  Open large language models (LLMs) with great performance in various tasks
have significantly advanced the development of LLMs. However, they are far
inferior to commercial models such as ChatGPT and GPT-4 when acting as agents
to tackle complex tasks in the real world. These agent tasks employ LLMs as the
central controller responsible for planning, memorization, and tool
utilization, necessitating both fine-grained prompting methods and robust LLMs
to achieve satisfactory performance. Though many prompting methods have been
proposed to complete particular agent tasks, there is lack of research focusing
on improving the agent capabilities of LLMs themselves without compromising
their general abilities. In this work, we present AgentTuning, a simple and
general method to enhance the agent abilities of LLMs while maintaining their
general LLM capabilities. We construct AgentInstruct, a lightweight
instruction-tuning dataset containing high-quality interaction trajectories. We
employ a hybrid instruction-tuning strategy by combining AgentInstruct with
open-source instructions from general domains. AgentTuning is used to
instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show
that AgentTuning enables LLMs' agent capabilities without compromising general
abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent
tasks, demonstrating generalized agent capabilities. We open source the
AgentInstruct and AgentLM-7B, 13B, and 70B models at
https://github.com/THUDM/AgentTuning, serving open and powerful alternatives to
commercial LLMs for agent tasks.
