enhance_llm provides a modular framework for orchestrating large language model calls in defined sequences, allowing developers to chain prompts, integrate external tools or APIs, manage conversational context, and implement conditional logic. It supports multiple LLM providers, custom prompt templates, asynchronous execution, error handling, and memory management. By abstracting the boilerplate of LLM interaction, enhance_llm streamlines the development of agent-like applications—such as automated assistants, data processing bots, and multi-step reasoning systems—making it easier to build, debug, and extend sophisticated workflows.

Team Coordination is a lightweight Python library designed to simplify the orchestration of multiple AI agents working together on complex tasks. By defining specialized agent roles—such as planners, executors, evaluators, or communicators—users can decompose a high-level objective into manageable sub-tasks, delegate them to individual agents, and facilitate structured communication between them. The framework handles asynchronous execution, protocol routing, and result aggregation, allowing teams of AI agents to collaborate efficiently. Its plugin system supports integration with popular LLMs, APIs, and custom logic, making it ideal for applications in automated customer service, research, game AI, and data processing pipelines. With clear abstractions and extensible components, Team Coordination accelerates the development of scalable multi-agent workflows.