Best Open Source AI Platform Projects

Learn how to develop, deploy and iterate on production-grade ML applications.

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Making large AI models cheaper, faster and more accessible

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.

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

A WebGL accelerated JavaScript library for training and deploying ML models.

Ongoing research training transformer models at scale

🦉 Data Versioning and ML Experiments

Trigger.dev – build and deploy fully‑managed AI agents and workflows

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Build, Manage and Deploy AI/ML Systems

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Academic Research Skills for Claude Code: research → write → review → revise → finalize

Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry

⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports Cloud / Self-hosted use. Integrated with PyTorch / Transformers / verl / LLaMA Factory / ms-swift / Ultralytics / MMEngine / Keras etc.

🔮 Instill Core is a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications

High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8+ vector databases, and agent orchestration, all from your IDE. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.