MeTTa-Motto is an interoperability layer designed to embed LLMs directly into the MeTTa runtime, effectively treating neural models as programmable functions within a symbolic workflow. It allows developers to compose prompts, chain LLM calls, and manage context seamlessly within MeTTa scripts, enabling a bidirectional flow where symbolic logic guides neural generation and neural outputs are parsed back into grounded atoms.
The library operates through a flexible Agent architecture, ranging from stateless model wrappers (e.g., ChatGPT, Claude) to stateful Dialogue Agents and Retrieval Agents (RAG). Crucially, MeTTa-Motto supports functional calling, empowering LLMs to autonomously recognize when to invoke specific MeTTa functions and extract arguments from natural language. With extensive integration for Python and LangChain, it serves as the essential tooling for building systems that combine the fluency of generative AI with the rigorous control of the Hyperon stack.
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