OpenICL provides a unified interface for in-context learning experiments, integrating various retrieval and inference methods to enable systematic comparison of language models. It supports various prompt instructions, retrieval techniques, and inference strategies including self-consistency. Users can load datasets, define prompt templates, and evaluate different LM configurations with built-in methods for fast research prototyping.