This repository is a monorepo for NPM packages published under the `@elevenlabs` scope. It contains multiple packages in the `packages` folder. The setup allows for easy development, linking packages, creating new packages, and publishing them with GitHub actions.

Cortex.cpp is an open-source platform designed as the brain for robots, offering functionalities such as vision, speech, language, tabular data processing, and action. It provides an AI platform for running AI models with multi-engine support, hardware optimization with automatic GPU detection, and an OpenAI-compatible API. Users can download models from the Hugging Face model hub, run models, manage resources, and access advanced features like multiple quantizations and engine management. The tool is under active development, promising rapid improvements for users.

Buster is a modern analytics platform designed with AI in mind, focusing on self-serve experiences powered by Large Language Models. It addresses pain points in existing tools by advocating for AI-centric app development, cost-effective data warehousing, improved CI/CD processes, and empowering data teams to create powerful, user-friendly data experiences. The platform aims to revolutionize AI analytics by enabling data teams to build deep integrations and own their entire analytics stack.

WhatsApp Group Summary Bot is an AI-powered tool that joins WhatsApp groups, tracks conversations, and generates intelligent summaries. It features automated group chat responses, LLM-based conversation summaries, knowledge base integration, persistent message history with PostgreSQL, support for multiple message types, group management, and a REST API with Swagger docs. Prerequisites include Docker, Python 3.12+, PostgreSQL with pgvector extension, Voyage AI API key, and a WhatsApp account for the bot. The tool can be quickly set up by cloning the repository, configuring environment variables, starting services, and connecting devices. It offers API usage for loading new knowledge base topics and generating & dispatching summaries to managed groups. The project architecture includes FastAPI backend, WhatsApp Web API client, PostgreSQL database with vector storage, and AI-powered message processing.

The MCP Server for Apache Spark History Server is a tool that connects AI agents to Apache Spark History Server for intelligent job analysis and performance monitoring. It enables AI agents to analyze job performance, identify bottlenecks, and provide insights from Spark History Server data. The server bridges AI agents with existing Apache Spark infrastructure, allowing users to query job details, analyze performance metrics, compare multiple jobs, investigate failures, and generate insights from historical execution data.

Terminator is an AI-powered desktop automation tool that is open source, MIT-licensed, and cross-platform. It works across all apps and browsers, inspired by GitHub Actions & Playwright. It is 100x faster than generic AI agents, with over 95% success rate and no vendor lock-in. Users can create automations that work across any desktop app or browser, achieve high success rates without costly consultant armies, and pre-train workflows as deterministic code.

README-AI is a developer tool that auto-generates README.md files using a combination of data extraction and generative AI. It streamlines documentation creation and maintenance, enhancing developer productivity. This project aims to enable all skill levels, across all domains, to better understand, use, and contribute to open-source software. It offers flexible README generation, supports multiple large language models (LLMs), provides customizable output options, works with various programming languages and project types, and includes an offline mode for generating boilerplate README files without external API calls.

GPT-Load is a high-performance, enterprise-grade AI API transparent proxy service designed for enterprises and developers needing to integrate multiple AI services. Built with Go, it features intelligent key management, load balancing, and comprehensive monitoring capabilities for high-concurrency production environments. The tool serves as a transparent proxy service, preserving native API formats of various AI service providers like OpenAI, Google Gemini, and Anthropic Claude. It supports dynamic configuration, distributed leader-follower deployment, and a Vue 3-based web management interface. GPT-Load is production-ready with features like dual authentication, graceful shutdown, and error recovery.

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.

Gollama is a delightful tool that brings Ollama, your offline conversational AI companion, directly into your terminal. It provides a fun and interactive way to generate responses from various models without needing internet connectivity. Whether you're brainstorming ideas, exploring creative writing, or just looking for inspiration, Gollama is here to assist you. The tool offers an interactive interface, customizable prompts, multiple models selection, and visual feedback to enhance user experience. It can be installed via different methods like downloading the latest release, using Go, running with Docker, or building from source. Users can interact with Gollama through various options like specifying a custom base URL, prompt, model, and enabling raw output mode. The tool supports different modes like interactive, piped, CLI with image, and TUI with image. Gollama relies on third-party packages like bubbletea, glamour, huh, and lipgloss. The roadmap includes implementing piped mode, support for extracting codeblocks, copying responses/codeblocks to clipboard, GitHub Actions for automated releases, and downloading models directly from Ollama using the rest API. Contributions are welcome, and the project is licensed under the MIT License.

ZCF (Zero-Config Claude-Code Flow) is a tool that provides zero-configuration, one-click setup for Claude Code with bilingual support, intelligent agent system, and personalized AI assistant. It offers an interactive menu for easy operations and direct commands for quick execution. The tool supports bilingual operation with automatic language switching and customizable AI output styles. ZCF also includes features like BMad Workflow for enterprise-grade workflow system, Spec Workflow for structured feature development, CCR (Claude Code Router) support for proxy routing, and CCometixLine for real-time usage tracking. It provides smart installation, complete configuration management, and core features like professional agents, command system, and smart configuration. ZCF is cross-platform compatible, supports Windows and Termux environments, and includes security features like dangerous operation confirmation mechanism.

Cortex is a C++ AI engine with a Docker-like command-line interface and client libraries. It supports running AI models using ONNX, TensorRT-LLM, and llama.cpp engines. Cortex can function as a standalone server or be integrated as a library. The tool provides support for various engines and models, allowing users to easily deploy and interact with AI models. It offers a range of CLI commands for managing models, embeddings, and engines, as well as a REST API for interacting with models. Cortex is designed to simplify the deployment and usage of AI models in C++ applications.

Auto Engineer is a tool designed to automate the Software Development Life Cycle (SDLC) by building production-grade applications with a combination of human and AI agents. It offers a plugin-based architecture that allows users to install only the necessary functionality for their projects. The tool guides users through key stages including Flow Modeling, IA Generation, Deterministic Scaffolding, AI Coding & Testing Loop, and Comprehensive Quality Checks. Auto Engineer follows a command/event-driven architecture and provides a modular plugin system for specific functionalities. It supports TypeScript with strict typing throughout and includes a built-in message bus server with a web dashboard for monitoring commands and events.

MooER (摩耳) is an LLM-based speech recognition and translation model developed by Moore Threads. It allows users to transcribe speech into text (ASR) and translate speech into other languages (AST) in an end-to-end manner. The model was trained using 5K hours of data and is now also available with an 80K hours version. MooER is the first LLM-based speech model trained and inferred using domestic GPUs. The repository includes pretrained models, inference code, and a Gradio demo for a better user experience.

TunaCode CLI is an AI-powered coding assistant that provides a command-line interface for developers to enhance their coding experience. It offers features like model selection, parallel execution for faster file operations, and various commands for code management. The tool aims to improve coding efficiency and provide a seamless coding environment for developers.

ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference. It aims to offer simple, high-performance ML inference and a good developer experience. Users can provide inference APIs for ONNX models without writing additional code by placing the models in the directory structure. Each session can choose between CPU or CUDA, analyze input/output, and provide Swagger API documentation for easy testing. Ready-to-run Docker images are available, making it convenient to deploy the server.

This repository is a monorepo for NPM packages published under the `@elevenlabs` scope. It contains multiple packages in the `packages` folder. The setup allows for easy development, linking packages, creating new packages, and publishing them with GitHub actions.

Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

The Mistral Python Client is a tool inspired by cohere-python that allows users to interact with the Mistral AI API. It provides functionalities to access and utilize the AI capabilities offered by Mistral. Users can easily install the client using pip and manage dependencies using poetry. The client includes examples demonstrating how to use the API for various tasks, such as chat interactions. To get started, users need to obtain a Mistral API Key and set it as an environment variable. Overall, the Mistral Python Client simplifies the integration of Mistral AI services into Python applications.

ComposeAI is an Android & iOS application similar to ChatGPT, built using Compose Multiplatform. It utilizes various technologies such as Compose Multiplatform, Material 3, OpenAI Kotlin, Voyager, Koin, SQLDelight, Multiplatform Settings, Coil3, Napier, BuildKonfig, Firebase Analytics & Crashlytics, and AdMob. The app architecture follows Google's latest guidelines. Users need to set up their own OpenAI API key before using the app.

aiohttp-devtools provides dev tools for developing applications with aiohttp and associated libraries. It includes CLI commands for running a local server with live reloading and serving static files. The tools aim to simplify the development process by automating tasks such as setting up a new application and managing dependencies. Developers can easily create and run aiohttp applications, manage static files, and utilize live reloading for efficient development.

BitBom Minefield is a tool that uses roaring bit maps to graph Software Bill of Materials (SBOMs) with a focus on speed, air-gapped operation, scalability, and customizability. It is optimized for rapid data processing, operates securely in isolated environments, supports millions of nodes effortlessly, and allows users to extend the project without relying on upstream changes. The tool enables users to manage and explore software dependencies within isolated environments by offline processing and analyzing SBOMs.

Gez is a high-performance micro frontend framework based on ESM. It uses Rspack compilation and maps modules to URLs with strong caching and content-based hashing. Gez embraces modern micro frontend architecture by leveraging ESM and importmap for dependency management, providing reliable isolation with module scope, seamless integration with any modern frontend framework, intuitive development experience, and optimal performance with zero runtime overhead and reliable caching strategies.

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.

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

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Nvidia GPU exporter for prometheus, using `nvidia-smi` binary to gather metrics.

Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

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

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.