standard data-science libraries (pandas, pyarrow, plotly) and async/HTTP utilities.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most production and research contexts.

Quickstart:

pip install kumoai

import kumoai
# Interact with Kumo platform via SDK methods

Requires Python 3.10 or later. Optional database connectors (sqlite, duckdb, snowflake) can be installed separately via extras.

Verify before relying:

- Specific ML capabilities and supported model types beyond graph neural networks
- Performance characteristics and scalability limits for typical workloads
- Integration patterns with specific data warehouses or cloud providers

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 164.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

machine learning platform sdk, python ml workflow automation, data warehouse ml integration, graph neural networks python, cloud data stack ml tools, ml-platform-client, data-warehouse-integration

[View on SkillFed](https://skillfed.io/packages/kumoai) · [View on PyPI](https://pypi.org/project/kumoai/)