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We present ControlLLM, a novel framework that enables large language models(LLMs) to utilize multi-modal tools for solving complex real-world tasks.Despite the remarkable performance of LLMs, they still struggle with toolinvocation due to ambiguous user prompts, inaccurate tool selection andparameterization, and inefficient tool scheduling. To overcome thesechallenges, our framework comprises three key components: (1) a \textit{taskdecomposer} that breaks down a complex task into clear subtasks withwell-defined inputs and outputs; (2) a \textit{Thoughts-on-Graph (ToG)paradigm} that searches the optimal solution path on a pre-built tool graph,which specifies the parameter and dependency relations among different tools;and (3) an \textit{execution engine with a rich toolbox} that interprets thesolution path and runs the tools efficiently on different computationaldevices. We evaluate our framework on diverse tasks involving image, audio, andvideo processing, demonstrating its superior accuracy, efficiency, andversatility compared to existing methods. The code is athttps://github.com/OpenGVLab/ControlLLM.

ControlLLM: Augment Language Models with Tools by Searching on Graphs