  LLM watermarking, which embeds imperceptible yet algorithmically detectable
signals in model outputs to identify LLM-generated text, has become crucial in
mitigating the potential misuse of large language models. However, the
abundance of LLM watermarking algorithms, their intricate mechanisms, and the
complex evaluation procedures and perspectives pose challenges for researchers
and the community to easily experiment with, understand, and assess the latest
advancements. To address these issues, we introduce MarkLLM, an open-source
toolkit for LLM watermarking. MarkLLM offers a unified and extensible framework
for implementing LLM watermarking algorithms, while providing user-friendly
interfaces to ensure ease of access. Furthermore, it enhances understanding by
supporting automatic visualization of the underlying mechanisms of these
algorithms. For evaluation, MarkLLM offers a comprehensive suite of 12 tools
spanning three perspectives, along with two types of automated evaluation
pipelines. Through MarkLLM, we aim to support researchers while improving the
comprehension and involvement of the general public in LLM watermarking
technology, fostering consensus and driving further advancements in research
and application. Our code is available at https://github.com/THU-BPM/MarkLLM.
