We present SmartMem Green Agent, an automated evaluation framework for assessing large language model (LLM) agents in smart home control scenarios. Our benchmark evaluates agents across multiple cognitive dimensions: (1) instruction grounding — mapping natural language commands to device-specific actions; (2) state reasoning — querying and interpreting device states to generate accurate responses; (3) episodic memory — retaining and retrieving user preferences across extended interaction sequences; and (4) multi-turn dialogue management — maintaining coherent task execution over multiple conversational exchanges. The evaluation pipeline employs a simulated smart home environment with heterogeneous IoT devices (lighting, climate control, audio systems, security) and measures both action-level accuracy and final state correctness. Our framework enables systematic benchmarking of memory-augmented LLM agents under realistic, multi-step task conditions.