UMbreLLa is a tool designed for deploying Large Language Models (LLMs) for personal agents. It combines offloading, speculative decoding, and quantization to optimize single-user LLM deployment scenarios. With UMbreLLa, 70B-level models can achieve performance comparable to human reading speed on an RTX 4070Ti, delivering exceptional efficiency and responsiveness, especially for coding tasks. The tool supports deploying models on various GPUs and offers features like code completion and CLI/Gradio chatbots. Users can configure the LLM engine for optimal performance based on their hardware setup.

BrowserAI is a production-ready tool that allows users to run AI models directly in the browser, offering simplicity, speed, privacy, and open-source capabilities. It provides WebGPU acceleration for fast inference, zero server costs, offline capability, and developer-friendly features. Perfect for web developers, companies seeking privacy-conscious AI solutions, researchers experimenting with browser-based AI, and hobbyists exploring AI without infrastructure overhead. The tool supports various AI tasks like text generation, speech recognition, and text-to-speech, with pre-configured popular models ready to use. It offers a simple SDK with multiple engine support and seamless switching between MLC and Transformers engines.

Dexto is a lightweight runtime for creating and running AI agents that turn natural language into real-world actions. It serves as the missing intelligence layer for building AI applications, standalone chatbots, or as the reasoning engine inside larger products. Dexto features a powerful CLI and Web UI for running AI agents, supports multiple interfaces, allows hot-swapping of LLMs from various providers, connects to remote tool servers via the Model Context Protocol, is config-driven with version-controlled YAML, offers production-ready core features, extensibility for custom services, and enables multi-agent collaboration via MCP and A2A.

Neogenesis System is an advanced AI decision-making framework that enables agents to 'think about how to think'. It implements a metacognitive approach with real-time learning, tool integration, and multi-LLM support, allowing AI to make expert-level decisions in complex environments. Key features include metacognitive intelligence, tool-enhanced decisions, real-time learning, aha-moment breakthroughs, experience accumulation, and multi-LLM support.

AgentScope is a multi-agent platform designed to empower developers to build multi-agent applications with large-scale models. It features three high-level capabilities: Easy-to-Use, High Robustness, and Actor-Based Distribution. AgentScope provides a list of `ModelWrapper` to support both local model services and third-party model APIs, including OpenAI API, DashScope API, Gemini API, and ollama. It also enables developers to rapidly deploy local model services using libraries such as ollama (CPU inference), Flask + Transformers, Flask + ModelScope, FastChat, and vllm. AgentScope supports various services, including Web Search, Data Query, Retrieval, Code Execution, File Operation, and Text Processing. Example applications include Conversation, Game, and Distribution. AgentScope is released under Apache License 2.0 and welcomes contributions.

Candle-vllm is an efficient and easy-to-use platform designed for inference and serving local LLMs, featuring an OpenAI compatible API server. It offers a highly extensible trait-based system for rapid implementation of new module pipelines, streaming support in generation, efficient management of key-value cache with PagedAttention, and continuous batching. The tool supports chat serving for various models and provides a seamless experience for users to interact with LLMs through different interfaces.

AutoRound is an advanced weight-only quantization algorithm for low-bits LLM inference. It competes impressively against recent methods without introducing any additional inference overhead. The method adopts sign gradient descent to fine-tune rounding values and minmax values of weights in just 200 steps, often significantly outperforming SignRound with the cost of more tuning time for quantization. AutoRound is tailored for a wide range of models and consistently delivers noticeable improvements.

Alphora is a full-stack framework for building production AI agents, providing agent orchestration, prompt engineering, tool execution, memory management, streaming, and deployment with an async-first, OpenAI-compatible design. It offers features like agent derivation, reasoning-action loop, async streaming, visual debugger, OpenAI compatibility, multimodal support, tool system with zero-config tools and type safety, prompt engine with dynamic prompts, memory and storage management, sandbox for secure execution, deployment as API, and more. Alphora allows users to build sophisticated AI agents easily and efficiently.

Augustus is a Go-based LLM vulnerability scanner designed for security professionals to test large language models against a wide range of adversarial attacks. It integrates with 28 LLM providers, covers 210+ adversarial attacks including prompt injection, jailbreaks, encoding exploits, and data extraction, and produces actionable vulnerability reports. The tool is built for production security testing with features like concurrent scanning, rate limiting, retry logic, and timeout handling out of the box.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

Tokscale is a high-performance CLI tool and visualization dashboard for tracking token usage and costs across multiple AI coding agents. It helps monitor and analyze token consumption from various AI coding tools, providing real-time pricing calculations using LiteLLM's pricing data. Inspired by the Kardashev scale, Tokscale measures token consumption as users scale the ranks of AI-augmented development. It offers interactive TUI mode, multi-platform support, real-time pricing, detailed breakdowns, web visualization, flexible filtering, and social platform features.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

GPULlama3.java powered by TornadoVM is a Java-native implementation of Llama3 that automatically compiles and executes Java code on GPUs via TornadoVM. It supports Llama3, Mistral, Qwen2.5, Qwen3, and Phi3 models in the GGUF format. The repository aims to provide GPU acceleration for Java code, enabling faster execution and high-performance access to off-heap memory. It offers features like interactive and instruction modes, flexible backend switching between OpenCL and PTX, and cross-platform compatibility with NVIDIA, Intel, and Apple GPUs.

RubyLLM::Agents is a production-ready Rails engine for building, managing, and monitoring LLM-powered AI agents. It seamlessly integrates with Rails apps, providing features like automatic execution tracking, cost analytics, budget controls, and a real-time dashboard. Users can build intelligent AI agents in Ruby using a clean DSL and support various LLM providers like OpenAI GPT-4, Anthropic Claude, and Google Gemini. The engine offers features such as agent DSL configuration, execution tracking, cost analytics, reliability with retries and fallbacks, budget controls, multi-tenancy support, async execution with Ruby fibers, real-time dashboard, streaming, conversation history, image operations, alerts, and more.

AI Counsel is a true deliberative consensus MCP server where AI models engage in actual debate, refine positions across multiple rounds, and converge with voting and confidence levels. It features two modes (quick and conference), mixed adapters (CLI tools and HTTP services), auto-convergence, structured voting, semantic grouping, model-controlled stopping, evidence-based deliberation, local model support, data privacy, context injection, semantic search, fault tolerance, and full transcripts. Users can run local and cloud models to deliberate on various questions, ground decisions in reality by querying code and files, and query past decisions for analysis. The tool is designed for critical technical decisions requiring multi-model deliberation and consensus building.

Conduit is a unified Swift 6.2 SDK for local and cloud LLM inference, providing a single Swift-native API that can target Anthropic, OpenRouter, Ollama, MLX, HuggingFace, and Apple’s Foundation Models without rewriting your prompt pipeline. It allows switching between local, cloud, and system providers with minimal code changes, supports downloading models from HuggingFace Hub for local MLX inference, generates Swift types directly from LLM responses, offers privacy-first options for on-device running, and is built with Swift 6.2 concurrency features like actors, Sendable types, and AsyncSequence.

UMbreLLa is a tool designed for deploying Large Language Models (LLMs) for personal agents. It combines offloading, speculative decoding, and quantization to optimize single-user LLM deployment scenarios. With UMbreLLa, 70B-level models can achieve performance comparable to human reading speed on an RTX 4070Ti, delivering exceptional efficiency and responsiveness, especially for coding tasks. The tool supports deploying models on various GPUs and offers features like code completion and CLI/Gradio chatbots. Users can configure the LLM engine for optimal performance based on their hardware setup.

Glide is a cloud-native LLM gateway that provides a unified REST API for accessing various large language models (LLMs) from different providers. It handles LLMOps tasks such as model failover, caching, key management, and more, making it easy to integrate LLMs into applications. Glide supports popular LLM providers like OpenAI, Anthropic, Azure OpenAI, AWS Bedrock (Titan), Cohere, Google Gemini, OctoML, and Ollama. It offers high availability, performance, and observability, and provides SDKs for Python and NodeJS to simplify integration.

Easy, fast, and cheap pretrain, finetune, serving for everyone

Agents-Flex is a LLM Application Framework like LangChain base on Java. It provides a set of tools and components for building LLM applications, including LLM Visit, Prompt and Prompt Template Loader, Function Calling Definer, Invoker and Running, Memory, Embedding, Vector Storage, Resource Loaders, Document, Splitter, Loader, Parser, LLMs Chain, and Agents Chain.

Entirely-in-browser, fully private LLM chatbot supporting Llama 3, Mistral and other open source models. Fully private = No conversation data ever leaves your computer. Runs in the browser = No server needed and no install needed! Works offline. Easy-to-use interface on par with ChatGPT, but for open source LLMs. System requirements include a modern browser with WebGPU support. Supported models include TinyLlama-1.1B-Chat-v0.4-q4f32_1-1k, Llama-3-8B-Instruct-q4f16_1, Phi1.5-q4f16_1-1k, and Mistral-7B-Instruct-v0.2-q4f16_1. Looking for contributors to improve the interface, support more models, speed up initial model loading time, and fix bugs.

ShellGPT is a tool that allows users to chat with a large language model (LLM) in the terminal. It can be used for various purposes such as generating shell commands, telling stories, and interacting with Linux terminal. The tool provides different modes of usage including direct mode for asking questions, REPL mode for chatting with LLM, and TUI mode tailored for inferring shell commands. Users can customize the tool by setting up different language model backends such as Ollama or using OpenAI compatible API endpoints. Additionally, ShellGPT comes with built-in system contents for general questions, correcting typos, generating URL slugs, programming questions, shell command inference, and git commit message generation. Users can define their own content or share customized contents in the discuss section.

Open-LLM-VTuber is a project in early stages of development that allows users to interact with Large Language Models (LLM) using voice commands and receive responses through a Live2D talking face. The project aims to provide a minimum viable prototype for offline use on macOS, Linux, and Windows, with features like long-term memory using MemGPT, customizable LLM backends, speech recognition, and text-to-speech providers. Users can configure the project to chat with LLMs, choose different backend services, and utilize Live2D models for visual representation. The project supports perpetual chat, offline operation, and GPU acceleration on macOS, addressing limitations of existing solutions on macOS.

The demo-chatbot repository contains a simple app to chat with an LLM, allowing users to create any LLM Inference Web Apps using Python. The app utilizes OpenAI's GPT-4 API to generate responses to user messages, with the flexibility to switch to other APIs or models. The repository includes a tutorial in the Taipy documentation for creating the app. Users need an OpenAI account with an active API key to run the app by cloning the repository, installing dependencies, setting up the API key in a .env file, and running the main.py file.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.