Project Miyagi showcases Microsoft's Copilot Stack in an envisioning workshop aimed at designing, developing, and deploying enterprise-grade intelligent apps. By exploring both generative and traditional ML use cases, Miyagi offers an experiential approach to developing AI-infused product experiences that enhance productivity and enable hyper-personalization. Additionally, the workshop introduces traditional software engineers to emerging design patterns in prompt engineering, such as chain-of-thought and retrieval-augmentation, as well as to techniques like vectorization for long-term memory, fine-tuning of OSS models, agent-like orchestration, and plugins or tools for augmenting and grounding LLMs.

agentUniverse is a multi-agent framework based on large language models, providing flexible capabilities for building individual agents. It focuses on multi-agent collaborative patterns, integrating domain experience to help agents solve problems in various fields. The framework includes pattern components like PEER and DOE for event interpretation, industry analysis, and financial report generation. It offers features for agent construction, multi-agent collaboration, and domain expertise integration, aiming to create intelligent applications with professional know-how.

agentUniverse is a multi-agent framework based on large language models, providing flexible capabilities for building individual agents. It focuses on collaborative pattern components to solve problems in various fields and integrates domain experience. The framework supports LLM model integration and offers various pattern components like PEER and DOE. Users can easily configure models and set up agents for tasks. agentUniverse aims to assist developers and enterprises in constructing domain-expert-level intelligent agents for seamless collaboration.

KitOps is a CNCF open standards project for packaging, versioning, and securely sharing AI/ML projects. It provides a unified solution for packaging, versioning, and managing assets in security-conscious enterprises, governments, and cloud operators. KitOps elevates AI artifacts to first-class, governed assets through ModelKits, which are tamper-proof, signable, and compatible with major container registries. The tool simplifies collaboration between data scientists, developers, and SREs, ensuring reliable and repeatable workflows for both development and operations. KitOps supports packaging for various types of models, including large language models, computer vision models, multi-modal models, predictive models, and audio models. It also facilitates compliance with the EU AI Act by offering tamper-proof, signable, and auditable ModelKits.

CodeFuse-muAgent is a Multi-Agent framework designed to streamline Standard Operating Procedure (SOP) orchestration for agents. It integrates toolkits, code libraries, knowledge bases, and sandbox environments for rapid construction of complex Multi-Agent interactive applications. The framework enables efficient execution and handling of multi-layered and multi-dimensional tasks.

Emeltal is a local ML voice chat tool that uses high-end models to provide a self-contained, user-friendly out-of-the-box experience. It offers a hand-picked list of proven open-source high-performance models, aiming to provide the best model for each category/size combination. Emeltal heavily relies on the llama.cpp for LLM processing, and whisper.cpp for voice recognition. Text rendering uses Ink to convert between Markdown and HTML. It uses PopTimer for debouncing things. Emeltal is released under the terms of the MIT license, and all model data which is downloaded locally by the app comes from HuggingFace, and use of the models and data is subject to the respective license of each specific model.

R1-searcher is a tool designed to incentivize the search capability in large reasoning models (LRMs) via reinforcement learning. It enables LRMs to invoke web search and obtain external information during the reasoning process by utilizing a two-stage outcome-supervision reinforcement learning approach. The tool does not require instruction fine-tuning for cold start and is compatible with existing Base LLMs or Chat LLMs. It includes training code, inference code, model checkpoints, and a detailed technical report.

agentUniverse is a framework for developing applications powered by multi-agent based on large language model. It provides essential components for building single agent and multi-agent collaboration mechanism for customizing collaboration patterns. Developers can easily construct multi-agent applications and share pattern practices from different fields. The framework includes pre-installed collaboration patterns like PEER and DOE for complex task breakdown and data-intensive tasks.

LangChain for Java simplifies integrating Large Language Models (LLMs) into Java applications by offering unified APIs for various LLM providers and embedding stores. It provides a comprehensive toolbox with tools for prompt templating, chat memory management, function calling, and high-level patterns like Agents and RAG. The library supports 15+ popular LLM providers and 15+ embedding stores, offering numerous examples to help users quickly start building LLM-powered applications. LangChain4j is a fusion of ideas from various projects and actively incorporates new techniques and integrations to keep users up-to-date. The project is under active development, with core functionality already in place for users to start building LLM-powered apps.

Nixtla is a production-ready generative pretrained transformer for time series forecasting and anomaly detection. It can accurately predict various domains such as retail, electricity, finance, and IoT with just a few lines of code. TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity, making it accessible even to users with minimal coding experience. The model is based on self-attention and is independently trained on a vast time series dataset to minimize forecasting error. It offers features like zero-shot inference, fine-tuning, API access, adding exogenous variables, multiple series forecasting, custom loss function, cross-validation, prediction intervals, and handling irregular timestamps.

LLM-Assistant is a browser interface based on Gradio that interfaces with local LLMs to call functions and act as a general assistant. It works with any instruct-finetuned LLM, can search for information (RAG), knows when to call functions, has realtime mode for working across the system, and answers questions from PDF files. The tool aims to provide voice access and more functions in the future. Current bugs include rare crashes. Setup involves cloning the repo to a virtual environment, installing requirements, downloading and placing LLM model in the model folder, and running main.py. Usage includes Assistant mode for general chat and calling functions like playing music, as well as Realtime mode for editing documents or replying to emails in real-time.

AgentUp is an active development tool that provides a developer-first agent framework for creating AI agents with enterprise-grade infrastructure. It allows developers to define agents with configuration, ensuring consistent behavior across environments. The tool offers secure design, configuration-driven architecture, extensible ecosystem for customizations, agent-to-agent discovery, asynchronous task architecture, deterministic routing, and MCP support. It supports multiple agent types like reactive agents and iterative agents, making it suitable for chatbots, interactive applications, research tasks, and more. AgentUp is built by experienced engineers from top tech companies and is designed to make AI agents production-ready, secure, and reliable.

Aphrodite is the official backend engine for PygmalionAI, serving as the inference endpoint for the website. It allows serving Hugging Face-compatible models with fast speeds. Features include continuous batching, efficient K/V management, optimized CUDA kernels, quantization support, distributed inference, and 8-bit KV Cache. The engine requires Linux OS and Python 3.8 to 3.12, with CUDA >= 11 for build requirements. It supports various GPUs, CPUs, TPUs, and Inferentia. Users can limit GPU memory utilization and access full commands via CLI.

Aphrodite is an inference engine optimized for serving HuggingFace-compatible models at scale. It leverages vLLM's Paged Attention technology to deliver high-performance model inference for multiple concurrent users. The engine supports continuous batching, efficient key/value management, optimized CUDA kernels, quantization support, distributed inference, and modern samplers. It can be easily installed and launched, with Docker support for deployment. Aphrodite requires Linux or Windows OS, Python 3.8 to 3.12, and CUDA >= 11. It is designed to utilize 90% of GPU VRAM but offers options to limit memory usage. Contributors are welcome to enhance the engine.

5ire is a cross-platform desktop client that integrates a local knowledge base for multilingual vectorization, supports parsing and vectorization of various document formats, offers usage analytics to track API spending, provides a prompts library for creating and organizing prompts with variable support, allows bookmarking of conversations, and enables quick keyword searches across conversations. It is licensed under the GNU General Public License version 3.

BESSER is a low-modeling low-code open-source platform funded by an FNR Pearl grant. It is built on B-UML, a Python-based interpretation of a 'Universal Modeling Language'. Users can specify their software application using B-UML and generate executable code for various applications like Django models or SQLAlchemy-compatible database structures. BESSER is available on PyPi and can be installed with pip. It supports popular Python IDEs and encourages contributions from the community.

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.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

Jupyter AI connects generative AI with Jupyter notebooks. It provides a user-friendly and powerful way to explore generative AI models in notebooks and improve your productivity in JupyterLab and the Jupyter Notebook. Specifically, Jupyter AI offers: * An `%%ai` magic that turns the Jupyter notebook into a reproducible generative AI playground. This works anywhere the IPython kernel runs (JupyterLab, Jupyter Notebook, Google Colab, Kaggle, VSCode, etc.). * A native chat UI in JupyterLab that enables you to work with generative AI as a conversational assistant. * Support for a wide range of generative model providers, including AI21, Anthropic, AWS, Cohere, Gemini, Hugging Face, NVIDIA, and OpenAI. * Local model support through GPT4All, enabling use of generative AI models on consumer grade machines with ease and privacy.

Khoj is an open-source, personal AI assistant that extends your capabilities by creating always-available AI agents. You can share your notes and documents to extend your digital brain, and your AI agents have access to the internet, allowing you to incorporate real-time information. Khoj is accessible on Desktop, Emacs, Obsidian, Web, and Whatsapp, and you can share PDF, markdown, org-mode, notion files, and GitHub repositories. You'll get fast, accurate semantic search on top of your docs, and your agents can create deeply personal images and understand your speech. Khoj is self-hostable and always will be.

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

Danswer is an open-source Gen-AI Chat and Unified Search tool that connects to your company's docs, apps, and people. It provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for configuring Personas (AI Assistants) and their Prompts. Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc. By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already supported?" or "Where's the pull request for feature Y?"

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.