Flock provides a developer-friendly, modular framework for chaining multiple LLM calls, managing conversational memory, and integrating external tools into autonomous agents. With support for asynchronous execution and plugin extensions, Flock enables fine-grained control over agent behaviors, triggers, and context handling. It works seamlessly in Node.js and browser environments, letting teams rapidly prototype chatbots, data-processing workflows, virtual assistants, and other AI-driven automation solutions.

Wavify is a platform for on-device speech AI that allows software engineers to embed speech recognition, wake word detection, and other voice functionalities into their applications. With state-of-the-art models and cross-platform support, Wavify ensures high performance and privacy, as the data never leaves the device. It supports over 20 languages and works across various operating systems, making it versatile and accessible for different tech stacks.

AIMLAPI is a platform that provides access to over 100 advanced AI models through a single, unified API. The platform is designed to deliver low latency and high scalability, and it allows developers to seamlessly integrate various AI functionalities into their applications. With AIMLAPI, you can save up to 80% compared to other AI service providers like OpenAI, making it a cost-effective and efficient solution for leveraging state-of-the-art AI technologies.

LiteLLM is a comprehensive framework designed to streamline the management of multiple large language models (LLMs) through a unified API. By offering a standardized interaction model similar to OpenAI’s API, users can easily leverage over 100 different LLMs without dealing with diverse formats and protocols. LiteLLM handles complexities like load balancing, fallbacks, and spending tracking across different service providers, making it easier for developers to integrate and manage various LLM services in their applications.

ChainStream is a cross-platform mobile and desktop inference framework that streams partial outputs from large language models in real time. It breaks LLM inference into submodel chains, enabling incremental token delivery and reducing perceived latency. Developers can integrate ChainStream into their apps using a simple C++ API, select preferred backends like ONNX Runtime or TFLite, and customize pipeline stages. It runs on Android, iOS, Windows, Linux, and macOS, allowing for truly on-device AI-driven chat, translation, and assistant features without server dependencies.