Behavior trees (BTs) are widely adopted in the field of agent control, particularly in robotics, due to their modularity and reactivity. However, constructing a BT that meets the desired expectations is time-consuming and challenging, especially for non-expert users. This paper presents BtBot, a multi-modal sketch-based behavior tree synthesis technique. Given a natural language task description and a set of positive and negative examples, BtBot automatically generates a BT program that aligns with the natural language description and meets the requirements of the examples. Inside BtBot, an LLM is employed to understand the task’s natural language description and generate a sketch of the task execution. Then, BtBot searches the sketch to synthesize a candidate BT program consistent with the user-provided positive and negative examples. When the sketch is proven impossible to generate the target BT, BtBot provides a multi-step repairing method that modifies the sketch’s control nodes and structure to search for the target BT. We have implemented BtBot in a prototype and evaluated it on a benchmark of 70 tasks across multiple scenarios. The experimental results indicate that BtBot outperforms the existing BT synthesis techniques in effectiveness and efficiency. Besides, an user study is conducted to demonstrate BtBot’s usefulness.