llm-lab provides a flexible toolkit for creating intelligent agents using large language models. It includes an agent orchestration engine, support for custom prompt templates, memory and state tracking, and seamless integration with external APIs and plugins. Users can write scenarios, define toolchains, simulate interactions, and collect performance logs. The framework also offers a built-in testing suite to validate agent behavior against expected outcomes. Extensible by design, llm-lab enables developers to swap LLM providers, add new tools, and evolve agent logic through iterative experimentation.

MASChat provides a flexible framework for orchestrating conversations among multiple AI agents powered by language models. Developers can define agents with specific roles—such as researcher, summarizer, or critic—and specify their prompts, permissions, and communication protocols. MASChat’s central manager handles message routing, ensures context preservation, and logs interactions for traceability. By coordinating specialized agents, MASChat decomposes complex tasks—like research, content creation, or data analysis—into parallel workflows, improving efficiency and insight. It integrates with OpenAI’s GPT APIs or local LLMs and allows plugin extensions for custom behaviors. MASChat is ideal for prototyping multi-agent strategies, simulating collaborative environments, and exploring emergent behaviors in AI systems.