lmms-eval and MASEval
lmms-eval provides a general-purpose multimodal evaluation framework across modalities, while MASEval specializes in evaluating multi-agent LLM systems, making them complementary tools for different evaluation scenarios rather than direct competitors.
About lmms-eval
EvolvingLMMs-Lab/lmms-eval
One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks
This tool helps researchers and AI practitioners reliably compare how well different multimodal AI models understand and respond to various types of real-world information. You provide an AI model and a set of diverse tasks involving text, images, video, and audio, and it outputs consistent, trustworthy performance metrics. Anyone who builds, deploys, or studies large multimodal models will find this useful for understanding model capabilities.
About MASEval
parameterlab/MASEval
Multi-Agent LLM Evaluation
This is for AI researchers and developers who need to compare how well different multi-agent LLM systems perform. It takes your existing agent implementations (from frameworks like AutoGen or LangChain) and runs them through standard benchmarks or your own custom evaluation tasks. The output helps you understand which agent architectures and configurations are most effective for specific challenges.
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