AIBrix is an open-source initiative providing essential building blocks for scalable GenAI inference infrastructure. It delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored to enterprise needs. Key features include High-Density LoRA Management, LLM Gateway and Routing, LLM App-Tailored Autoscaler, Unified AI Runtime, Distributed Inference, Distributed KV Cache, Cost-efficient Heterogeneous Serving, and GPU Hardware Failure Detection.

An open-source AI assistant for taking meeting notes that captures live meeting audio, transcribes it in real-time, and generates summaries while ensuring user privacy. Perfect for teams to focus on discussions while automatically capturing and organizing meeting content without external servers or complex infrastructure. Features include modern UI, real-time audio capture, speaker diarization, local processing for privacy, and more. The tool also offers a Rust-based implementation for better performance and native integration, with features like live transcription, speaker diarization, and a rich text editor for notes. Future plans include database connection for saving meeting minutes, improving summarization quality, and adding download options for meeting transcriptions and summaries. The backend supports multiple LLM providers through a unified interface, with configurations for Anthropic, Groq, and Ollama models. System architecture includes core components like audio capture service, transcription engine, LLM orchestrator, data services, and API layer. Prerequisites for setup include Node.js, Python, FFmpeg, and Rust. Development guidelines emphasize project structure, testing, documentation, type hints, and ESLint configuration. Contributions are welcome under the MIT License.

Simplifine is an open-source library designed for easy LLM finetuning, enabling users to perform tasks such as supervised fine tuning, question-answer finetuning, contrastive loss for embedding tasks, multi-label classification finetuning, and more. It provides features like WandB logging, in-built evaluation tools, automated finetuning parameters, and state-of-the-art optimization techniques. The library offers bug fixes, new features, and documentation updates in its latest version. Users can install Simplifine via pip or directly from GitHub. The project welcomes contributors and provides comprehensive documentation and support for users.

xLLM is an efficient LLM inference framework optimized for Chinese AI accelerators, enabling enterprise-grade deployment with enhanced efficiency and reduced cost. It adopts a service-engine decoupled inference architecture, achieving breakthrough efficiency through technologies like elastic scheduling, dynamic PD disaggregation, multi-stream parallel computing, graph fusion optimization, and global KV cache management. xLLM supports deployment of mainstream large models on Chinese AI accelerators, empowering enterprises in scenarios like intelligent customer service, risk control, supply chain optimization, ad recommendation, and more.

Flow-Like is an enterprise-grade workflow operating system built upon Rust for uncompromising performance, efficiency, and code safety. It offers a modular frontend for apps, a rich set of events, a node catalog, a powerful no-code workflow IDE, and tools to manage teams, templates, and projects within organizations. With typed workflows, users can create complex, large-scale workflows with clear data origins, transformations, and contracts. Flow-Like is designed to automate any process through seamless integration of LLM, ML-based, and deterministic decision-making instances.

ComfyUI-Copilot is an intelligent assistant built on the Comfy-UI framework that simplifies and enhances the AI algorithm debugging and deployment process through natural language interactions. It offers intuitive node recommendations, workflow building aids, and model querying services to streamline development processes. With features like interactive Q&A bot, natural language node suggestions, smart workflow assistance, and model querying, ComfyUI-Copilot aims to lower the barriers to entry for beginners, boost development efficiency with AI-driven suggestions, and provide real-time assistance for developers.

Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

Wanwu AI Agent Platform is an enterprise-grade one-stop commercially friendly AI agent development platform designed for business scenarios. It provides enterprises with a safe, efficient, and compliant one-stop AI solution. The platform integrates cutting-edge technologies such as large language models and business process automation to build an AI engineering platform covering model full life-cycle management, MCP, web search, AI agent rapid development, enterprise knowledge base construction, and complex workflow orchestration. It supports modular architecture design, flexible functional expansion, and secondary development, reducing the application threshold of AI technology while ensuring security and privacy protection of enterprise data. It accelerates digital transformation, cost reduction, efficiency improvement, and business innovation for enterprises of all sizes.

Cherry Studio is a desktop client that supports multiple LLM providers on Windows, Mac, and Linux. It offers diverse LLM provider support, AI assistants & conversations, document & data processing, practical tools integration, and enhanced user experience. The tool includes features like support for major LLM cloud services, AI web service integration, local model support, pre-configured AI assistants, document processing for text, images, and more, global search functionality, topic management system, AI-powered translation, and cross-platform support with ready-to-use features and themes for a better user experience.

TaskingAI brings Firebase's simplicity to **AI-native app development**. The platform enables the creation of GPTs-like multi-tenant applications using a wide range of LLMs from various providers. It features distinct, modular functions such as Inference, Retrieval, Assistant, and Tool, seamlessly integrated to enhance the development process. TaskingAI’s cohesive design ensures an efficient, intelligent, and user-friendly experience in AI application development.

Astron Agent is an enterprise-grade, commercial-friendly Agentic Workflow development platform that integrates AI workflow orchestration, model management, AI and MCP tool integration, RPA automation, and team collaboration features. It supports high-availability deployment, enabling organizations to rapidly build scalable, production-ready intelligent agent applications and establish their AI foundation for the future. The platform is stable, reliable, and business-friendly, with key features such as enterprise-grade high availability, intelligent RPA integration, ready-to-use tool ecosystem, and flexible large model support.

KServe provides a Kubernetes Custom Resource Definition for serving predictive and generative machine learning (ML) models. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to ML deployments. KServe enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing, and explainability. It is a standard, cloud agnostic Model Inference Platform for serving predictive and generative AI models on Kubernetes, built for highly scalable use cases.

LiteRT is Google's open-source high-performance runtime for on-device AI, previously known as TensorFlow Lite. The repository is currently not intended for open-source development, but aims to evolve to allow direct building and contributions. LiteRT supports Python versions 3.9, 3.10, 3.11 on Linux and MacOS. It ensures compatibility with existing .tflite file extension and format, offering conversion tools and continued active development under the name LiteRT.

veScale is a PyTorch Native LLM Training Framework. It provides a set of tools and components to facilitate the training of large language models (LLMs) using PyTorch. veScale includes features such as 4D parallelism, fast checkpointing, and a CUDA event monitor. It is designed to be scalable and efficient, and it can be used to train LLMs on a variety of hardware platforms.

BMAD-METHOD™ is a universal AI agent framework that revolutionizes Agile AI-Driven Development. It offers specialized AI expertise across various domains, including software development, entertainment, creative writing, business strategy, and personal wellness. The framework introduces two key innovations: Agentic Planning, where dedicated agents collaborate to create detailed specifications, and Context-Engineered Development, which ensures complete understanding and guidance for developers. BMAD-METHOD™ simplifies the development process by eliminating planning inconsistency and context loss, providing a seamless workflow for creating AI agents and expanding functionality through expansion packs.

Docq is a private and secure GenAI tool designed to extract knowledge from business documents, enabling users to find answers independently. It allows data to stay within organizational boundaries, supports self-hosting with various cloud vendors, and offers multi-model and multi-modal capabilities. Docq is extensible, open-source (AGPLv3), and provides commercial licensing options. The tool aims to be a turnkey solution for organizations to adopt AI innovation safely, with plans for future features like more data ingestion options and model fine-tuning.

AIBrix is an open-source initiative providing essential building blocks for scalable GenAI inference infrastructure. It delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored to enterprise needs. Key features include High-Density LoRA Management, LLM Gateway and Routing, LLM App-Tailored Autoscaler, Unified AI Runtime, Distributed Inference, Distributed KV Cache, Cost-efficient Heterogeneous Serving, and GPU Hardware Failure Detection.

AgentField is an open-source control plane designed for autonomous AI agents, providing infrastructure for agents to make decisions beyond chatbots. It offers features like scaling infrastructure, routing & discovery, async execution, durable state, observability, trust infrastructure with cryptographic identity, verifiable credentials, and policy enforcement. Users can write agents in Python, Go, TypeScript, or interact via REST APIs. The tool enables the creation of AI backends that reason autonomously within defined boundaries, offering predictability and flexibility. AgentField aims to bridge the gap between AI frameworks and production-ready infrastructure for AI agents.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

OpenVINO™ Model Server (OVMS) is a high-performance system for serving models. Implemented in C++ for scalability and optimized for deployment on Intel architectures, the model server uses the same architecture and API as TensorFlow Serving and KServe while applying OpenVINO for inference execution. Inference service is provided via gRPC or REST API, making deploying new algorithms and AI experiments easy.

KitOps is a packaging and versioning system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using. KitOps simplifies the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. KitOps' ModelKits are a standards-based package for models, their dependencies, configurations, and codebases. ModelKits are portable, reproducible, and work with the tools you already use.

CSGHub is an open source, trustworthy large model asset management platform that can assist users in governing the assets involved in the lifecycle of LLM and LLM applications (datasets, model files, codes, etc). With CSGHub, users can perform operations on LLM assets, including uploading, downloading, storing, verifying, and distributing, through Web interface, Git command line, or natural language Chatbot. Meanwhile, the platform provides microservice submodules and standardized OpenAPIs, which could be easily integrated with users' own systems. CSGHub is committed to bringing users an asset management platform that is natively designed for large models and can be deployed On-Premise for fully offline operation. CSGHub offers functionalities similar to a privatized Huggingface(on-premise Huggingface), managing LLM assets in a manner akin to how OpenStack Glance manages virtual machine images, Harbor manages container images, and Sonatype Nexus manages artifacts.

The AI Horde is an enterprise-level ML-Ops crowdsourced distributed inference cluster for AI Models. This middleware can support both Image and Text generation. It is infinitely scalable and supports seamless drop-in/drop-out of compute resources. The Public version allows people without a powerful GPU to use Stable Diffusion or Large Language Models like Pygmalion/Llama by relying on spare/idle resources provided by the community and also allows non-python clients, such as games and apps, to use AI-provided generations.

Caikit is an AI toolkit that enables users to manage models through a set of developer friendly APIs. It provides a consistent format for creating and using AI models against a wide variety of data domains and tasks.

llm-resource is a comprehensive collection of high-quality resources for Large Language Models (LLM). It covers various aspects of LLM including algorithms, training, fine-tuning, alignment, inference, data engineering, compression, evaluation, prompt engineering, AI frameworks, AI basics, AI infrastructure, AI compilers, LLM application development, LLM operations, AI systems, and practical implementations. The repository aims to gather and share valuable resources related to LLM for the community to benefit from.

LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

AIAC is a library and command line tool to generate Infrastructure as Code (IaC) templates, configurations, utilities, queries, and more via LLM providers such as OpenAI, Amazon Bedrock, and Ollama. Users can define multiple 'backends' targeting different LLM providers and environments using a simple configuration file. The tool allows users to ask a model to generate templates for different scenarios and composes an appropriate request to the selected provider, storing the resulting code to a file and/or printing it to standard output.

ENOVA is an open-source service for Large Language Model (LLM) deployment, monitoring, injection, and auto-scaling. It addresses challenges in deploying stable serverless LLM services on GPU clusters with auto-scaling by deconstructing the LLM service execution process and providing configuration recommendations and performance detection. Users can build and deploy LLM with few command lines, recommend optimal computing resources, experience LLM performance, observe operating status, achieve load balancing, and more. ENOVA ensures stable operation, cost-effectiveness, efficiency, and strong scalability of LLM services.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

Vidur is a high-fidelity and extensible LLM inference simulator designed for capacity planning, deployment configuration optimization, testing new research ideas, and studying system performance of models under different workloads and configurations. It supports various models and devices, offers chrome trace exports, and can be set up using mamba, venv, or conda. Users can run the simulator with various parameters and monitor metrics using wandb. Contributions are welcome, subject to a Contributor License Agreement and adherence to the Microsoft Open Source Code of Conduct.

AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.