你刚刷到这条消息，本来准备顺手划走，但又怕自己错过了真正会影响下一步判断的那一点。
最容易做错的，是opendatalab / MinerU；代价往往是如果只盯表面热闹，你很容易在错误方向上花掉时间、预算和注意力。；我先给一个保守判断：MinerU卖的是Agent可吞的数据，不是识字。。
You see opendatalab / MinerU, notice the 70k-star signal, assume it's another OCR story, and move on. That is exactly how you end up spending time, budget, and attention on the wrong question. My conservative read: MinerU is selling data an agent can actually ingest, not just text recognition.
What changed my view was not the hype, but the framing. The GitHub repo sits at about 70.3k stars, but the more useful signal is the positioning: PDF 文档（PDF） and Office files into LLM-ready Markdown and JSON for agent 工作流程（工作流程（workflow）s） [S001]. That is a 工作流程 promise, not just an extraction promise.
The homepage makes the same bet. It describes MinerU as an intelligent document parsing platform for Agent and RAG 工作流程（工作流程（workflow）s）, and it highlights machine-readable Markdown, JSON, and LaTeX instead of stopping at plain OCR [S002]. In plain English: it is trying to remove the cleanup step between a document and the model that needs to use it.
That does not mean OCR is obsolete. It means that inside Agent and RAG pipelines, recognition quality is only part of the job. The real bottleneck is often whether the output is structured enough that your model can use it without another round of manual fixing.
A tool update is worth following only if it changes your next decision, not because it ships the longest feature list. If you're evaluating document tools for AI apps, don't ask "How well does it read?
真正该讨论的是：opendatalab / MinerU