Genkit

Genkit is an open-source framework for building full-stack AI-powered applications, used in production by Google's Firebase. It provides SDKs for JavaScript/TypeScript (Stable), Go (Beta), and Python (Alpha) with unified interface for integrating AI models from providers like Google, OpenAI, Anthropic, Ollama. Rapidly build chatbots, automations, and recommendation systems using streamlined APIs for multimodal content, structured outputs, tool calling, and agentic workflows. Genkit simplifies AI integration with open-source SDK, unified APIs, and offers text and image generation, structured data generation, tool calling, prompt templating, persisted chat interfaces, AI workflows, and AI-powered data retrieval (RAG).

Firebase Genkit (beta) is a framework with powerful tooling to help app developers build, test, deploy, and monitor AI-powered features with confidence. Genkit is cloud optimized and code-centric, integrating with many services that have free tiers to get started. It provides unified API for generation, context-aware AI features, evaluation of AI workflow, extensibility with plugins, easy deployment to Firebase or Google Cloud, observability and monitoring with OpenTelemetry, and a developer UI for prototyping and testing AI features locally. Genkit works seamlessly with Firebase or Google Cloud projects through official plugins and templates.

Higress is an open-source cloud-native API gateway built on the core of Istio and Envoy, based on Alibaba's internal practice of Envoy Gateway. It is designed for AI-native API gateway, serving AI businesses such as Tongyi Qianwen APP, Bailian Big Model API, and Machine Learning PAI platform. Higress provides capabilities to interface with LLM model vendors, AI observability, multi-model load balancing/fallback, AI token flow control, and AI caching. It offers features for AI gateway, Kubernetes Ingress gateway, microservices gateway, and security protection gateway, with advantages in production-level scalability, stream processing, extensibility, and ease of use.

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.

AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.

LLMariner is an extensible open source platform built on Kubernetes to simplify the management of generative AI workloads. It enables efficient handling of training and inference data within clusters, with OpenAI-compatible APIs for seamless integration with a wide range of AI-driven applications.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

GPTMe is a tool that allows users to interact with an LLM assistant directly in their terminal in a chat-style interface. The tool provides features for the assistant to run shell commands, execute code, read/write files, and more, making it suitable for various development and terminal-based tasks. It serves as a local alternative to ChatGPT's 'Code Interpreter,' offering flexibility and privacy when using a local model. GPTMe supports code execution, file manipulation, context passing, self-correction, and works with various AI models like GPT-4. It also includes a GitHub Bot for requesting changes and operates entirely in GitHub Actions. In progress features include handling long contexts intelligently, a web UI and API for conversations, web and desktop vision, and a tree-based conversation structure.

Bodo is a high-performance Python compute engine designed for large-scale data processing and AI workloads. It utilizes an auto-parallelizing just-in-time compiler to optimize Python programs, making them 20x to 240x faster compared to alternatives. Bodo seamlessly integrates with native Python APIs like Pandas and NumPy, eliminates runtime overheads using MPI for distributed execution, and provides exceptional performance and scalability for data workloads. It is easy to use, interoperable with the Python ecosystem, and integrates with modern data platforms like Apache Iceberg and Snowflake. Bodo focuses on data-intensive and computationally heavy workloads in data engineering, data science, and AI/ML, offering automatic optimization and parallelization, linear scalability, advanced I/O support, and a high-performance SQL engine.

Geoweaver is an in-browser software that enables users to easily compose and execute full-stack data processing workflows using online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It provides server management, code repository, workflow orchestration software, and history recording capabilities. Users can run it from both local and remote machines. Geoweaver aims to make data processing workflows manageable for non-coder scientists and preserve model run history. It offers features like progress storage, organization, SSH connection to external servers, and a web UI with Python support.

Personal AI assistant/agent in your terminal, with tools for using the terminal, running code, editing files, browsing the web, using vision, and more. A great coding agent that is general-purpose to assist in all kinds of knowledge work, from a simple but powerful CLI. An unconstrained local alternative to ChatGPT with 'Code Interpreter', Cursor Agent, etc. Not limited by lack of software, internet access, timeouts, or privacy concerns if using local models.

hugegraph-ai aims to explore the integration of HugeGraph with artificial intelligence (AI) and provide comprehensive support for developers to leverage HugeGraph's AI capabilities in their projects. It includes modules for large language models, graph machine learning, and a Python client for HugeGraph. The project aims to address challenges like timeliness, hallucination, and cost-related issues by integrating graph systems with AI technologies.

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices. Key value propositions of ExecuTorch are: * **Portability:** Compatibility with a wide variety of computing platforms, from high-end mobile phones to highly constrained embedded systems and microcontrollers. * **Productivity:** Enabling developers to use the same toolchains and SDK from PyTorch model authoring and conversion, to debugging and deployment to a wide variety of platforms. * **Performance:** Providing end users with a seamless and high-performance experience due to a lightweight runtime and utilizing full hardware capabilities such as CPUs, NPUs, and DSPs.

The oneAPI-samples repository contains a collection of samples for the Intel oneAPI Toolkits. These samples cover various topics such as AI and analytics, end-to-end workloads, features and functionality, getting started samples, Jupyter notebooks, direct programming, C++, Fortran, libraries, publications, rendering toolkit, and tools. Users can find samples based on expertise, programming language, and target device. The repository structure is organized by high-level categories, and platform validation includes Ubuntu 22.04, Windows 11, and macOS. The repository provides instructions for getting samples, including cloning the repository or downloading specific tagged versions. Users can also use integrated development environments (IDEs) like Visual Studio Code. The code samples are licensed under the MIT license.

ClicShoppingAI is a powerful open-source Ecommerce solution that supports B2B, B2C, and B2B-B2C. Integrated with cutting-edge generative artificial intelligence systems like Gpt and Ollama, it helps merchants increase turnover and competitiveness for free. With AI capabilities, it optimizes inventory, offers personalized recommendations, and provides top-notch customer service. The solution is modular, lightweight, and user-friendly, with a seamless, responsive design for all devices. Installation is easy, empowering ongoing development through community support. Features include GPT API integration, generative AI functionalities, real-time safety stock predictive, WYSIWYG product description creation, image editor management, full SEO optimization, payment and shipping modules, extension system, GDPR compliance, multi-language support, and more.

Agent Development Kit (ADK) for Java is an open-source toolkit designed for developers to build, evaluate, and deploy sophisticated AI agents with flexibility and control. It allows defining agent behavior, orchestration, and tool use directly in code, enabling robust debugging, versioning, and deployment anywhere. The toolkit offers a rich tool ecosystem, code-first development approach, and support for modular multi-agent systems, making it ideal for creating advanced AI agents integrated with Google Cloud services.

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.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

LoLLMs Server is a text generation server based on large language models. It provides a Flask-based API for generating text using various pre-trained language models. This server is designed to be easy to install and use, allowing developers to integrate powerful text generation capabilities into their applications.

LlamaIndex.TS is a data framework for your LLM application. Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

Botpress is a platform for building next-generation chatbots and assistants powered by OpenAI. It provides a range of tools and integrations to help developers quickly and easily create and deploy chatbots for various use cases.

BotSharp is an open-source machine learning framework for building AI bot platforms. It provides a comprehensive set of tools and components for developing and deploying intelligent virtual assistants. BotSharp is designed to be modular and extensible, allowing developers to easily integrate it with their existing systems and applications. With BotSharp, you can quickly and easily create AI-powered chatbots, virtual assistants, and other conversational AI applications.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

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.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.