kumoai
AI on the Modern Data Stack
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Optional database connectors (sqlite, duckdb, snowflake) can be installed separately via extras.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most production and research contexts.
last release 2026-05-11 (95 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 164,331 downloads/mo, #10,552 on PyPI
Alternatives
Verify before relying
pip install kumoai
import kumoai
# Interact with Kumo platform via SDK methods- 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
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 on this page. Verify before relying on it.
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
kumoai on PyPI
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.
Requires Python 3.10 or later. Optional database connectors (sqlite, duckdb, snowflake) can be installed separately via extras.
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 methodsVerify 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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagespandaspyarrowrequestsurllib3plotlytyping_extensionskumo-apitqdmaiohttppydanticrichjinja2 |
| Maintenance | Actively maintained 95 days since the last release |
| First released | |
| Downloads | 164,331 / month, #10,552 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: kumoai-2.22.0-py3-none-any.whl
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See also kumo-api · snowflake-ml-python · tinker · kubeflow · hopsworks · kfp · azure-ai-ml · azureml-sdk · datarobot · kfp-server-api