Comprehensive Comparison of Multi AI Agent Frameworks: Choosing the Right Multi-Agent System

Introduction

With the rapid development of artificial intelligence technology, multi-agent systems have become important tools for solving complex tasks. From conversational collaboration to automated task processing, different application scenarios require different framework architectures. This article provides an in-depth comparison of seven mainstream multi-agent frameworks (AutoGen, LangGraph, CrewAI, AgentMesh, AutoAgent, InfiAgent, MAS²), analyzing from multiple dimensions including technical architecture, collaboration patterns, and development approaches to help AI developers, system architects, and technical decision-makers choose the most suitable multi-agent framework based on actual needs.

Framework Overview

This article compares the following seven frameworks:

  1. AutoGen (Microsoft)
  2. LangGraph (Based on LangChain)
  3. CrewAI
  4. AgentMesh
  5. AutoAgent / AutoAgents
  6. InfiAgent
  7. MAS²

1. AutoGen (Microsoft)

Framework Introduction

AutoGen is an open-source framework developed by Microsoft Research, specifically designed for building complex agent systems with multiple agents. The framework emphasizes modularity and scalability, suitable for conversational and task-oriented AI applications.

Core Features

  • Multi-Agent Collaboration: Supports multiple autonomous agents working together to complete complex tasks
  • Flexible Agent Definition: Can define different types of agents, including experts, assistants, strategists, and other roles
  • Conversational Collaboration: Agents communicate through a conversational space, supporting multi-party dialogue and collaboration
  • Modular Design: Provides modular, extensible toolkits, making it easy for developers to quickly build complex AI systems

Technical Architecture

AutoGen adopts a conversational collaboration model where agents communicate through a virtual conversational space. This design enables agents to:

  • Conduct multi-party dialogues (text, audio, or video formats)
  • Dynamically adjust collaboration strategies
  • Flexibly combine different types of agents according to task requirements

Use Cases

  • Scenarios requiring multi-party dialogue and collaboration
  • Building complex conversational systems
  • Task automation applications
  • Systems requiring flexible agent role and responsibility definitions

References

2. LangGraph (Based on LangChain)

Framework Introduction

LangGraph is an open-source framework developed by the LangChain team, specifically designed for building stateful, multi-agent systems. The framework uses the concept of directed cyclic graphs (DAG) to implement complex dynamic workflows through graph structures.

Core Features

  • Graph Architecture: Uses directed cyclic graphs (DAG) to manage agent workflows
  • State Management: Natively supports short-term and long-term memory, maintaining context consistency
  • Cycles and Branches: Supports cycles and conditional branches, adapting to dynamically changing scenarios
  • Human Intervention: Supports human-in-the-loop functionality, allowing human intervention at any stage of the workflow
  • Persistence: Provides persistent state management, facilitating stateful behavior implementation

Technical Architecture

LangGraph adopts a graph architecture pattern, managing agent workflows through node and edge definitions. This design enables:

  • Building complex dynamic workflows
  • Supporting cycle and conditional branch logic
  • Seamless integration with LangChain and LangSmith
  • Active community support

Use Cases

  • Multi-agent orchestration applications requiring high customization
  • Building complex dynamic workflows
  • Multi-agent systems requiring state management
  • Applications requiring human-in-the-loop control

References

3. CrewAI

Framework Introduction

CrewAI is a multi-agent collaboration framework that supports task allocation and role management. The framework particularly emphasizes high-performance team collaboration and features a unique "role hibernation" mechanism to optimize resource usage.

Core Features

  • Task Allocation: Supports efficient task allocation and collaboration mechanisms
  • Role Management: Provides flexible role management functionality
  • Role Hibernation Mechanism: Features a "role hibernation" mechanism that can save approximately 37% of memory
  • Ease of Use: Intuitive and easy to set up, primarily relying on prompt engineering
  • Rapid Prototyping: Non-technical users can quickly get started, suitable for rapid prototyping

Technical Architecture

CrewAI adopts a task allocation collaboration model, organizing multi-agent collaboration through clear task allocation and role definitions. This design enables:

  • Agents to collaborate efficiently according to task requirements
  • The system to automatically manage agent lifecycles
  • Optimized resource usage, improving system performance

Use Cases

  • Team tasks requiring high-performance collaboration
  • Rapid prototyping and demonstrations
  • Applications where multiple agents collaborate to complete complex tasks
  • Scenarios requiring clear task allocation and role management

References

4. AgentMesh

Framework Introduction

AgentMesh is an open-source multi-agent collaboration framework that adopts a layered architecture design. The framework has strong scalability and supports multiple runtime modes, making it easy to integrate into existing applications.

Core Features

  • Layered Architecture: Adopts a layered architecture design, dividing the system into model layer, framework layer, and application layer
  • Multiple Runtime Modes: Supports CLI, Web, SDK, API, and other runtime modes
  • Scalability: Has strong scalability, with each layer being extensible
  • Easy Integration: Easy to integrate into existing applications, providing multiple interfaces
  • Flexible Deployment: Supports flexible deployment options to meet different needs

Technical Architecture

AgentMesh adopts a layered architecture pattern:

  • Model Layer: Supports integration of mainstream commercial models
  • Framework Layer: Provides core multi-agent capabilities, including tools, memory, knowledge, models required by agents, and team modules responsible for multi-agent interaction
  • Application Layer: Supports command-line execution, Web interface execution, integration into self-developed applications through SDK or API, and integration with common communication and office software

Use Cases

  • Scenarios requiring integration into existing applications
  • Building scalable multi-agent systems
  • Scenarios requiring multiple runtime modes
  • Enterprise applications requiring flexible deployment options

References

5. AutoAgent / AutoAgents

Framework Introduction

AutoAgent is a fully automated, zero-code framework that enables users to create and deploy large language model agents using only natural language. The framework serves as an autonomous agent operating system, containing four key components.

Core Features

  • Zero-Code Framework: Can automatically generate agents, requiring only natural language to create and deploy
  • Fully Automated: Supports efficient and dynamic creation and modification of tools, agents, and workflows without coding or human intervention
  • Lower Technical Barrier: Suitable for non-technical users, rapid prototyping
  • Autonomous Agent Operating System: Contains agent system utilities, LLM-driven actionable engines, self-managed file systems, and self-practicing agent customization modules

Technical Architecture

AutoAgent adopts a zero-code architecture, generating and configuring agent systems through natural language understanding. Core components include:

  • Agent System Utilities: Provides basic functionality for agent management
  • LLM-Driven Actionable Engine: Responsible for understanding and executing natural language instructions
  • Self-Managed File System: Automatically manages agent-related files and configurations
  • Self-Practicing Agent Customization Module: Automatically optimizes and adjusts agent configurations

Use Cases

  • Rapid prototyping
  • Scenarios where non-technical users need to create and deploy agents
  • Scenarios requiring rapid agent deployment
  • Applications that lower development barriers

References

6. InfiAgent

Framework Introduction

InfiAgent is a self-evolving pyramid agent framework designed to handle infinite scenarios. The framework introduces an "agent-as-tool" mechanism that automatically decomposes complex agents into hierarchical multi-agent systems.

Core Features

  • Self-Evolution: Supports agent self-evolution mechanisms, automatically reconstructing agent DAGs based on new tasks, poor performance, or optimization opportunities
  • Pyramid Architecture: Adopts a hierarchical agent system, automatically decomposing complex agents into hierarchical multi-agent systems
  • Automatic Task Decomposition: Can automatically decompose complex tasks
  • Dual Review Mechanism: Ensures quality and stability of task completion
  • Agent Routing: Achieves efficient task-agent matching

Technical Architecture

InfiAgent adopts a pyramid architecture pattern, processing complex tasks through hierarchical agent systems:

  • Agent-as-Tool: Automatically decomposes complex agents into hierarchical multi-agent systems
  • Dual Review: Ensures quality and stability of task completion
  • Agent Routing: Achieves efficient task-agent matching
  • Self-Evolution: Automatically reconstructs agent DAGs based on new tasks, poor performance, or optimization opportunities

Use Cases

  • Complex tasks requiring handling of infinite scenarios
  • Applications requiring handling of complex tasks and changing scenarios
  • Building self-evolving intelligent agent systems
  • Agent systems requiring high autonomy and adaptability

References

7. MAS²

Framework Introduction

MAS² is a self-generating, self-configuring, self-correcting multi-agent system. The framework adopts a "generator-executor-corrector" three-agent team that can dynamically combine and adaptively correct target agent systems according to real-time task requirements.

Core Features

  • Self-Generating: Can automatically generate agent systems according to task requirements
  • Self-Configuring: Automatically configures agent systems to adapt to task requirements
  • Self-Correcting: Supports adaptive correction, can dynamically adjust agent combinations
  • Dynamic Combination: Supports dynamic agent combination, adjusting according to real-time task requirements
  • Highly Automated: Reduces human intervention, provides highly automated agent management and configuration capabilities

Technical Architecture

MAS² adopts a "generator-executor-corrector" three-agent team architecture:

  • Generator: Responsible for generating agent systems according to task requirements
  • Executor: Responsible for executing tasks
  • Corrector: Responsible for correction and optimization based on execution results

Through collaborative tree optimization training and specialization of these meta-agents, MAS² performs excellently in complex scenarios such as deep research and code generation.

Use Cases

  • Complex scenarios such as deep research and code generation
  • Multi-agent systems requiring high automation and adaptability
  • Systems requiring adaptive and self-correcting capabilities
  • Applications requiring dynamic agent combination adjustment

References

Framework Classification and Comparison

Classification by Architecture Pattern

1. Layered Architecture

  • AgentMesh: Divides the system into model layer, framework layer, and application layer

2. Graph Architecture

  • LangGraph: Uses graph structures to manage agent workflows

3. Pyramid Architecture

  • InfiAgent: Adopts hierarchical agent systems

4. Conversational Architecture

  • AutoGen: Agents communicate through conversational space

5. Task Allocation Architecture

  • CrewAI: Collaborates through task allocation

6. Zero-Code Architecture

  • AutoAgent: Requires only natural language

7. Adaptive Architecture

  • MAS²: Self-generating, self-configuring, self-correcting

Classification by Collaboration Mode

1. Conversational Collaboration

  • AutoGen: Agents communicate through conversational space

2. Task Allocation

  • CrewAI: Collaborates through task allocation

3. Tool-Based Collaboration

  • InfiAgent: Agents serve as tools for other agents

4. Dynamic Combination

  • MAS²: Dynamically combines agents

Classification by Development Method

1. Code-Based Frameworks

Require code configuration:

  • AutoGen
  • LangGraph
  • CrewAI
  • AgentMesh
  • InfiAgent
  • MAS²

2. Zero-Code Frameworks

Require only natural language:

  • AutoAgent

Comprehensive Comparison Table

Framework Development Method Architecture Pattern Collaboration Mode Learning Curve Use Cases
AutoGen Code Conversational Conversational Collaboration Medium Multi-party dialogue, collaborative tasks
LangGraph Code Graph Graph Workflow High High customization, complex workflows
CrewAI Code Task Allocation Task Allocation Low Team collaboration, rapid prototyping
AgentMesh Code Layered Layered Collaboration Medium Integration with existing systems
AutoAgent Zero-Code Zero-Code Auto-Generated Very Low Rapid prototyping, non-technical users
InfiAgent Code Pyramid Tool-Based High Complex tasks, self-evolution
MAS² Code Adaptive Dynamic Combination High Deep research, code generation

Selection Recommendations

Selection Based on Requirements

Need High Customization

Recommendation: LangGraph

  • Provides maximum flexibility and customization capabilities
  • Supports complex dynamic workflows
  • Suitable for teams with rich development experience

Need Rapid Prototyping

Recommendation: AutoAgent or CrewAI

  • AutoAgent: Zero-code, usable by non-technical personnel
  • CrewAI: Intuitive and easy to use, primarily relies on prompt engineering

Need Integration with Existing Systems

Recommendation: AgentMesh

  • Supports multiple runtime modes (CLI, Web, SDK, API)
  • Easy to integrate into existing applications
  • Layered architecture facilitates expansion

Need to Handle Complex Tasks

Recommendation: InfiAgent or MAS²

  • InfiAgent: Strong self-evolution capability, suitable for handling infinite scenarios
  • MAS²: Strong adaptive capability, suitable for deep research and code generation

Need Multi-Party Dialogue

Recommendation: AutoGen

  • Specifically designed for conversational collaboration
  • Supports multi-party dialogue and collaboration
  • Flexible agent definition

Selection Based on Technical Background

Non-Technical Personnel

Recommendation: AutoAgent

  • Zero-code framework
  • Requires only natural language
  • Quick to get started

Junior Developers

Recommendation: CrewAI

  • Intuitive and easy to use
  • Primarily relies on prompt engineering
  • Gentle learning curve

Intermediate Developers

Recommendation: AutoGen or AgentMesh

  • Provides good documentation and community support
  • Balances functionality and ease of use

Senior Developers

Recommendation: LangGraph, InfiAgent, or MAS²

  • Provides maximum flexibility and customization capabilities
  • Suitable for building complex enterprise-level applications

Conclusion

Choosing the right multi-agent framework requires comprehensive consideration of multiple factors, including:

  • Application Scenarios: Different scenarios require different architecture patterns
  • Technical Background: Team's technical level affects framework selection
  • Development Resources: Investment of time and human resources
  • Expansion Needs: Whether expansion and maintenance are needed in the future

No single framework can be suitable for all scenarios. The key is to make the best choice based on actual needs. When selecting a framework, it is recommended to:

  1. Clarify application scenarios and requirements
  2. Assess the team's technical background
  3. Refer to official documentation and community resources
  4. Conduct small-scale POC (Proof of Concept)
  5. Adjust based on actual usage

We hope this article can help developers better understand the characteristics and applicable scenarios of various multi-agent frameworks and make informed choices.

Complete Reference List

Official Documentation and Resources

Academic Papers

Technical Articles and Comparisons

Market Research Reports

Other Resources