Evaluating Multi-agent Coordination Frameworks: A Comparative Study Of Cerwai, Autogen, Langgraph, And Mcp

Abstract

This Paper Presents A Comparative Study Of Four Ma- Jor Approaches Currently Shaping Multi-Agent Systems (MAS): The Structured, Graph-based Model Of LangGraph, The Decen- Tralized Conversational Setup Of AutoGen, The Role-based Or- Chestration Used In CrewAI (or Cerwai), And The Foundational Security And Architecture Layer Provided By The Model Context Protocol (MCP). The Goal Is To Clearly Outline Their Architectural Differences, Highlight Real-world Performance Insights, And Ex- Plain The Underlying Engineering Trade-offs So That Developers And Researchers Can Make Informed Decisions When Building Production-ready Agentic AI Systems. Architecturally, Each Framework Brings A Distinct Approach. LangGraph’s Directed Acyclic Graphs (DAGs) Enable Consistent State Management And Deterministic Task Execution, Making It Ideal For Complex, Long-running Workflows With Checkpointing. AutoGen, By Contrast, Focuses On Agility And Conversational Simplicity, Excelling In Interactive Applications That Require Quick Coordination Between Agents. CrewAI Streamlines The Creation Of Autonomous Teams Through Predefined Roles And Task Structures, Offering An Approachable Entry Point For Specialized Automation. A Key Takeaway Is That The Model Context Protocol (MCP) Is Not A Competing Framework But A Foundational Standard For Security, Tool Management, And Interoperability Across MAS Ecosystems. It Mitigates Major Risks Related To Arbitrary Tool Execution And Enables Safe, Standardized Integration Between Different Agentic Frameworks. Based On Performance Metrics Like Response Consistency Index (RCI) And Latency, The Study Concludes That There Is No Universally Superior Framework—each Is Suited To Different Goals. The Right Choice Depends On Whether The Priority Is Maximizing Reliability And Accuracy (high RCI) Or Achieving Low-latency Responsiveness For Interactive Systems.

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