☁️ Build multimodal AI applications with cloud-native stack

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.

A high-throughput and memory-efficient inference and serving engine for LLMs

Operating LLMs in production

Build Production-Grade AI Applications

Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)

ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.

🐢 The testing framework for ML models, from tabular to LLMs

本项目旨在分享大模型相关技术原理以及实战经验。

AGiXT is a dynamic AI Agent Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. Combining adaptive memory, smart features, and a versatile plugin system, AGiXT delivers efficient and comprehensive AI solutions.

An awesome & curated list of best LLMOps tools for developers

Turn expensive prompts into cheap fine-tuned models

🏕️ Reproducible development environment

Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines

⚡The Pure Python Framework for Web Apps, Meticulously Optimized for AI agents🤖. World's first AMS🥇

AI Observability & Evaluation - Evaluate, troubleshoot, and fine tune your LLM, CV, and NLP models in a notebook.

Your open-source LLM evaluation toolkit. Get scores for factual accuracy, context retrieval quality, tonality, and many more to understand the quality of your LLM applications

cube studio开源云原生一站式机器学习/深度学习AI平台，支持sso登录，多租户/多项目组，数据资产对接，notebook在线开发，拖拉拽任务流pipeline编排，多机多卡分布式算法训练，超参搜索，推理服务VGPU，多集群调度，边缘计算，serverless，标注平台，自动化标注，数据集管理，大模型一键微调，llmops，私有知识库，AI应用商店，支持模型一键开发/推理/微调，私有化部署，支持国产cpu/gpu/npu芯片，支持RDMA，支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/spark/ray/volcano分布式

Test your prompts, models, RAGs. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality.