Code translation transforms code between programming languages while preserving functionality, which is critical in software development and maintenance. While traditional learning-based code translation methods have limited effectiveness due to the lack of sufficient parallel training data, Large Language Models (LLMs) have recently advanced this field with their strong code generation and comprehension capabilities. However, code translated by LLMs still suffers from diverse quality issues, such as syntax and semantic errors. In this work, we propose \ourtool{}, a novel multi-agent system that eliminates the errors during LLM-based code translation. The main insight of \ourtool{} is to localize error-prone code blocks via fine-grained execution alignment between source and target code. We evaluate \ourtool{} on a newly constructed benchmark of recent programming tasks to mitigate data leakage. \ourtool{} outperforms the latest \unitrans{} by up to 33.3% in translation accuracy and achieves an average improvement of 56.7% over \agentless{} in program repair performance. We also conduct an ablation study and evaluate \ourtool{} across different LLMs, demonstrating its effectiveness and strong generalizability.