We introduce Actions, a framework and programming environment to facilitate the implementation of tool-augmented language models (LMs). Concretely, we augment LMs with the ability to call actions (arbitrary Python functions), and experiment with different ways of tool discovery and invocation. We find that, while previous works heavily rely on few-shot prompting to teach tool use, a zero-shot, instruction-only approach is enough to achieve competitive performance. At the same time, Actions zero-shot approach also offers a much simpler programming interface, not requiring any involved demonstrations. Building on this, we show how Actions enables LLMs to automatically discover and combine multiple tools to solve complex tasks. Overall, we find that inline tool use as enabled by Actions, outperforms existing tool augmentation approaches, both in arithmetic reasoning tasks and text-based question answering. Our implementation extends the open source LMQL programming language for LM interaction and is available at ANONYMIZED (upon publication).