Solving complex reasoning tasks is a key real-world application of agents. Thanks to the pretraining of Large Language Models (LLMs) on code data, recent approaches like CodeAct successfully use code as LLM agents’ action, achieving good results. However, CodeAct greedily generates the next action’s code block by relying on fragmented thoughts, resulting in inconsistency and accumulative hallucination. Moreover, CodeAct lacks action-related ground-truth (GT), making its supervision signals and termination conditions questionable in multi-turn interactions. To address these issues, we propose Tree-of-Code (ToC), a self-growing framework that generates nodes through self-supervision, incorporating prompt and model exploration in a GT-free setting. Each node employs CodeProgram, an end-to-end code generation paradigm that aligns executable code logic with global reasoning. This approach uses task-level execution success as both node validity and stop-growing flags, bypassing process supervision to enable online applications. Experiments on two datasets with ten popular zero-shot LLMs show that ToC boosts accuracy by nearly 20% over CodeAct with fewer than 1/4 turns. To further investigate the trade-off between efficacy and efficiency, ablation studies on different ToC tree sizes and exploration mechanisms validate ToC’s superiority.