Best AI tools for< Support Frameworks >

Kombai is an AI tool designed to code email and web designs like humans. It uses deep learning and heuristics models to interpret UI designs and generate high-quality HTML, CSS, or React code with human-like names for classes and components. Kombai aims to help developers save time by automating the process of writing UI code based on design files without the need for tagging, naming, or grouping elements. The tool is currently in 'public research preview' and is free for individual developers to use.

CodeParrot is an AI tool designed to speed up frontend development tasks by generating production-ready frontend components from Figma design files using Large Language Models. It helps developers reduce UI development time, improve code quality, and focus on more creative tasks. CodeParrot offers customization options, support for frameworks like React, Vue, and Angular, and integrates seamlessly into various workflows, making it a must-have tool for developers looking to enhance their frontend development process.

The Web App Builder by Unshift AI is an AI-powered platform designed to help users quickly and efficiently create fully functional web applications using modern JavaScript frameworks. With features like an advanced editor, support for various frameworks, and access to professionally written code, the platform streamlines the app development process and saves developers time. Users can easily customize design elements, manage content, and export their apps to different frameworks. The platform also offers AI-generated content, extensive component libraries, and a customizable design system to enhance app development. Overall, The Web App Builder is a comprehensive tool for building web applications with ease and efficiency.

AI Video API is an all-in-one API hub for AI-generated video, offering a cost-effective, user-friendly, and robust solution for creating videos in various styles. The platform allows users to transform their ideas into stunning videos with just a few words, enabling text-to-video generation, image to animated video conversion, extended video length, dual output formats, and real-time alerts. With seamless integration into popular frameworks and support for multiple programming languages, AI Video API empowers users to innovate effortlessly, stay ahead of the curve, and scale their projects limitlessly.

Adrian AI Lab is an AI-powered platform offering specialized GPTs for business and lifestyle purposes. The platform provides tools like Copywriter GPT for crafting ads based on viral marketing frameworks and Business Strategies GPT for expert analysis of strategies for startups and established businesses. Users can leverage these AI capabilities to enhance their marketing efforts and business decision-making processes.

Valohai is a scalable MLOps platform that enables Continuous Integration/Continuous Deployment (CI/CD) for machine learning and pipeline automation on-premises and across various cloud environments. It helps streamline complex machine learning workflows by offering framework-agnostic ML capabilities, automatic versioning with complete lineage of ML experiments, hybrid and multi-cloud support, scalability and performance optimization, streamlined collaboration among data scientists, IT, and business units, and smart orchestration of ML workloads on any infrastructure. Valohai also provides a knowledge repository for storing and sharing the entire model lifecycle, facilitating cross-functional collaboration, and allowing developers to build with total freedom using any libraries or frameworks.

Trudo.ai is an AI-powered workflow automation platform that allows users to build complex workflows using simple English language commands. The platform is backed by Python code and features interactive UI components. Users can create and customize nodes, handle dynamic routing, and benefit from flexible memory allocation. Trudo.ai also offers AI Copilot functionality for non-technical users to generate logic and user interfaces. With support for various data types and no extra frameworks required, Trudo.ai covers a wide range of use cases and provides versions to track workflow changes.

Code99 is an AI-powered platform designed to speed up the development process by generating instant boilerplate code. It allows users to customize their tech stack, streamline development, and launch projects faster. Ideal for startups, developers, and IT agencies, Code99 offers features such as authentication, database support, CRUD REST APIs, data validation, Swagger API documentation, email integration, state management, and more. The platform aims to supercharge productivity by enabling the rapid creation of production-ready apps in minutes, with AI handling the heavy lifting. Users can choose from popular frameworks and databases, customize features, and effortlessly edit and experiment with their projects.

Myple is an AI application that enables users to build, scale, and secure AI applications with ease. It provides production-ready AI solutions tailored to individual needs, offering a seamless user experience. With support for multiple languages and frameworks, Myple simplifies the integration of AI through open-source SDKs. The platform features a clean interface, keyboard shortcuts for efficient navigation, and templates to kickstart AI projects. Additionally, Myple offers AI-powered tools like RAG chatbot for documentation, Gmail agent for email notifications, and AskFeynman for physics-related queries. Users can connect their favorite tools and services effortlessly, without any coding. Joining the beta program grants early access to new features and issue resolution prioritization.

Conformity is an AI-powered platform designed to simplify compliance processes for businesses. It offers expert advice on compliance, personalized templates, and immediate responses to compliance questions. With CompliBot at its core, Conformity ensures businesses meet regulatory requirements effortlessly. The platform covers a wide range of compliance frameworks such as SOC 2, HIPAA, GDPR, and more, providing comprehensive support to users.

Goast.ai is an AI assistant designed to help engineering teams resolve errors and exceptions faster by automatically analyzing and fixing issues from error logs. It offers real-time bug fixes, root cause analysis, and automated bug fixing processes, ultimately saving time and improving productivity for development teams. Goast integrates with popular observability tools, supports various frameworks and languages, and provides a user-friendly interface for seamless collaboration and feedback.

Qlerify is an AI-powered software modeling tool that helps digital transformation teams accelerate the digitalization of enterprise business processes. It allows users to quickly create workflows with AI, generate source code in minutes, and reuse actionable models in various formats. Qlerify supports powerful frameworks like Event Storming, Domain Driven Design, and Business Process Modeling, providing a user-friendly interface for collaborative modeling.

ConvoStack is a free and open-source full-stack framework that makes it easy for developers to embed a customizable AI chatbot widget into a website with just a few lines of code. It supports popular frameworks such as Pinecone DB, Express, and React, and allows developers to integrate their own AI models using LangChain. ConvoStack is built on a tech stack that is used and loved by developers, and it is designed to be customizable for any use case.

The AI SDK is a free open-source library designed to empower users to build AI-powered products. It offers a unified provider API, generative UI capabilities, framework-agnostic support, and streaming AI responses. The SDK is trusted by builders at OpenAI, Claude, and Hugging Face, and has received positive feedback for its ease of use and efficiency in building AI features within minutes.

Ogoodo is a Kanban tool that helps teams to visualize their workflow, track their progress, and improve their productivity. It offers a variety of features to support the Kanban framework, including a board view, time tracking, timeline prediction, and analytics. Ogoodo is designed to be easy to use and can be customized to fit the needs of any team.

PoplarML is a platform that enables the deployment of production-ready, scalable ML systems with minimal engineering effort. It offers one-click deploys, real-time inference, and framework agnostic support. With PoplarML, users can seamlessly deploy ML models using a CLI tool to a fleet of GPUs and invoke their models through a REST API endpoint. The platform supports Tensorflow, Pytorch, and JAX models.

SWE Kit is an open-source headless IDE designed for building custom coding agents with state-of-the-art performance. It offers AI-native tools to streamline the coding review process, enhance code quality, and optimize development efficiency. The application supports various agentic frameworks and LLM inference providers, providing a flexible runtime environment for seamless codebase interaction. With features like code analysis, code indexing, and third-party service integrations, SWE Kit empowers developers to create and run coding agents effortlessly.

ClearPoint is a strategic planning and execution software designed to drive change and streamline strategy for organizations of all sizes. It offers a robust platform with features such as data collection automation, extensive integrations, customized reporting, data security, artificial intelligence capabilities, analytics, automation, and real-time collaboration. ClearPoint provides solutions for strategy planning, business reporting, OKR management, organizational alignment, project management, and data visualization. The application is known for its user-friendly interface, flexibility, and ability to support any strategic framework or reporting structure. With over 12,000 users worldwide, ClearPoint is a trusted partner in strategy execution, offering personalized support and guidance from strategy experts. Powered by process automation and AI, ClearPoint helps organizations achieve their goals faster and more efficiently from one centralized platform.

QA.tech is an advanced end-to-end testing application designed for B2B SaaS companies. It offers AI-powered testing solutions to help businesses ship faster, cut costs, and improve testing efficiency. The application features an AI agent named Jarvis that automates the testing process by scanning web apps, creating detailed memory structures, generating tests based on user interactions, and continuously testing for defects. QA.tech provides developer-friendly bug reports, supports various web frameworks, and integrates with CI/CD pipelines. It aims to revolutionize the testing process by offering faster, smarter, and more efficient testing solutions.

HostAI is a platform that allows users to host their artificial intelligence models and applications with ease. It provides a user-friendly interface for managing and deploying AI projects, eliminating the need for complex server setups. With HostAI, users can seamlessly run their AI algorithms and applications in a secure and efficient environment. The platform supports various AI frameworks and libraries, making it versatile for different AI projects. HostAI simplifies the process of AI deployment, enabling users to focus on developing and improving their AI models.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Arbigent (Arbiter-Agent) is an AI agent testing framework designed to make AI agent testing practical for modern applications. It addresses challenges faced by traditional UI testing frameworks and AI agents by breaking down complex tasks into smaller, dependent scenarios. The framework is customizable for various AI providers, operating systems, and form factors, empowering users with extensive customization capabilities. Arbigent offers an intuitive UI for scenario creation and a powerful code interface for seamless test execution. It supports multiple form factors, optimizes UI for AI interaction, and is cost-effective by utilizing models like GPT-4o mini. With a flexible code interface and open-source nature, Arbigent aims to revolutionize AI agent testing in modern applications.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

FastAgency is a powerful tool that leverages the AutoGen framework to quickly build applications with multi-agent workflows. It supports various interfaces like ConsoleUI and MesopUI, allowing users to create interactive applications. The tool enables defining workflows between agents, such as students and teachers, and summarizing conversations. FastAgency aims to expand its capabilities by integrating with additional agentic frameworks like CrewAI, providing more options for workflow definition and AI tool integration.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

HumanLayer is a Python toolkit designed to enable AI agents to interact with humans in tool-based and asynchronous workflows. By incorporating humans-in-the-loop, agentic tools can access more powerful and meaningful tasks. The toolkit provides features like requiring human approval for function calls, human as a tool for contacting humans, omni-channel contact capabilities, granular routing, and support for various LLMs and orchestration frameworks. HumanLayer aims to ensure human oversight of high-stakes function calls, making AI agents more reliable and safe in executing impactful tasks.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.

The 'Awesome AI Guidelines' repository aims to simplify the ecosystem of guidelines, principles, codes of ethics, standards, and regulations around artificial intelligence. It provides a comprehensive collection of resources addressing ethical and societal challenges in AI systems, including high-level frameworks, principles, processes, checklists, interactive tools, industry standards initiatives, online courses, research, and industry newsletters, as well as regulations and policies from various countries. The repository serves as a valuable reference for individuals and teams designing, building, and operating AI systems to navigate the complex landscape of AI ethics and governance.

Phoenix is a tool that provides MLOps and LLMOps insights at lightning speed with zero-config observability. It offers a notebook-first experience for monitoring models and LLM Applications by providing LLM Traces, LLM Evals, Embedding Analysis, RAG Analysis, and Structured Data Analysis. Users can trace through the execution of LLM Applications, evaluate generative models, explore embedding point-clouds, visualize generative application's search and retrieval process, and statistically analyze structured data. Phoenix is designed to help users troubleshoot problems related to retrieval, tool execution, relevance, toxicity, drift, and performance degradation.

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.

The Stripe Agent Toolkit enables popular agent frameworks to integrate with Stripe APIs through function calling. It includes support for Python and TypeScript, built on top of Stripe Python and Node SDKs. The toolkit provides tools for LangChain, CrewAI, and Vercel's AI SDK, allowing users to configure actions like creating payment links, invoices, refunds, and more. Users can pass the toolkit as a list of tools to agents for integration with Stripe. Context values can be provided for making requests, such as specifying connected accounts for API calls. The toolkit also supports metered billing for Vercel's AI SDK, enabling billing events submission based on customer ID and input/output meters.

Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator support including CUDA GPU, ROCm GPU, TPU, IPU and other NPUs. It allocates and isolates the underlying computing resources for multi-tenant computation sessions on-demand or in batches with customizable job schedulers with its own orchestrator. All its functions are exposed as REST/GraphQL/WebSocket APIs.

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

Godot RL Agents is an open-source package that facilitates the integration of Machine Learning algorithms with games created in the Godot Engine. It provides interfaces for popular RL frameworks, support for memory-based agents, 2D and 3D games, AI sensors, and is licensed under MIT. Users can train agents in the Godot editor, create custom environments, export trained agents in ONNX format, and utilize advanced features like different RL training frameworks.

FlagPerf is an integrated AI hardware evaluation engine jointly built by the Institute of Intelligence and AI hardware manufacturers. It aims to establish an industry-oriented metric system to evaluate the actual capabilities of AI hardware under software stack combinations (model + framework + compiler). FlagPerf features a multidimensional evaluation metric system that goes beyond just measuring 'whether the chip can support specific model training.' It covers various scenarios and tasks, including computer vision, natural language processing, speech, multimodal, with support for multiple training frameworks and inference engines to connect AI hardware with software ecosystems. It also supports various testing environments to comprehensively assess the performance of domestic AI chips in different scenarios.

Twinny is a free and open-source AI code completion plugin for Visual Studio Code and compatible editors. It integrates with various tools and frameworks, including Ollama, llama.cpp, oobabooga/text-generation-webui, LM Studio, LiteLLM, and Open WebUI. Twinny offers features such as fill-in-the-middle code completion, chat with AI about your code, customizable API endpoints, and support for single or multiline fill-in-middle completions. It is easy to install via the Visual Studio Code extensions marketplace and provides a range of customization options. Twinny supports both online and offline operation and conforms to the OpenAI API standard.

Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. Katib can perform training jobs using any Kubernetes Custom Resources with out of the box support for Kubeflow Training Operator, Argo Workflows, Tekton Pipelines and many more.

Intel® Neural Compressor is an open-source Python library that supports popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as TensorFlow, PyTorch, ONNX Runtime, and MXNet. It provides key features, typical examples, and open collaborations, including support for a wide range of Intel hardware, validation of popular LLMs, and collaboration with cloud marketplaces, software platforms, and open AI ecosystems.

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RISEN Prompt Engineer GPT streamlines AI interactions by applying the RISEN framework, focusing on specific, structured prompts for targeted, creative responses. This method improves the precision of AI-generated answers, ensuring they meet user expectations effectively.

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