Castorice-LLM-Service provides a standardized HTTP interface to interact with various large language model providers out of the box. Developers can configure multiple backends—including cloud APIs and self-hosted models—via environment variables or config files. It supports retrieval-augmented generation through seamless vector database integration, enabling context-aware responses. Features such as request batching optimize throughput and cost, while streaming endpoints deliver token-by-token responses. Built-in caching, RBAC, and Prometheus-compatible metrics help ensure secure, scalable, and observable deployment on-premises or in the cloud.

ChatStreamAiAgent provides developers with a lightweight Python toolkit to implement AI chat agents that stream token outputs as they are generated. It supports multiple LLM providers, asynchronous event hooks, and easy integration into web or console applications. With built-in context management and prompt templating, teams can rapidly prototype conversational assistants, customer support bots, or interactive tutorials while delivering low-latency, real-time responses.

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.