The Best opensource Alternatives in 2026

This SDK provides AI evaluations, prompt management, and observability for Python and TypeScript workflows.

Helicone provides open-source observability for LLM applications, with tools to monitor, evaluate, and experiment.

Sagify describes its project as a way to work with large language models and machine learning more easily.

Curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning.

ZenML provides a platform for machine learning pipelines and AI agents.

The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.

Aim tracks machine learning experiments and describes itself as an open-source experiment tracker.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.

Cog packages machine learning models into containers to support deployment across environments.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

ModelDB tracks machine learning model versions, metadata, and experiments.

Flexible, high-performance serving system for machine learning models.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

Xinference runs language, speech, and multimodal models through a unified inference API across cloud, on premises, or laptop setups.

Compose, test, and optimize prompts for language-model applications in Promptmetheus, an IDE supporting more than 100 models.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

The Datumbox framework is written in Java for developing machine learning and statistical applications.

Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.

The linfa project provides a machine learning framework written in Rust.

Hypernets offers a general automated machine learning framework for developing end to end AutoML toolkits in specific domains.

MLRun provides an open-source MLOps platform for managing machine learning applications, pipelines, and CI/CD delivery.

FLAML provides a fast Python library for automated machine learning and tuning.