Tsinghua University's latest research introduces the AgentTuning method to enhance LLM's agent capabilities. The AgentInstruct agent dataset has been constructed, containing high-quality interactive trajectories and CoT reasoning chains. A hybrid instruction fine-tuning strategy was adopted, using AgentInstruct in conjunction with general instructions. Results indicate that AgentLM outperforms Llama 2 significantly in both held-in and held-out tasks. The 70B version of AgentLM excels in agent tasks.