OpenShield is a firewall designed for AI models to protect against various attacks such as prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency granting, overreliance, and model theft. It provides rate limiting, content filtering, and keyword filtering for AI models. The tool acts as a transparent proxy between AI models and clients, allowing users to set custom rate limits for OpenAI endpoints and perform tokenizer calculations for OpenAI models. OpenShield also supports Python and LLM based rules, with upcoming features including rate limiting per user and model, prompts manager, content filtering, keyword filtering based on LLM/Vector models, OpenMeter integration, and VectorDB integration. The tool requires an OpenAI API key, Postgres, and Redis for operation.

ARTKIT is a Python framework developed by BCG X for automating prompt-based testing and evaluation of Gen AI applications. It allows users to develop automated end-to-end testing and evaluation pipelines for Gen AI systems, supporting multi-turn conversations and various testing scenarios like Q&A accuracy, brand values, equitability, safety, and security. The framework provides a simple API, asynchronous processing, caching, model agnostic support, end-to-end pipelines, multi-turn conversations, robust data flows, and visualizations. ARTKIT is designed for customization by data scientists and engineers to enhance human-in-the-loop testing and evaluation, emphasizing the importance of tailored testing for each Gen AI use case.

PremSQL is an open-source library designed to help developers create secure, fully local Text-to-SQL solutions using small language models. It provides essential tools for building and deploying end-to-end Text-to-SQL pipelines with customizable components, ideal for secure, autonomous AI-powered data analysis. The library offers features like Local-First approach, Customizable Datasets, Robust Executors and Evaluators, Advanced Generators, Error Handling and Self-Correction, Fine-Tuning Support, and End-to-End Pipelines. Users can fine-tune models, generate SQL queries from natural language inputs, handle errors, and evaluate model performance against predefined metrics. PremSQL is extendible for customization and private data usage.

Open Deep Research is an open-source tool designed to generate AI-powered reports from web search results efficiently. It combines Bing Search API for search results retrieval, JinaAI for content extraction, and customizable report generation. Users can customize settings, export reports in multiple formats, and benefit from rate limiting for stability. The tool aims to streamline research and report creation in a user-friendly platform.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project caches pre-generated model results to reduce response time for similar requests and enhance user experience. It integrates various embedding frameworks and local storage options, offering functionalities like cache-writing, cache-querying, and cache-clearing through RESTful API. The tool supports multi-tenancy, system commands, and multi-turn dialogue, with features for data isolation, database management, and model loading schemes. Future developments include data isolation based on hyperparameters, enhanced system prompt partitioning storage, and more versatile embedding models and similarity evaluation algorithms.

tinyllm is a lightweight framework designed for developing, debugging, and monitoring LLM and Agent powered applications at scale. It aims to simplify code while enabling users to create complex agents or LLM workflows in production. The core classes, Function and FunctionStream, standardize and control LLM, ToolStore, and relevant calls for scalable production use. It offers structured handling of function execution, including input/output validation, error handling, evaluation, and more, all while maintaining code readability. Users can create chains with prompts, LLM models, and evaluators in a single file without the need for extensive class definitions or spaghetti code. Additionally, tinyllm integrates with various libraries like Langfuse and provides tools for prompt engineering, observability, logging, and finite state machine design.

Neuron AI is a PHP framework that provides an Agent class for creating fully functional agents to perform tasks like analyzing text for SEO optimization. The framework manages advanced mechanisms such as memory, tools, and function calls. Users can extend the Agent class to create custom agents and interact with them to get responses based on the underlying LLM. Neuron AI aims to simplify the development of AI-powered applications by offering a structured framework with documentation and guidelines for contributions under the MIT license.

Neuron is a PHP framework for creating and orchestrating AI Agents, providing tools for the entire agentic application development lifecycle. It allows integration of AI entities in existing PHP applications with a powerful and flexible architecture. Neuron offers tutorials and educational content to help users get started using AI Agents in their projects. The framework supports various LLM providers, tools, and toolkits, enabling users to create fully functional agents for tasks like data analysis, chatbots, and structured output. Neuron also facilitates monitoring and debugging of AI applications, ensuring control over agent behavior and decision-making processes.

Instructor for PHP is a library designed for structured data extraction in PHP, powered by Large Language Models (LLMs). It simplifies the process of extracting structured, validated data from unstructured text or chat sequences. Instructor enhances workflow by providing a response model, validation capabilities, and max retries for requests. It supports classes as response models and provides features like partial results, string input, extracting scalar and enum values, and specifying data models using PHP type hints or DocBlock comments. The library allows customization of validation and provides detailed event notifications during request processing. Instructor is compatible with PHP 8.2+ and leverages PHP reflection, Symfony components, and SaloonPHP for communication with LLM API providers.

Nous is an open-source TypeScript platform for autonomous AI agents and LLM based workflows. It aims to automate processes, support requests, review code, assist with refactorings, and more. The platform supports various integrations, multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It offers advanced features like reasoning/planning, memory and function call history, hierarchical task decomposition, and control-loop function calling options. Nous is designed to be a flexible platform for the TypeScript community to expand and support different use cases and integrations.

LLM-on-Ray is a comprehensive solution for building, customizing, and deploying Large Language Models (LLMs). It simplifies complex processes into manageable steps by leveraging the power of Ray for distributed computing. The tool supports pretraining, finetuning, and serving LLMs across various hardware setups, incorporating industry and Intel optimizations for performance. It offers modular workflows with intuitive configurations, robust fault tolerance, and scalability. Additionally, it provides an Interactive Web UI for enhanced usability, including a chatbot application for testing and refining models.

Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

Vercel AI Provider for running Large Language Models locally using Ollama. This module is under development and may contain errors and frequent incompatible changes. It provides the capability of generating and streaming text and objects, with features like image input, object generation, tool usage simulation, tool streaming simulation, intercepting fetch requests, and provider management. The provider can be customized with optional settings like baseURL and headers.

GuideLLM is a platform for evaluating and optimizing the deployment of large language models (LLMs). By simulating real-world inference workloads, GuideLLM enables users to assess the performance, resource requirements, and cost implications of deploying LLMs on various hardware configurations. This approach ensures efficient, scalable, and cost-effective LLM inference serving while maintaining high service quality. The tool provides features for performance evaluation, resource optimization, cost estimation, and scalability testing.

Atropos is a robust and scalable framework for Reinforcement Learning Environments with Large Language Models (LLMs). It provides a flexible platform to accelerate LLM-based RL research across diverse interactive settings. Atropos supports multi-turn and asynchronous RL interactions, integrates with various inference APIs, offers a standardized training interface for experimenting with different RL algorithms, and allows for easy scalability by launching more environment instances. The framework manages diverse environment types concurrently for heterogeneous, multi-modal training.

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.

OpenShield is a firewall designed for AI models to protect against various attacks such as prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency granting, overreliance, and model theft. It provides rate limiting, content filtering, and keyword filtering for AI models. The tool acts as a transparent proxy between AI models and clients, allowing users to set custom rate limits for OpenAI endpoints and perform tokenizer calculations for OpenAI models. OpenShield also supports Python and LLM based rules, with upcoming features including rate limiting per user and model, prompts manager, content filtering, keyword filtering based on LLM/Vector models, OpenMeter integration, and VectorDB integration. The tool requires an OpenAI API key, Postgres, and Redis for operation.

ZenGuard AI enables AI developers to integrate production-level, low-code LLM (Large Language Model) guardrails into their generative AI applications effortlessly. With ZenGuard AI, ensure your application operates within trusted boundaries, is protected from prompt injections, and maintains user privacy without compromising on performance.

Auto-News is an automatic news aggregator tool that utilizes Large Language Models (LLM) to pull information from various sources such as Tweets, RSS feeds, YouTube videos, web articles, Reddit, and journal notes. The tool aims to help users efficiently read and filter content based on personal interests, providing a unified reading experience and organizing information effectively. It features feed aggregation with summarization, transcript generation for videos and articles, noise reduction, task organization, and deep dive topic exploration. The tool supports multiple LLM backends, offers weekly top-k aggregations, and can be deployed on Linux/MacOS using docker-compose or Kubernetes.

RSSBrew is a self-hosted RSS tool designed for aggregating multiple RSS feeds, applying custom filters, and generating AI summaries. It allows users to control content through custom filters based on Link, Title, and Description, with various match types and relationship operators. Users can easily combine multiple feeds into a single processed feed and use AI for article summarization and digest creation. The tool supports Docker deployment and regular installation, with ongoing documentation and development. Licensed under AGPL-3.0, RSSBrew is a versatile tool for managing and summarizing RSS content.

AIO-Firebog-Blocklists is a comprehensive tool that combines various sources into a single, cohesive blocklist. It offers customizable options to suit individual preferences and needs, ensuring regular updates to stay up-to-date with the latest threats. The tool focuses on performance optimization to minimize impact while maintaining effective filtering. It is designed to help users with ad blocking, malware protection, tracker prevention, and content filtering.

Zhihu++ is a third-party client for Zhihu that focuses on privacy, individual internet rights, and ad-free experience. It features a unique local recommendation algorithm that provides and filters high-quality content locally. The algorithm is independent of Zhihu's algorithm, runs on web crawlers, and allows customization of recommendation weights. The project aims to help users reclaim their right to choose their own content and not be enslaved by algorithms. It supports various recommendation solutions for mobile/web platforms and offers features like setting block words, AI answer blocking, user blocking, and topic blocking. Zhihu++ is not a traditional free software, see the licensing agreement for details.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

Kuwa GenAI OS is an open, free, secure, and privacy-focused Generative-AI Operating System. It provides a multi-lingual turnkey solution for GenAI development and deployment on Linux and Windows. Users can enjoy features such as concurrent multi-chat, quoting, full prompt-list import/export/share, and flexible orchestration of prompts, RAGs, bots, models, and hardware/GPUs. The system supports various environments from virtual hosts to cloud, and it is open source, allowing developers to contribute and customize according to their needs.

Ludwig is a declarative deep learning framework designed for scale and efficiency. It is a low-code framework that allows users to build custom AI models like LLMs and other deep neural networks with ease. Ludwig offers features such as optimized scale and efficiency, expert level control, modularity, and extensibility. It is engineered for production with prebuilt Docker containers, support for running with Ray on Kubernetes, and the ability to export models to Torchscript and Triton. Ludwig is hosted by the Linux Foundation AI & Data.

Wenda is a platform for large-scale language model invocation designed to efficiently generate content for specific environments, considering the limitations of personal and small business computing resources, as well as knowledge security and privacy issues. The platform integrates capabilities such as knowledge base integration, multiple large language models for offline deployment, auto scripts for additional functionality, and other practical capabilities like conversation history management and multi-user simultaneous usage.

LLMonFHIR is an iOS application that utilizes large language models (LLMs) to interpret and provide context around patient data in the Fast Healthcare Interoperability Resources (FHIR) format. It connects to the OpenAI GPT API to analyze FHIR resources, supports multiple languages, and allows users to interact with their health data stored in the Apple Health app. The app aims to simplify complex health records, provide insights, and facilitate deeper understanding through a conversational interface. However, it is an experimental app for informational purposes only and should not be used as a substitute for professional medical advice. Users are advised to verify information provided by AI models and consult healthcare professionals for personalized advice.

Chinese-Mixtral-8x7B is an open-source project based on Mistral's Mixtral-8x7B model for incremental pre-training of Chinese vocabulary, aiming to advance research on MoE models in the Chinese natural language processing community. The expanded vocabulary significantly improves the model's encoding and decoding efficiency for Chinese, and the model is pre-trained incrementally on a large-scale open-source corpus, enabling it with powerful Chinese generation and comprehension capabilities. The project includes a large model with expanded Chinese vocabulary and incremental pre-training code.

AI-Horde-Worker is a repository containing the original reference implementation for a worker that turns your graphics card(s) into a worker for the AI Horde. It allows users to generate or alchemize images for others. The repository provides instructions for setting up the worker on Windows and Linux, updating the worker code, running with multiple GPUs, and stopping the worker. Users can configure the worker using a WebUI to connect to the horde with their username and API key. The repository also includes information on model usage and running the Docker container with specified environment variables.

OpenShield is a firewall designed for AI models to protect against various attacks such as prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency granting, overreliance, and model theft. It provides rate limiting, content filtering, and keyword filtering for AI models. The tool acts as a transparent proxy between AI models and clients, allowing users to set custom rate limits for OpenAI endpoints and perform tokenizer calculations for OpenAI models. OpenShield also supports Python and LLM based rules, with upcoming features including rate limiting per user and model, prompts manager, content filtering, keyword filtering based on LLM/Vector models, OpenMeter integration, and VectorDB integration. The tool requires an OpenAI API key, Postgres, and Redis for operation.

VoAPI is a new high-value/high-performance AI model interface management and distribution system. It is a closed-source tool for personal learning use only, not for commercial purposes. Users must comply with upstream AI model service providers and legal regulations. The system offers a visually appealing interface, independent development documentation page support, service monitoring page configuration support, and third-party login support. It also optimizes interface elements, user registration time support, data operation button positioning, and more.

VoAPI is a new high-value/high-performance AI model interface management and distribution system. It is a closed-source tool for personal learning use only, not for commercial purposes. Users must comply with upstream AI model service providers and legal regulations. The system offers a visually appealing interface with features such as independent development documentation page support, service monitoring page configuration support, and third-party login support. Users can manage user registration time, optimize interface elements, and support features like online recharge, model pricing display, and sensitive word filtering. VoAPI also provides support for various AI models and platforms, with the ability to configure homepage templates, model information, and manufacturer information.