MASBench is a unified benchmark suite for systematically analyzing architectural design choices in LLM-based agent frameworks — spanning orchestration, memory, planning, specialization, and multi-agent coordination under controlled execution. The suite isolates framework-level effects from model capabilities and task complexity, enabling controlled evaluation of single-agent and multi-agent architectural design choices.
Resources: Website | Paper (coming soon)
Existing benchmarks primarily test isolated agent capabilities (reasoning, tool use, memory) without addressing how framework architecture governs performance and scalability. MASBench fills this gap by:
- Providing controlled evaluation of architectural design decisions under fixed models and tasks
- Isolating framework-level effects from model capabilities and task complexity
- Enabling systematic comparison across orchestration patterns, memory architectures, and coordination mechanisms
- Supporting reproducible analysis of scalability and resource utilization characteristics
- Unified execution pipeline: Standardized interfaces normalize execution across diverse frameworks
- Standardized configuration & logging: Consistent measurement and artifact collection
- Controlled architectural isolation: Framework behavior evaluated independently of model and task variations
- Cost-aware backend routing: Abstracted LLM backends support efficient, framework-agnostic evaluation
MASBench organizes frameworks along three primary paradigms:
- Graph-based orchestration: Workflows modeled as directed graphs with nodes representing computational steps and edges defining control flow
- Role-based agent systems: Agents structured around specialized roles with coordination mechanisms routing tasks based on role assignments
- Environment/simulation-mediated systems: Agents situated within shared environments where interaction occurs through state and action interfaces
The suite evaluates key architectural dimensions:
- Orchestration & control flow: How frameworks structure task execution and manage dependencies
- Memory architecture: Long-term retention, learning, and forgetting mechanisms
- Planning interfaces: Multi-step reasoning under framework constraints
- Specialization mechanisms: Role assignment, task routing, and capability distribution
- Communication topology & coordination: Information flow patterns, coordination mechanisms, and topology-induced interaction patterns in multi-agent settings
- Memory — Long-term retention, learning, forgetting
- Planning — Multi-step reasoning under interface constraints
- Specialization — Role assignment and capability distribution
- Framework Overhead — Orchestration and execution efficiency
- Tool Use — Architectural integration patterns
- Coordination & Topology — Communication patterns and coordination outcomes
MASBench enforces reproducibility through:
- Fixed Python version (3.12.3) and pinned dependencies (
requirements.lock) - Unified execution pipeline with standardized configuration and logging
- Backend abstraction supporting cost-aware, framework-agnostic evaluation
- Experimental results preserved in
results/for transparency
Analysis and interpretation are documented within individual experiment directories and associated publications.
If you use MASBench in academic work, please cite:
@article{orogat2026mafbench,
title={Understanding Multi-Agent LLM Frameworks: A Unified Benchmark and Experimental Analysis},
author={Orogat, Abdelghny and Rostam, Ana and Mansour, Essam},
journal={arXiv preprint arXiv:submit/7225627},
year={2026}
}Abdelghny Orogat — Concordia University
Email: Abdelghny.Orogat@concordia.ca
