Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Benchmarks
Frameworks
Evaluation Libraries
Existing benchmarks like GAIA and AgentBench are model-centric. MASEval provides a unified interface for evaluating agents across multiple benchmarks with minimal integration overhead.
LLM-based agent frameworks (smolagents, LangGraph, CAMEL) have proliferated. MASEval offers system-level evaluation infrastructure for comparing design decisions and framework implementations.
Inspect AI and HAL are evaluation frameworks, but lack multi-agent specific tracing or cross-framework comparison. MASEval focuses on system-level, framework-agnostic evaluation.
Enterprise Process Flow
Setup Environment & Agents
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Execute Custom Orchestration
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Collect Traces & Metadata
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Evaluate Metrics
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Generate Report
| Feature | MASEval | Other |
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| Multi-Agent Native |
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| Framework-Agnostic |
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| System-Level Eval |
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| Unified Benchmarks |
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| Trace-First |
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Case Study: Impact of Framework Choice
In experiments across 3 benchmarks, 3 models, and 3 frameworks, framework choice impacted performance comparably to model choice. For example, Haiku 4.5 scored 90.4% with smolagents but 59.5% with LlamaIndex on MACS Travel, a 30.9pp gap. This highlights the importance of system-level evaluation beyond just model capabilities.
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Mean Range across Models
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Mean Range across Frameworks
ROI Projection
Calculate Your Potential AI Savings
Understand the tangible impact of MASEval on your operational efficiency and cost reduction with our interactive ROI calculator.
Annual Cost Savings
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Hours Reclaimed Annually
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Your Journey
Seamless MASEval Integration Roadmap
Our structured approach ensures a smooth transition to enhanced AI evaluation capabilities. Partner with us for expert guidance at every step.
Phase 01: Initial Assessment & Setup
We begin by understanding your current agentic systems and evaluation needs. This includes defining key metrics and integrating MASEval adapters with your existing frameworks.
Phase 02: Benchmark Customization & Execution
Tailor existing benchmarks or develop new ones using MASEval's toolkit. Execute initial evaluations, collecting detailed traces and performance data across chosen models and frameworks.
Phase 03: Deep Analysis & Optimization
Leverage MASEval's tracing and reporting to identify performance bottlenecks and architectural insights. Collaborate to refine agent designs, communication topologies, and error handling for optimal results.
Phase 04: Continuous Evaluation & Scalability
Establish a continuous evaluation pipeline for ongoing performance monitoring and regression testing. Scale your multi-agent systems with confidence, backed by robust and reproducible evaluation.
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Transform your multi-agent system development with principled, system-level benchmarking.
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