MCP-Ollama-Client provides a unified interface to communicate with Ollama’s language models running locally. It supports full-duplex multi-turn dialogues with automatic history tracking, live streaming of completion tokens, and dynamic prompt templates. Developers can choose between installed models, customize hyperparameters like temperature and max tokens, and monitor usage metrics directly in the terminal. The client exposes a simple REST-like API wrapper for integration into automation scripts or local applications. With built-in error reporting and configuration management, it streamlines the development and testing of LLM-powered workflows without relying on external APIs.

PulpGen provides a unified, configurable platform to build advanced LLM-based applications. It offers seamless integrations with popular vector stores, embedding services, and LLM providers. Developers can define custom pipelines for retrieval-augmented generation, enable real-time streaming outputs, batch process large document collections, and monitor system performance. Its extensible architecture allows plug-and-play modules for cache management, logging, and auto-scaling, making it ideal for AI-powered search, question-answering, summarization, and knowledge management solutions.

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.

Steel is a developer-centric framework designed to accelerate the creation and operation of LLM-powered agents in production environments. It offers provider-agnostic connectors for major model APIs, an in-memory and persistent memory store, built-in tool invocation patterns, automatic caching of responses, and detailed tracing for observability. Developers can define complex agent workflows, integrate custom tools (e.g., search, database queries, and external APIs), and handle streaming outputs. Steel abstracts the complexity of orchestration, allowing teams to focus on business logic and rapidly iterate on AI-driven applications.