This paper presents LLaVA-Plus (Large Language and Vision Assistants that Plug and Learn to Use Skills), a general-purpose multimodal assistant trained using an end-to-end approach that systematically expands the capabilities of large multimodal models (LMMs). LLaVA-Plus maintains a skill repository that contains a wide range of vision and vision-language pre-trained models (tools), and is able to activate relevant tools, given users’ multimodal inputs, to compose their execution results on the fly to fulfill many real-world tasks. To acquire the ability of using tools, LLaVA-Plus is trained on multimodal instruction-following data that we have curated. The training data covers many tool use examples of visual understanding, generation, external knowledge retrieval and their compositions. Empirical results show that LLaVA-Plus outperforms LLaVA in existing capabilities, and exhibits many new capabilities. Compared with tool-augmented LLMs, LLaVA-Plus is distinct in that the image query is directly grounded in and actively engaged throughout the entire human-AI interaction sessions, significantly improving tool use performance and enabling new scenarios.