# Last chance join us tomorrow: Dive into ParseBench for AI Agents! 🚀

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ParseBench-2 (1)

Hi there, Llama Enthusiasts!

ParseBench has quickly become the standard framework for evaluating document parsing for AI agents. In this session we go under the hood: the methodology, what we tested, and how to use it to run your own eval.

Most existing benchmarks like OlmOCR were not built for how agents consume parsed output. They test on the wrong documents with the wrong metrics and miss the failures that matter most in production.

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Most existing benchmarks like OlmOCR were not built for how agents consume parsed output. They test on the wrong documents with the wrong metrics and miss the failures that matter most in production.In this session, we'll cover:

How ParseBench compares against existing benchmarks and where they fall short

The five dimensions that predict parser performance on real enterprise documents

How to structure an eval around your specific documents and use case

What the results across 14 parsers reveal about where they break down

If you're an AI engineer or technical founder evaluating document parsing for a production workflow, this session gives you the framework and the data to make a better call.

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Thank you for being a part of the LlamaIndex community. We're here to help you unlock the power of document context for your AI agents!

Best regards,

The LlamaIndex Team

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LlamaIndex, 405 Howard Street, San Francisco, California 94105, United States

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