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While traditional XAI methods typically offer a small and technical set of explanation types, ECHO advances the accessibility and usability of AI explanations through a conversational approach, combining LLMs with a collection of tools and a human-in-the-loop process. We identify various explanation types from the literature, for which we create a set of predefined tools for tabular data. Using a modular architecture, ECHO integrates these predefined tools with dynamically generated tools to interact with AI models, facilitating tailored explanations for a large variety of user queries. 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