Platform Ecosystem: LangGraph alternatives (May 2025)

Research Date: May 28, 2025
Status: Current as of major industry announcements

🚀 Major Industry Developments (2025)

Microsoft Build 2025 (May 19, 2025)

• Azure AI Foundry Agent Service now Generally Available^[1]
• Unified SDK: "bringing Semantic Kernel and AutoGen into a single, developer-focused SDK"^[1]
• Interoperability: "Agent-to-Agent (A2A) and Model Context Protocol (MCP) support"^[1]
• Enterprise Focus: Multi-agent orchestration for complex business tasks

Google I/O 2025

• Agent Development Kit (ADK) "introduced at Google Cloud NEXT 2025, a new open-source framework designed to simplify the full stack end-to-end development of agents and multi-agent systems"^[2]
• Agent2Agent (A2A) Protocol "launched with support and contributions from more than 50 technology partners like Atlassian, Box, Cohere, Intuit, Langchain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG and Workday"^[3]
• Agent Mode: "an experimental feature where you will be able to simply describe your end goal and Gemini can get things done on your behalf"^[4]
• Agent Garden: Collection of ready-to-use agent samples and tools

The Protocol Wars: A2A vs MCP

• A2A (Agent2Agent): Google-led, focuses on agent-to-agent communication
• MCP (Model Context Protocol): Anthropic-led, focuses on model-context integration
• Industry Relationship: "Agentic applications need both A2A and MCP - MCP for tools and A2A for agents"^[5]
• Enterprise Adoption: "50+ technology partners including Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG and Workday"^[3]

Platform Ecosystem Overview

The following diagram illustrates the current landscape of LangGraph alternatives, organized by platform type and updated with the latest 2025 developments:

1. Open Source Frameworks (Updated)

🆕 Google ADK (Agent Development Kit)

• Status: "ADK is the same framework powering agents within Google products like Agentspace and the Google Customer Engagement Suite (CES)"^[2]
• Architecture: "Multi-Agent by Design: Build modular and scalable applications by composing multiple specialized agents in a hierarchy"^[2]
• Features:
  • "bidirectional audio and video streaming capabilities" ^[2]
  • "Choose the model that works best for your needs - whether it is Gemini or any model accessible via Vertex AI Model Garden" ^[2]
  • "over 100 pre-built connectors" ^[6]
  • Rich tool ecosystem integration with LangChain, LlamaIndex, CrewAI
• Best For: Complex multi-agent systems, enterprise applications
• Repository: Google Cloud ADK

🆕 PydanticAI (December 2024)

• Status: "Python agent framework designed to make it less painful to build production grade applications with Generative AI"^[13]
• Philosophy: "Built with one simple aim: to bring that FastAPI feeling to GenAI app development"^[13]
• Strengths:
  • "Built by the team behind Pydantic (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more)" ^[13]
  • Type-safe with static type checking integration (mypy, pyrite)
  • "Model-agnostic: Supports OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral" ^[13]
  • Dependency injection system for testing and production
• Best For: Production-grade applications requiring type safety and validation
• Repository: GitHub - pydantic/pydantic-ai

🆕 AG2 (Community Fork)

• Status: "AG2 is not a new framework — it's a rebranded version of AutoGen 0.2.34, now continuing under the name AG2"^[14]
• Governance: "Community-driven with an Open RFC process"^[14]
• Strengths:
  • "Full backward compatibility, no breaking changes planned" ^[15]
  • Maintains familiar AutoGen 0.2 architecture
  • Community-driven development and support
• Best For: Teams wanting stability and familiar AutoGen patterns without Microsoft's enterprise direction
• Repository: AG2 GitHub Organization

🆕 Atomic Agents

• Philosophy: "Businesses need AI systems that produce consistent, reliable outputs aligned with their brand and objectives"^[7]
• Architecture: "designed around the concept of atomicity to be an extremely lightweight and modular framework"^[7]
• Strengths:
  • "Define clear input and output schemas to ensure consistent behavior" ^[7]
  • "Fine-tune each part of the system individually, from system prompts to tool integrations" ^[7]
  • "built on top of Instructor and leverages the power of Pydantic for data and schema validation" ^[7]
  • Multi-provider support via Instructor compatibility
• Best For: Businesses requiring consistent, brand-aligned outputs
• Repository: GitHub - BrainBlend-AI/atomic-agents

🔄 Microsoft AutoGen + Semantic Kernel (Unified)

• Major Change: Previously separate frameworks now unified in Azure AI Foundry
• New Features:
  • Single developer-focused SDK
  • A2A and MCP protocol support
  • Enhanced observability and monitoring
  • Microsoft Entra Agent ID for security
• Best For: Enterprise Microsoft ecosystem integration
• Transition: Existing AutoGen users can migrate to unified experience

CrewAI (Continued Leadership in Simplicity)

• Status: "CrewAI specializes in creating intelligent agents capable of collaborating, sharing tasks, and optimizing actions through real-time communication and decision-making"^[8]
• Strengths: Role-based agent teams, intuitive setup, fast development
• Assessment: "CrewAI prioritizes simplicity" and is "well-suited for startups focused on building collaborative AI systems"^[8]
• Best For: Startups, rapid iteration, team-based workflows

OpenAI Swarm (Experimental)

• Status: Lightweight, experimental framework
• Focus: Dynamic agent handoffs and coordination
• Best For: Research, prototyping, simple multi-agent scenarios

Other Established Frameworks

• Haystack: Production-ready RAG applications, component-based architecture
• LlamaIndex Workflows: Event-driven agent systems
• DSPy: Programming framework for LLM optimization
• OpenAI Agents SDK: "Production-ready upgrade of our previous experimentation for agents, Swarm"^[16]

🆕 Developer-Focused Tools

Instructor - Structured Output Foundation

• Status: "The most popular Python library for working with structured outputs from large language models (LLMs), boasting over 1 million monthly downloads"^[18]
• Core Purpose: "Built on top of Pydantic, it provides a simple, transparent, and user-friendly API to manage validation, retries, and streaming responses"^[18]
• Why It's Foundational: "Built by the team behind Pydantic (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more)"^[13]
• Key Features:
  • "Response Models: Specify Pydantic models to define the structure of your LLM outputs" ^[18]
  • "Retry Management: Easily configure the number of retry attempts for your requests" ^[18]
  • "Flexible Backends: Seamlessly integrate with various LLM providers beyond OpenAI" ^[18]
• Multi-Provider Support: OpenAI, Anthropic, Gemini, Ollama, Groq, Cohere, LiteLLM
• Best For: Type-safe structured outputs, foundation for other frameworks
• Repository: GitHub - instructor-ai/instructor

Marvin - Lightweight AI Toolkit

• Status: "A lightweight AI engineering framework for building natural language interfaces that are reliable, scalable, and easy to trust"^[19]
• Developer: "Built by the engineering team at Prefect"^[20] (creators of the popular data orchestration platform)
• Philosophy: "Sometimes the most challenging part of working with generative AI is remembering that it's not magic; it's software"^[19]
• Architecture: "Python framework for building agentic AI workflows" with "structured, developer-focused framework for defining workflows and delegating work to LLMs"^[21]
• Key Features:
  • "Task-Centric Architecture: Break complex AI workflows into manageable, observable steps" ^[21]
  • "Rigorously type-hinted, used by thousands of engineers" ^[20]
  • "Built for incremental adoption"^[20] - can be used as serialization library or full engine
• Current Status: "Marvin 3.x is under active development" with API potentially undergoing breaking changes^[21]
• Best For: Lightweight agent workflows, incremental AI adoption, structured task orchestration
• Repository: GitHub - PrefectHQ/marvin

Rivet - Visual Agent IDE

• Description: "Powerful Integrated Development Environment (IDE) and library for creating AI agents using a visual, graph-based interface"^[17]
• Components: Rivet Application (editor/IDE) and Rivet Core/Node (TypeScript libraries)
• Strengths: "Node-based editor enables you to create, configure, and debug complex AI prompt chains and AI agent chains visually"^[17]
• Best For: Visual development, debugging agent flows, graph-based agent design

2. Enterprise Cloud Platforms (2025 Updates)

🔥 Azure AI Foundry Agent Service (GA)

• Launch: May 19, 2025 - General Availability
• Unified Experience: AutoGen + Semantic Kernel in single SDK
• Security: Microsoft Entra Agent ID for enterprise identity management
• Protocols: Native A2A and MCP support
• Observability: Built-in performance, quality, cost, and safety metrics
• Best For: Enterprise Microsoft environments

🔄 Google Vertex AI (Enhanced)

• New Additions:
  • Agent Development Kit integration
  • Agent2Agent protocol support
  • Agent Garden sample library
  • Enhanced multi-agent orchestration
• Model Support: 200+ models from various providers
• Integration: Over 100 pre-built connectors
• Best For: Google Cloud native applications, multi-vendor environments

🆕 AWS Bedrock Agents (Multi-Agent)

• New Capability: "Amazon Web Services has released a multi-agent collaboration capability for Amazon Bedrock, introducing a framework for deploying and managing multiple AI agents that collaborate on complex tasks"^[9]
• Architecture: "The system enables specialized agents to work together under a supervisor agent's coordination"^[9]
• Focus: Enterprise-scale applications with complex task distribution
• Best For: AWS ecosystem, large-scale automation

3. No-Code/Low-Code Platforms

n8n (Developer Preferred)

• Strengths: 422+ app integrations, visual workflow automation
• AI Features: AI agent nodes, LLM integrations, workflow automation
• Community: Strong developer community and documentation
• Best For: Technical teams wanting visual workflows

🆕 Shakudo AgentFlow (Enterprise OS)

• Positioning: "Secure AI agent platform that lets enterprises create and deploy intelligent agents using natural language"^[10]
• Features:
  • "Transform business processes into AI agents using plain English SOWs" ^[10]
  • "Keep your data secure with on-premise deployment, connect to existing databases, and scale from simple tasks to complex multi-agent workflows" ^[10]
  • "uses Model Context Protocol (MCP) to integrate seamlessly with your existing databases, APIs, SaaS tools, and internal systems" ^[10]
• Best For: Enterprises requiring secure, scalable agent infrastructure
• Website: Shakudo AgentFlow

Flowise (Open Source Visual)

• Status: Leading open-source visual agent builder
• Features: Drag-and-drop interface, LangChain integration
• Community: Active development, strong documentation
• Best For: Open-source visual development, customization needs

Other Platforms

• Voiceflow: Customer experience focus, voice/chat integration
• Botpress: Chatbot specialization, multi-channel deployment
• Langflow: LangChain-based visual workflows

4. 🆕 Interoperability Protocols (New Category)

Agent2Agent (A2A) Protocol

• Launch: "Today, we're launching a new, open protocol called Agent2Agent (A2A)" in April 2025^[3]
• Partners: "with support and contributions from more than 50 technology partners like Atlassian, Box, Cohere, Intuit, Langchain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG and Workday"^[3]
• Technology: "JSON-RPC 2.0 over HTTP(S) for request/response interactions" with "Server-Sent Events (SSE) for streaming real-time updates"^[11]
• Purpose: "The A2A protocol will allow AI agents to communicate with each other, securely exchange information, and coordinate actions"^[3]
• Microsoft Adoption: "we are committed to advancing open protocols like Agent2Agent (A2A), coming soon to Azure AI Foundry and Copilot Studio"^[12]

Model Context Protocol (MCP)

• Provider: Anthropic
• Purpose: Standardize how applications provide context to language models
• Relationship to A2A: Complementary - MCP for tools, A2A for agents
• Enterprise Support: Microsoft first-party support across platforms

Emerging Protocols

• Agent Network Protocol (ANP): Decentralized agent communication
• Agent Communication Protocol (ACP): IBM's enterprise-focused standard
• Industry Trend: Multiple standards competing for adoption

Competitive Analysis Matrix

Strategic Decision Framework

Choose Azure AI Foundry If:

• Already in Microsoft ecosystem (Office 365, Azure)
• Need enterprise security and compliance
• Want unified AutoGen + Semantic Kernel experience
• Require both A2A and MCP protocol support

Choose Google ADK + Vertex AI If:

• Prefer Google Cloud infrastructure
• Need advanced multi-agent coordination
• Want access to diverse model ecosystem (200+ models)
• Require bidirectional streaming capabilities

Choose Atomic Agents If:

• Prioritize predictable, consistent outputs
• Need fine-grained control over agent behavior
• Want modular, component-based architecture
• Prefer open-source with clear abstractions

Choose CrewAI If:

• Need rapid prototyping and development
• Want simple, role-based agent teams
• Prefer intuitive, beginner-friendly framework
• Building collaborative AI systems

Choose Shakudo AgentFlow If:

• Require enterprise-grade security and governance
• Need on-premise deployment options
• Want AI operating system approach
• Prefer natural language agent creation

Future-Proofing Considerations

Protocol Adoption Strategy

1. Immediate: Implement MCP for tool integration
2. Short-term: Evaluate A2A for agent communication
3. Long-term: Monitor protocol evolution and industry adoption

Technology Evolution Tracking

• Google: Focus on open-source, multi-vendor approach
• Microsoft: Enterprise integration and unified developer experience
• Anthropic: Context and safety-focused protocols
• Industry: Interoperability and standardization trends

Recommended Approach

1. Prototype: Test 2-3 frameworks with your specific use cases
2. Evaluate: Consider protocol support for future interoperability
3. Scale: Choose based on enterprise requirements and ecosystem alignment
4. Monitor: Stay updated on protocol evolution and industry adoption

Key Takeaways for 2025

1. Interoperability is Critical: A2A and MCP protocols will define agent ecosystem success
2. Enterprise Consolidation: Microsoft and Google providing comprehensive, managed solutions
3. Control vs Autonomy: New frameworks like Atomic Agents prioritize predictability
4. Open Source Momentum: Google's ADK and continued community-driven development
5. Security Focus: Enterprise platforms emphasizing security, compliance, and governance

References and Citations

[1] Microsoft Blog. "Microsoft Build 2025: The age of AI agents and building the open agentic web." May 19, 2025. https://blogs.microsoft.com/blog/2025/05/19/microsoft-build-2025-the-age-of-ai-agents-and-building-the-open-agentic-web/

[2] Google Developers Blog. "Agent Development Kit: Making it easy to build multi-agent applications." April 9, 2025. https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/

[3] Google Developers Blog. "Announcing the Agent2Agent Protocol (A2A)." April 9, 2025. https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/

[4] Google Blog. "Google I/O 2025: 100 things Google announced." May 2025. https://blog.google/technology/ai/google-io-2025-all-our-announcements/

[5] Google A2A Documentation. "A2A and MCP - Agent2Agent Protocol (A2A)." https://google.github.io/A2A/topics/a2a-and-mcp/

[6] Google Cloud Blog. "Build and manage multi-system agents with Vertex AI." April 9, 2025. https://cloud.google.com/blog/products/ai-machine-learning/build-and-manage-multi-system-agents-with-vertex-ai

[7] GitHub. "BrainBlend-AI/atomic-agents: Building AI agents, atomically." https://github.com/BrainBlend-AI/atomic-agents

[8] Shakudo Blog. "Top 9 AI Agent Frameworks as of May 2025." May 2025. https://www.shakudo.io/blog/top-9-ai-agent-frameworks

[9] InfoQ. "Amazon Bedrock Introduces Multi-Agent Systems (MAS) with Open Source Framework Integration." January 2025. https://www.infoq.com/news/2025/01/aws-bedrock-multi-agent-ai/

[10] Shakudo. "AgentFlow | Secure AI agents using the best tools." https://www.shakudo.io/agentflow

[11] Blott Studio. "MCP vs A2A: Which Protocol Is Better For AI Agents? [2025]." April 13, 2025. https://www.blott.studio/blog/post/mcp-vs-a2a-which-protocol-is-better-for-ai-agents

[12] Microsoft Cloud Blog. "Empowering multi-agent apps with the open Agent2Agent (A2A) protocol." May 7, 2025. https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/05/07/empowering-multi-agent-apps-with-the-open-agent2agent-a2a-protocol/

[13] GitHub. "pydantic/pydantic-ai: Agent Framework / shim to use Pydantic with LLMs." https://github.com/pydantic/pydantic-ai

[14] Towards AI. "AutoGen, AG2, and Semantic Kernel: Complete Guide." January 27, 2025. https://towardsai.net/p/machine-learning/autogen-ag2-and-semantic-kernel-complete-guide

[15] Getting Started AI. "What's going on with AutoGen and AG2?" March 10, 2025. https://www.gettingstarted.ai/autogen-vs-ag2/

[16] OpenAI Agents SDK. "OpenAI Agents SDK Documentation." https://openai.github.io/openai-agents-python/

[17] n8n Blog. "9 AI Agent Frameworks Battle: Why Developers Prefer n8n." April 24, 2025. https://blog.n8n.io/ai-agent-frameworks/

[18] PyPI. "instructor: Instructor is the most popular Python library for working with structured outputs." https://pypi.org/project/instructor/

[19] PyPI. "marvin: A lightweight AI engineering framework for building natural language interfaces." https://pypi.org/project/marvin/1.4.0/

[20] Marvin Documentation. "Marvin Overview." https://www.askmarvin.ai/welcome

[21] GitHub. "PrefectHQ/marvin: AI agents that spark joy." https://github.com/PrefectHQ/marvin

Additional Resources

Industry Analysis Reports

• Langfuse Blog. "Comparing Open-Source AI Agent Frameworks." March 19, 2025. https://langfuse.com/blog/2025-03-19-ai-agent-comparison
• Turing Blog. "A Detailed Comparison of Top 6 AI Agent Frameworks in 2025." https://www.turing.com/resources/ai-agent-frameworks
• VentureBeat. "Google's Agent2Agent interoperability protocol aims to standardize agentic communication." April 9, 2025. https://venturebeat.com/ai/googles-agent2agent-interoperability-protocol-aims-to-standardize-agentic-communication/

Framework Documentation

• LangGraph Documentation: https://langchain-ai.github.io/langgraph/
• CrewAI Documentation: https://docs.crewai.com/
• Microsoft Semantic Kernel: https://learn.microsoft.com/en-us/semantic-kernel/
• Google Vertex AI Agent Builder: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-builder/overview
• AWS Bedrock Agents: https://aws.amazon.com/bedrock/agents/

Protocol Specifications

• Agent2Agent Protocol: https://google.github.io/A2A/
• Model Context Protocol: https://modelcontextprotocol.io/
• Agent Network Protocol: https://agent-network-protocol.com/

Research Methodology: This analysis is based on official announcements, documentation, and verified industry sources from major technology companies including Microsoft (Build 2025), Google (I/O 2025), Amazon Web Services, and leading AI framework providers. All citations include publication dates and direct source links for verification.