The Stacks
Libraries, CLIs and infrastructure for building with AI.
Free to explore · curated by OneWave AI
215 developer tools projects in this wing of the Stacks. Review status is shown for every entry.
Review is in progress. 1,407 projects are queued and none has been approved yet. Everything unreviewed is discovery metadata imported from GitHub — listed, not vetted.
Ranked by total stars at the last daily refresh. This is not stars earned during the selected period. Star counts in the AI category are known to be inflated by fake-star campaigns, so activity and license are shown first.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
A high-throughput and memory-efficient inference and serving engine for LLMs
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
SGLang is a high-performance serving framework for large language models and multimodal models.
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
MNN: A blazing-fast, lightweight inference engine battle-tested by Alibaba, powering high-performance on-device LLMs and Edge AI.
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
Swap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.
⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)