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MASEval: Extending Multi-Agent Evaluation from Models to Systems

arXiv:2603.08835v1 Announce Type: new Abstract: The rapid adoption of LLM-based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). Yet existing benchmarks are model-centric: they fix the agentic setup and do not compare other system components. We argue that implementation decisions substantially impact performance, including choices such as topology, orchestration logic, and error handling. MASEval addresses this evaluation gap with a framework-agnostic library that treats the entire system as the unit of analysis. Through a systematic system-level comparison across 3 benchmarks, 3 models, and 3 frameworks, we find that framework choice matters as much as model choice. MASEval allows researchers to explore all components of agentic systems, opening new avenues for principled system design, and practitioners to identify the best implementation for their use case. MASEval is available under the MIT licence https://gi

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Cornelius Emde, Alexander Rubinstein, Anmol Goel, Ahmed Heakl, Sangdoo Yun, Seong Joon Oh, Martin Gubri · · 1 min read · 10 views
#cs.AI #cs.CL #cs.LG

arXiv:2603.08835v1 Announce Type: new Abstract: The rapid adoption of LLM-based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). Yet existing benchmarks are model-centric: they fix the agentic setup and do not compare other system components. We argue that implementation decisions substantially impact performance, including choices such as topology, orchestration logic, and error handling. MASEval addresses this evaluation gap with a framework-agnostic library that treats the entire system as the unit of analysis. Through a systematic system-level comparison across 3 benchmarks, 3 models, and 3 frameworks, we find that framework choice matters as much as model choice. MASEval allows researchers to explore all components of agentic systems, opening new avenues for principled system design, and practitioners to identify the best implementation for their use case. MASEval is available under the MIT licence https://github.com/parameterlab/MASEval.

Executive Summary

MASEval is a framework-agnostic library designed to evaluate the performance of agentic systems, rather than individual models. By comparing different system components, including topology, orchestration logic, and error handling, MASEval provides a more comprehensive understanding of system performance. The authors demonstrate the effectiveness of MASEval through a systematic comparison across three benchmarks, three models, and three frameworks, showing that framework choice has a significant impact on system performance. This work has significant implications for the development of principled system design and the identification of optimal implementation choices for specific use cases.

Key Points

  • ▸ MASEval evaluates agentic systems as a whole, rather than individual models.
  • ▸ The library compares different system components, including topology, orchestration logic, and error handling.
  • ▸ MASEval demonstrates the significant impact of framework choice on system performance.

Merits

Comprehensive Evaluation

MASEval provides a more comprehensive evaluation of agentic systems by considering the interactions between different system components.

Flexibility

The framework-agnostic design of MASEval allows researchers to easily compare different system configurations and identify optimal implementation choices.

Demerits

Scalability

Evaluating the performance of agentic systems can be computationally expensive, and MASEval may not be suitable for large-scale systems or complex benchmarks.

Interpretability

The results of MASEval may be difficult to interpret, particularly for systems with many interacting components.

Expert Commentary

The development of MASEval represents a significant step forward in the evaluation of agentic systems. By providing a framework-agnostic library that allows researchers to compare different system components, MASEval opens up new avenues for principled system design and the identification of optimal implementation choices. While there are limitations to MASEval, including scalability and interpretability concerns, the benefits of this work far outweigh the drawbacks. As the field of agentic systems continues to evolve, MASEval will play an increasingly important role in the development of more effective and efficient systems.

Recommendations

  • ✓ Researchers should use MASEval to evaluate the performance of agentic systems and identify optimal implementation choices for specific use cases.
  • ✓ Developers should consider the interactions between different system components when designing and evaluating agentic systems.

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