Deep learning has shown potential to enable automated personalized cancer treatment by automating radiotherapy treatment (RT) planning. However, generalizing RT planning across multiple protocols with deep learning remains a critical challenge due to the diversity of clinical requirements. This paper introduces Treat: a unified Text-guided Radiotherapy for dose prEdiction in Automated Treatment planning to address these complexities. By leveraging conditional text embeddings using the CLIP text-encoder, the model dynamically adapts to protocol-specific requirements, enabling the generation of high-quality per-protocol dose distributions. We propose an efficient text-conditioning method, graph prompts pooling (GPP), to effectively leverage multiple protocol-specific prompts, and dynamic batch weighting to balance the model training using multiple datasets. We validated Treat on five datasets–two early-stage prostate, left and right partial breast, and head-and-neck–using clinically relevant metrics: mean absolute error (MAE) of homogeneity index (HI) and dose-volume histogram (DVH). Compared to the protocol-specific model with the MAE-HI of 0.274 and the MAE-DVH of 7.46, Treat achieves a superior performance of 0.062 and 2.87 for MAE-HI and MAE-DVH score, respectively. When compared to baseline one-hot conditioning with the MAE-HI of 0.085 and the MAE-DVH of 3.35, GPP demonstrates its efficiency in adapting prompt-based conditioning for predicting dose distributions for diverse protocols. The code is available: https://github.com/mcintoshML/TextGuided_RT