RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents

RPA-Check addresses a specific and growing pain point in the LLM ecosystem: the difficulty of evaluating non-deterministic, open-ended narrative agents. While standard benchmarks (MMLU, GSM8K) fail here, RPA-Check introduces a structured methodology to automate what is currently a manual 'vibe check' process. Its defensibility is currently low (3) because it is a nascent research artifact with zero stars and very fresh visibility; it lacks the integration ecosystem that defines infrastructure-grade projects like 'lm-evaluation-harness' or 'Weight & Biases'. However, the 5 forks within 4 days indicate immediate academic/peer interest. The project faces medium frontier risk because while OpenAI/Anthropic focus on general reasoning and safety, the specialized entertainment and persona-driven market (e.g., Character.ai, NovelAI) requires these exact tools. Its primary threat is 'LLM-as-a-judge' generic prompts becoming 'good enough' to displace specialized frameworks, but the multi-stage approach (logical vs. narrative vs. role) provides a more granular diagnostic tool that generic judges lack.