AgentInteraction is a developer-focused Python framework designed to simulate, coordinate, and evaluate multi-agent interactions using large language models. It allows users to define distinct agent roles, control conversational flow through a central manager, and integrate any LLM provider via a consistent API. With features like message routing, context management, and performance analytics, AgentInteraction streamlines experimentation with collaborative or competitive agent architectures, making it easy to prototype complex dialogue scenarios and measure success rates.

Cohere is an AI-powered platform designed for natural language processing, enabling users to easily create, analyze, and understand text. With its state-of-the-art models, Cohere facilitates tasks such as text generation, semantic search, and document analysis. Businesses can integrate these capabilities into their applications, helping them enhance customer interactions, derive insights from text data, and automate content creation. Cohere's API supports seamless integration with various applications, ensuring flexibility and scalability.

This project demonstrates a comprehensive framework for creating retrieval-augmented AI agents using LlamaIndex. It guides developers through the entire workflow, starting with document ingestion and vector store creation, followed by defining a custom agent loop for contextual question-answering. Leveraging LlamaIndex's powerful indexing and retrieval capabilities, users can integrate any OpenAI-compatible language model, customize prompt templates, and manage conversation flows via a CLI interface. The modular architecture supports various data connectors, plugin extensions, and dynamic response customization, enabling rapid prototyping of enterprise-grade knowledge assistants, interactive chatbots, and research tools. This solution streamlines building domain-specific AI agents in Python, ensuring scalability, flexibility, and ease of integration.