We present a green agent framework for empirical game-theoretic evaluation of bargaining agents in multi-round negotiation scenarios with subjectively valued items. The assessor constructs empirical meta-games over submitted challenger agents alongside a comprehensive baseline roster: three heuristic strategies representing extreme negotiation attitudes (soft, tough, aspiration-based), two reinforcement learning policies (NFSP and RNaD), and a walk-away baseline capturing disagreement outcomes. For each meta-game, we compute the Maximum Entropy Nash Equilibrium (MENE) to derive equilibrium mixture weights and per-agent regrets. Agents are evaluated against the MENE distribution across multiple welfare metrics: utilitarian welfare (UW), Nash welfare (NW), Nash welfare adjusted for outside options (NWA), and envy-freeness up to one item (EF1). Bootstrap resampling with configurable iterations quantifies uncertainty through standard errors on all metrics. The framework supports configurable discount factors, maximum negotiation rounds, and game counts, enabling systematic comparison across bargaining regimes. By providing a pre-trained RL baselines and established heuristic opponents, this assessor facilitates benchmarking of LLM-based and algorithmic negotiation strategies, supporting research into AI behavior in mixed-motive economic settings.