Multi-Agent Development Framework

A Multi-Agent Development Framework is a AI agent software framework that enables the development and orchestration of multi-agent systems.

• AKA: Multi-Agent Framework.
• Context:
  • It can (typically) enable agent communication through interaction protocols.
  • It can (typically) support agent collaboration via workflow orchestration.
  • It can (typically) manage agent roles and agent behaviors.
  • It can (typically) provide tool integration for external interactions.
  • ...
  • It can (often) implement memory management for agent state.
  • It can (often) handle error recovery during execution.
  • It can (often) support human oversight via interaction interfaces.
  • It can (often) enable workflow customization through configuration options.
  • ...
  • It can have Architecture Types including centralized networks, decentralized networks, and hierarchical networks.
  • It can provide Development Tools for agent design and deployment.
  • It can implement Security Measures for system protection.
  • It can maintain Performance Metrics for system evaluation.
  • ...
• Example(s):
  • Modern Multi-Agent Frameworks, such as:
    • LLM Based Frameworks for language model orchestration:
      • AutoGen Framework for conversation orchestration.
      • CrewAI Framework for role based workflow.
      • LangGraph Framework for graph based execution.
      • MetaGPT Framework for software development simulation.
    • Agent Composition Frameworks for system architecture:
      • Microsoft Semantic Kernel for plugin orchestration.
      • Haystack Framework for pipeline composition.
      • Langchain Framework for chain orchestration.
  • Traditional Multi-Agent Frameworks, such as:
    • FIPA Compliant Frameworks for standardized communication:
      • JADE Framework for java based agents.
      • SPADE Framework for python based agents.
      • ZEUS Framework for enterprise agents.
    • Academic Frameworks for research implementation:
      • INGENIAS Framework for model driven development.
      • AgentSpeak Framework for BDI architecture.
      • SARL Framework for general purpose agents.
  • Generative Social Agent Frameworks, such as:
    • Behavioral Simulation Frameworks for human behavior:
      • Stanford Generative Framework for social interaction.
      • Meta AI Cicero Framework for negotiation behavior.
      • OpenAI Virtual Agent Framework for task completion.
    • Social Network Frameworks for agent community:
      • Cooperative Agent Network for group coordination.
      • Competitive Agent Framework for strategic interaction.
  • Domain Specific Frameworks, such as:
    • Game AI Frameworks for virtual environments:
      • Unity ML Framework for game agents.
      • Unreal AI Framework for simulation agents.
    • Robotics Frameworks for physical agents:
      • ROS Multi-Agent Framework for robot coordination.
      • MOOS Framework for autonomous systems.
  • Industrial Frameworks, such as:
    • Manufacturing Frameworks for production systems:
      • Factory Agent Framework for process automation.
      • Supply Chain Framework for logistics coordination.
    • Service Frameworks for business processes:
      • Customer Service Framework for interaction management.
      • Resource Allocation Framework for task distribution.
  • Research Frameworks, such as:
    • Experimental Frameworks for agent study:
      • Cognitive Architecture Framework for mental processes.
      • Social Learning Framework for behavioral adaptation.
    • Validation Frameworks for system testing:
      • Agent Verification Framework for behavior validation.
      • Performance Benchmark Framework for system evaluation.
  • Generative Social Agent Frameworks, such as:
    • Research Simulation Frameworks for behavioral study:
    • Policy Analysis Frameworks for societal impact:
    • Virtual World Frameworks for environment simulation:
    • Gaming Environment Frameworks for interactive simulation:
    • Training Simulation Frameworks for skill development:
  • ...
• Counter-Example(s):
  • Single-Agent Frameworks, which lack multi-agent coordination.
  • Monolithic Frameworks, which do not support agent distribution.
  • Static Frameworks, which cannot handle dynamic interactions.
• See: Software Framework, Multi-Agent System, Agent Architecture, Distributed System, Collaborative AI, System Orchestration.

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References

2025-03-20

• Perplexity.ai
  • Question: How do various multi-agent frameworks compare in terms of capabilities and features? Please analyze AutoGen, CrewAI, LangGraph, MetaGPT, OpenAI Responses, and Anthropic Framework across dimensions such as model integration, agent orchestration, tool support, and other core features.
  • Answer: Multi-agent frameworks provide different levels of support for various features, with strengths varying across implementations. The comparison reveals that AutoGen, OpenAI Responses, and Anthropic Framework generally offer high support for model integration, tool support, and security controls, while CrewAI and LangGraph excel in agent orchestration and visual workflows.
    
  • Comparing these frameworks shows clear specializations. AutoGen excels in enterprise integration and human-AI interaction. CrewAI offers superior role-based architecture with domain templates. LangGraph provides advanced graph visualization for complex agent interactions. MetaGPT features extensive pre-built agents but has limited tool support. OpenAI Responses excels in cost optimization and model integration, while Anthropic Framework delivers robust error handling and security compliance.
    
  • Implementation complexity varies significantly, with AutoGen and Anthropic Framework requiring more technical expertise but offering greater enterprise-grade solutions. Current trends show increasing focus on agent swarms, specialized hardware optimization, and regulatory compliance features.
    • Framework Overviews:
      • AutoGen: Open-source framework by Microsoft for multi-agent collaboration and orchestration.
      • CrewAI: Open-source Python framework for role-based multi-agent systems.
      • LangGraph: Framework for graph-based multi-agent workflows with visual representation.
      • MetaGPT: Framework assigning roles to GPTs for collaborative tasks.
      • OpenAI Responses: OpenAI's framework for integrating multiple AI models and tools.
      • Anthropic Framework: Framework leveraging Anthropic's LLMs for robust multi-agent systems.
    • Support Levels:
      • Model Integration:
        • High Support: AutoGen, MetaGPT, OpenAI Responses, Anthropic Framework.
        • Medium Support: CrewAI, LangGraph.
      • Agent Orchestration:
        • High Support: AutoGen, CrewAI, LangGraph, Anthropic Framework.
        • Medium Support: MetaGPT, OpenAI Responses.
      • Tool Support:
        • High Support: AutoGen, OpenAI Responses, Anthropic Framework.
        • Medium Support: CrewAI, LangGraph.
        • Low Support: MetaGPT.
      • Human-in-the-Loop:
        • High Support: AutoGen.
        • Medium Support: LangGraph, OpenAI Responses, Anthropic Framework.
        • Low Support: CrewAI, MetaGPT.
      • Streaming Responses:
        • High Support: AutoGen, OpenAI Responses, Anthropic Framework.
        • Medium Support: CrewAI, LangGraph.
        • Low Support: MetaGPT.
      • Custom Prompts:
        • High Support: AutoGen, CrewAI, MetaGPT, Anthropic Framework.
        • Medium Support: LangGraph, OpenAI Responses.
      • Memory Management:
        • High Support: CrewAI, MetaGPT, OpenAI Responses.
        • Medium Support: AutoGen, LangGraph, Anthropic Framework.
      • Error Handling:
        • High Support: LangGraph, Anthropic Framework.
        • Medium Support: AutoGen, CrewAI.
        • Low Support: MetaGPT, OpenAI Responses.
      • Security Controls:
        • High Support: AutoGen, OpenAI Responses, Anthropic Framework.
        • Medium Support: CrewAI, LangGraph.
        • Low Support: MetaGPT.
      • Deployment Options:
        • High Support: AutoGen, OpenAI Responses, Anthropic Framework.
        • Medium Support: CrewAI, LangGraph.
        • Low Support: MetaGPT.
      • Visual Workflows:
        • High Support: CrewAI, LangGraph.
        • Medium Support: OpenAI Responses, Anthropic Framework.
        • Low Support: AutoGen, MetaGPT.
      • Testing Framework:
        • High Support: MetaGPT, Anthropic Framework.
        • Medium Support: AutoGen, LangGraph, OpenAI Responses.
        • Low Support: CrewAI.
      • Domain Specialization:
        • High Support: CrewAI, MetaGPT.
        • Medium Support: AutoGen, LangGraph, Anthropic Framework.
        • Low Support: OpenAI Responses.
      • Cost Optimization:
        • High Support: OpenAI Responses.
        • Medium Support: AutoGen, CrewAI, Anthropic Framework.
        • Low Support: LangGraph, MetaGPT.
      • Compliance Tools:
        • High Support: AutoGen, OpenAI Responses, Anthropic Framework.
        • Medium Support: CrewAI.
        • Low Support: LangGraph, MetaGPT.
    • Framework-Specific Strengths:
      • OpenAI Responses:
        • New Responses API combines chat completions and Assistants API features.
        • Integrated web/search capabilities via gpt-4o-search models.
        • Computer vision integration through CUA model.
        • Robust token management for API cost optimization.
        • Flexible deployment options across cloud environments.
      • Anthropic Framework:
        • Multi-layer testing requirements per safety guidelines.
        • Specialized Claude 3.5 Sonnet classifier for intent detection.
        • Orchestrator-worker architecture for complex workflows.
        • Enterprise-grade security and compliance features.
        • AWS Inferentia integration for hardware acceleration.
      • AutoGen:
        • Mature enterprise deployment capabilities.
        • Sophisticated human-AI collaboration features.
        • Docker-based tool execution for secure code execution.
        • Asynchronous architecture for complex agent interactions.
        • Configurable human intervention modes for approval workflows.
      • CrewAI:
        • Visual workflow designer for non-technical users.
        • Strong domain-specific templates.
        • Role-based agent system for specialized tasks.
        • Hierarchical memory patterns for contextual information.
        • Python-native integration for rapid development.
      • LangGraph:
        • Graph-based task dependency resolution.
        • Visual representation of agent networks.
        • Robust error handling through graph-based workflows.
        • LangChain integration for tool access.
        • State tracking features for complex reasoning.
      • MetaGPT:
        • Extensive library of pre-built agents.
        • Strong async processing capabilities.
        • Advanced domain templates for specialized applications.
        • Built-in testing tools for agent quality assurance.
        • Team-based agent design for collaborative problem-solving.
    • Implementation Considerations:
      • Technical Complexity:
        • High Complexity: AutoGen, Anthropic Framework.
        • Medium Complexity: LangGraph, OpenAI Responses.
        • Low Complexity: CrewAI, MetaGPT.
      • Integration Effort:
        • High Effort: AutoGen, LangGraph.
        • Medium Effort: Anthropic Framework, OpenAI Responses.
        • Low Effort: CrewAI, MetaGPT.
      • Scaling Capability:
        • High Scaling: AutoGen, OpenAI Responses, Anthropic Framework.
        • Medium Scaling: LangGraph, CrewAI.
        • Low Scaling: MetaGPT.
      • Documentation Quality:
        • High Quality: AutoGen, OpenAI Responses.
        • Medium Quality: CrewAI, LangGraph, Anthropic Framework.
        • Low Quality: MetaGPT.
    • Emerging Trends:
      • Agent Swarms: OpenAI's Swarm framework showing promise for complex coordination.
      • Specialized Hardware: Anthropic Framework optimized for AWS Inferentia chips.
      • Regulatory Focus: Increased compliance features in enterprise-focused frameworks.
      • Cross-Framework Integration: Growing interoperability between frameworks.
      • Low-Code Development: Emerging visual development tools for agent system creation.
      • Enterprise Security: Enhanced security features for enterprise deployments.
      • Domain Specialization: Increasing vertical solutions for industry-specific applications.
      • Cost Management: Advanced token optimization and resource allocation tools.
  • Citations:

[1] https://www.akira.ai/blog/microsoft-autogen-with-multi-agent-system
[2] https://www.datacamp.com/tutorial/crew-ai
[3] https://aws.amazon.com/blogs/machine-learning/build-a-multi-agent-system-with-langgraph-and-mistral-on-aws/
[4] https://docs.deepwisdom.ai/main/en/guide/get_started/introduction.html
[5] https://lablab.ai/t/openais-swarm-a-deep-dive-into-multi-agent-orchestration-for-everyone
[6] https://awslabs.github.io/multi-agent-orchestrator/agents/built-in/anthropic-agent/
[7] https://www.ibm.com/think/topics/crew-ai
[8] https://blog.futuresmart.ai/multi-agent-system-with-langgraph
[9] https://microsoft.github.io/autogen/
[10] https://www.ibm.com/think/topics/metagpt
[11] https://awslabs.github.io/multi-agent-orchestrator/agents/built-in/openai-agent/
[12] https://aws.amazon.com/blogs/machine-learning/design-multi-agent-orchestration-with-reasoning-using-amazon-bedrock-and-open-source-frameworks/
[13] https://www.microsoft.com/en-us/research/project/autogen/
[14] https://docs.crewai.com/introduction
[15] https://blog.langchain.dev/langgraph-multi-agent-workflows/
[16] https://github.com/geekan/MetaGPT
[17] https://openai.com/index/new-tools-for-building-agents/
[18] https://www.anthropic.com/research/building-effective-agents
[19] https://www.youtube.com/watch?v=2VIoG4_r3-A
[20] https://www.crewai.com

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