TokenLivelihood: Studying the Economics of Self-Sustaining LLM Agents

In the token economy, agents are increasingly completing tasks autonomously, and the token consumption along this way is critical to keeping agents running sustainably, with agents constantly weighing cost against reward. However, existing work remains fragmented: budget-aware inference research treats the computational budget as a static constraint within a single task episode, while agent-based simulations typically assume fixed rewards without modeling the economic cycle of resource consumption and capital accumulation. To address these challenges, we introduce **TokenLivelihood**, a verifiable economic sandbox where computation serves as both production cost and economic capital. Agents choose and spend tokens to perform tasks dynamically, earn rewards from the market, and must sustain their operation solely through their accumulated earnings. The sandbox supports market regimes ranging from monopoly to competition, with prices tied to production costs and agents freely competing for tasks under resource constraints. Extensive experiments show that agents adapt their behavior to different economic environments. Overall, abundant capital encourages more aggressive investment decisions that may hinder long-term wealth accumulation, while intensified competition increasingly favors stronger models. Improvements in productive capability, in contrast, generate broad wealth gains by enabling agents to complete more valuable tasks across different market conditions.