The Best Starwhale Alternatives in 2026

Future AGI evaluates and observes AI and agent applications with tracing, simulations, datasets, and guardrails.

Talos provides tools for running hyperparameter experiments with TensorFlow and Keras.

This repository contains machine learning code examples that use Comet.ml.

FastChat offers an open platform for training, serving, and evaluating large language models, and hosts release repositories for Vicuna and Chatbot Arena.

The Hydrosphere repository describes an MLOps platform for machine learning operations.

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

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

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.

DJL is a Java framework for running deep learning models across different engines.

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

Manifest connects AI agents and tools to multiple model providers through an LLM gateway.

Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.

MMDeploy provides a model deployment framework from the OpenMMLab project.

Kitaru provides agent traces that teams can run and inspect during application workflows.

OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others).

File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.

Latitude provides observability for AI agents and helps teams trace failures and verify fixes.

Marquez collects, aggregates, and visualizes metadata about a data ecosystem.

Harbor sets up a preconfigured LLM stack with one command and includes hundreds of services to explore.

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