In my earlier posts, we first built a dev.bg job-search assistant by serving a local MLX model via mlx_lm.server, then using the OpenAI Python client to do tool calling. The client code owned the whole “tool loop”: call model → execute tool → append tool result → call model again, etc: A Job Postings Tool: A Guide to MLX-LM Server and Tool Use with the OpenAI Client. Later, we connected MLX to the Agno framework using theOpenAILike model adapter, and we saw how easy it is to give an Agno agent tools: Running Local Hugging Face Models with MLX-LM and the Agno Agentic Framework. In this post, we’ll rebuild the same agent with Agno and MLX, but this time we’ll focus on Human-in-the-Loop (HITL) control flow. Concretely, the agent can pause when it needs missing info, request that info via a structured user input (schema), continue from exactly where it left off.
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