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Framework Decision Matrix

Choosing the right agent framework depends on your team, requirements, and constraints. This guide helps you make an informed decision based on comparisons with frameworks we’ve thoroughly researched: CrewAI, OpenAI AgentKit, Google ADK, Claude Agent SDK, LangGraph Cloud, and Microsoft Agent Framework.

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Quick Decision Tree

Use this interactive decision tree to find the best framework for your needs:
Legend: 🟢 MCP Server with LangGraph | 🔵 Alternative Frameworks | 🟠 Competitor Frameworks

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Framework Comparison Matrix

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By Primary Use Case

Use CaseBest FrameworkAlternative
Enterprise ProductionMCP Server with LangGraphGoogle ADK
Quick PrototypingCrewAIOpenAI AgentKit
Non-DeveloperOpenAI AgentKitMCP Server (visual builder coming)
Healthcare/HIPAAMCP Server with LangGraph-
Financial/SOC 2MCP Server with LangGraph-
Google CloudGoogle ADKMCP Server with LangGraph
Azure CloudMicrosoft Agent FrameworkMCP Server with LangGraph
Multi-CloudMCP Server with LangGraph-
RAG-HeavyLlamaIndexMCP Server with LangGraph
Claude-OnlyClaude Agent SDKMCP Server with LangGraph
OpenAI-OnlyOpenAI AgentKitLangGraph Cloud
Cost-SensitiveMCP Server with LangGraph (self-host)CrewAI
Role-Based TeamsCrewAIMCP Server with LangGraph

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By Team Type

  • Startup Team (2-5 people)
  • Enterprise Team (50+ people)
  • Product Team (no developers)
  • Developer Team (10-50 people)
Characteristics:
  • Limited resources
  • Need to move fast
  • Cost-sensitive
  • May not have DevOps
Recommended:1. MCP Server with LangGraph (if you have a developer)
  • Deploy to LangGraph Cloud (2 min, free tier)
  • Migrate to self-hosted as you scale
  • Avoid vendor lock-in from day 1
2. CrewAI (if you want simpler)
  • Fastest getting started
  • Great learning resources
  • Free open-source
3. OpenAI AgentKit (if non-technical)
  • Visual builder
  • No code needed
  • Quick demos

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By Technical Requirements

Need: Support for multiple LLM providers (not locked to one vendor)Best Choice: MCP Server with LangGraph
  • 100+ providers via LiteLLM
  • Automatic fallback/retry
  • Cost optimization (use cheapest model)
  • Provider independence
Alternatives:
  • CrewAI: Good multi-LLM support
  • Google ADK: 200+ providers via LiteLLM
Avoid:
  • OpenAI AgentKit (OpenAI only)
  • Claude Agent SDK (Claude only)
Need: JWT, SSO, RBAC, Audit Logging, GDPR/HIPAA/SOC 2Best Choice: MCP Server with LangGraph
  • JWT authentication
  • Keycloak SSO integration
  • OpenFGA authorization (Zanzibar model)
  • Complete audit logging
  • GDPR, HIPAA, SOC 2 ready
  • Network policies
  • Secrets management (Infisical)
Alternatives:
  • Google ADK: Good (if Google Cloud)
Avoid:
  • CrewAI (no enterprise security)
  • OpenAI AgentKit (basic auth only)
  • LangGraph Cloud (limited compliance controls)
Need: Deploy on GCP, AWS, Azure, or on-premisesBest Choice: MCP Server with LangGraph
  • GKE, EKS, AKS support
  • Terraform modules included
  • Helm charts for K8s
  • Cloud Run (GCP)
  • ECS (AWS)
  • Container Instances (Azure)
  • On-premises Kubernetes
Alternatives:
  • None (most frameworks are single-cloud or platform-only)
Avoid:
  • Google ADK (Google Cloud only)
  • Microsoft Agent Framework (Azure-focused)
  • OpenAI AgentKit (OpenAI Platform only)
  • LangGraph Cloud (platform only)
Need: Tracing, metrics, logs, dashboards, alertsBest Choice: MCP Server with LangGraph
  • Dual Stack:
    • LangSmith (LLM tracing, cost tracking)
    • OpenTelemetry (distributed tracing)
  • Prometheus metrics
  • Pre-built Grafana dashboards
  • Jaeger tracing
  • Alert manager
  • Structured logging
Alternatives:
  • LangGraph Cloud: LangSmith only
  • OpenAI AgentKit: Evals only
Avoid:
  • CrewAI (basic logging)
Need: Minimize costs at scale (>1M requests/month)Best Choice: MCP Server with LangGraph
  • Self-host (10-50x cheaper than managed)
  • Multi-LLM (use cheaper models)
  • Cloud Run: $500/mo for 1M requests
  • Kubernetes: $1,000/mo for 10M requests
Comparison:
  • OpenAI AgentKit: $5,000/mo for 1M requests
  • LangGraph Cloud: $5,000/mo for 1M requests
  • MCP Server (self-hosted): $500/mo for 1M requests
Winner: MCP Server with LangGraph (10x cheaper)

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Decision Scenarios

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Scenario 1: Healthcare Startup

Requirements:
  • HIPAA compliance
  • Patient data protection
  • Audit trails
  • Private cloud deployment
Recommendation: MCP Server with LangGraph Why:
  • ✅ HIPAA-ready architecture
  • ✅ Complete audit logging
  • ✅ Private cloud deployment
  • ✅ Network isolation (Kubernetes)
  • ✅ Data residency controls
Deployment:
  • GKE (Google Cloud, HIPAA-compliant region)
  • OR EKS (AWS, HIPAA-compliant region)
  • OpenFGA for patient data access control
  • Infisical for secrets management

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Scenario 2: Marketing Team Building Content Agents

Requirements:
  • Non-technical team
  • Quick demos needed
  • Visual workflow design
  • Low volume
Recommendation: OpenAI AgentKit Why:
  • ✅ Visual Agent Builder (no code)
  • ✅ Drag-and-drop workflow
  • ✅ Quick prototyping
  • ✅ ChatKit for embedding
  • ✅ Design is free
Deployment:
  • OpenAI Platform (one-click)
  • Pay only for API usage

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Scenario 3: Enterprise Customer Support (High Volume)

Requirements:
  • 10M requests/month
  • Multi-region deployment
  • SSO integration
  • Cost optimization
Recommendation: MCP Server with LangGraph Why:
  • ✅ Cost: 1,000/mo(vs1,000/mo (vs 50,000/mo on Platform)
  • ✅ Keycloak SSO
  • ✅ Multi-region (GKE, EKS, AKS)
  • ✅ Auto-scaling
  • ✅ Complete observability
Deployment:
  • Kubernetes multi-region
  • LiteLLM with fallback (Gemini → Claude → GPT)
  • Grafana dashboards for support metrics

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Scenario 4: Research Team Exploring Agents

Requirements:
  • Learning/experimentation
  • Multiple team members (students/researchers)
  • Limited budget
  • Need good documentation
Recommendation: CrewAI Why:
  • ✅ Easiest getting started
  • ✅ Excellent learning resources (learn.crewai.com)
  • ✅ Role-based model (intuitive)
  • ✅ Free open-source
  • ✅ 100,000+ certified developers
Deployment:
  • Local development
  • Docker for team sharing

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Scenario 5: Google Cloud Native Company

Requirements:
  • Already on Google Cloud
  • Using Vertex AI
  • Gemini models
  • Agent hierarchies needed
Recommendation: Google ADK OR MCP Server with LangGraph Why Google ADK:
  • ✅ Native Google Cloud integration
  • ✅ Vertex AI Agent Engine
  • ✅ Gemini optimized
  • ✅ A2A protocol support
Why MCP Server with LangGraph:
  • ✅ Also supports Google Cloud (GKE, Cloud Run)
  • ✅ Multi-cloud (avoid lock-in)
  • ✅ More mature (LangGraph 1.0)
  • ✅ Complete observability
Decision: If 100% sure you’ll stay on Google Cloud → Google ADK. Otherwise → MCP Server with LangGraph.

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Scenario 6: Financial Services (Compliance Critical)

Requirements:
  • SOC 2 compliance
  • Audit trails
  • Data residency (EU)
  • Fine-grained permissions
Recommendation: MCP Server with LangGraph Why:
  • ✅ SOC 2 ready (complete audit logging)
  • ✅ OpenFGA (fine-grained permissions)
  • ✅ Deploy in EU region (GKE/EKS)
  • ✅ Network isolation
  • ✅ Secrets management (Infisical)
  • ✅ Binary authorization (GKE)
Deployment:
  • GKE (europe-west1)
  • OR EKS (eu-central-1)
  • Keycloak for identity
  • OpenFGA for permissions

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Framework Strengths Summary

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MCP Server with LangGraph

Best For:
  • Enterprise production deployments
  • Regulated industries (healthcare, finance)
  • Multi-cloud requirements
  • High-volume applications (>1M requests/mo)
  • Cost optimization needs
  • Complete observability requirements
Strengths:
  • Production-ready from day one
  • Enterprise security (JWT, OpenFGA, Keycloak)
  • Multi-cloud deployment (GCP, AWS, Azure)
  • 100+ LLM providers
  • Dual observability (LangSmith + OTEL)
  • Cost-effective at scale (self-hosting)
  • Complete documentation
Limitations:
  • Steeper learning curve (graph concepts)
  • Requires infrastructure knowledge
  • More complex setup (but documented)

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CrewAI

Best For:
  • Rapid prototyping
  • Learning/experimentation
  • Role-based agent workflows
  • Small teams (2-5 agents)
Strengths:
  • Easiest getting started
  • Excellent documentation (learn.crewai.com)
  • Intuitive role-based model
  • Fast execution (5.76x vs LangGraph in some cases)
  • Great community (100K+ devs)
Limitations:
  • Lacks enterprise security
  • No production deployment guides
  • Scaling requires meticulous effort
  • No built-in observability

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OpenAI AgentKit

Best For:
  • Non-technical users
  • Visual workflow design
  • Quick demos/prototypes
  • Embedding chat (ChatKit)
Strengths:
  • Visual Agent Builder (no code)
  • Drag-and-drop workflows
  • Easy for non-developers
  • Connector registry (centralized)
  • Evals framework
Limitations:
  • OpenAI models only (vendor lock-in)
  • Cannot self-host
  • Still in beta
  • Expensive at scale
  • Limited enterprise security

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Google ADK

Best For:
  • Google Cloud native companies
  • Gemini model focus
  • Multi-agent hierarchies
  • A2A protocol needs
Strengths:
  • Excellent Google Cloud integration
  • Vertex AI Agent Engine
  • 200+ LLM providers via LiteLLM
  • Hierarchical multi-agent
  • Audio/video streaming
Limitations:
  • Google Cloud lock-in
  • Relatively new (v1.0 in 2025)
  • Less community adoption
  • Limited documentation

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LangGraph Cloud

Best For:
  • Serverless-only preference
  • No DevOps team
  • Quick prototyping
  • Low volume (<100K requests/mo)
Strengths:
  • 2-minute deployment
  • Fully managed
  • LangSmith integration
  • Automatic scaling
Limitations:
  • Platform lock-in
  • Expensive at scale
  • Limited enterprise security
  • No self-hosting option

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Claude Agent SDK

Best For:
  • Claude models exclusively
  • Code-heavy agent tasks
  • Automatic context management
Strengths:
  • Excellent Claude integration
  • Automatic context management
  • Production infrastructure (from Claude Code)
  • TypeScript + Python SDKs
Limitations:
  • Claude models only
  • Requires Pro subscription
  • Newer ecosystem
  • Limited deployment options

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Migration Paths Between Frameworks

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From Prototype to Production

Prototyped with CrewAI → Production with MCP Server with LangGraph
1

Map Roles to Graph Nodes

Convert CrewAI agent roles to LangGraph nodes
2

Add Enterprise Features

Configure JWT, OpenFGA, observability
3

Deploy

Start with LangGraph Platform, migrate to K8s
Prototyped with OpenAI AgentKit → Production with MCP Server with LangGraph
1

Export Visual Workflows

Document Agent Builder node configurations
2

Recreate in LangGraph

Map visual nodes to Python code
3

Add Multi-LLM Support

Replace OpenAI-only with LiteLLM
4

Deploy

Self-host for cost optimization

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Pricing Comparison Calculator

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Cost per 1M Requests/Month

FrameworkInfrastructureLLM CostsTotalNotes
OpenAI AgentKit$0$5,000$5,000GPT-4 + web search
LangGraph Cloud$5,000$0$5,000Node execution fees
MCP Server (Cloud Run)$500$7.50$507Gemini Flash
MCP Server (Kubernetes)$300$7.50$307Gemini Flash
CrewAI (self-hosted)$200$7.50$207Compute + Gemini
Google ADK (Agent Engine)$1,000$10$1,010Gemini Pro
Cost Calculations: Infrastructure costs based on real cloud pricing (2025). LLM costs assume 5 tokens/request average. Your costs may vary based on actual token usage, model selection, and deployment configuration. Self-hosting provides 10-50x cost savings at scale but requires DevOps expertise.
Winner for Cost: MCP Server with LangGraph (10-16x cheaper at scale when self-hosting)

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Final Recommendations

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Choose MCP Server with LangGraph If:

  • ✅ Any of these apply:
  • Going to production
  • Enterprise/compliance requirements
  • High volume (>1M requests/mo)
  • Multi-cloud needed
  • Cost optimization critical
  • Complete observability needed
  • Developer team available

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Choose CrewAI If:

  • ✅ All of these apply:
  • Prototyping/learning
  • No enterprise requirements
  • Small team (2-5 agents)
  • Role-based model fits
  • Python developers

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Choose OpenAI AgentKit If:

  • ✅ Any of these apply:
  • Non-technical team
  • Need visual builder
  • OpenAI commitment OK
  • Low volume
  • Quick demos needed

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Choose Google ADK If:

  • ✅ All of these apply:
  • 100% Google Cloud
  • Gemini models priority
  • Google Cloud lock-in acceptable
  • Multi-agent hierarchies needed

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Choose LangGraph Cloud If:

  • ✅ All of these apply:
  • Serverless only (never self-host)
  • Low volume (<100K/mo)
  • No DevOps team
  • LangChain lock-in acceptable

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When MCP Server Might Be Overkill:

Consider simpler alternatives if:
  • Prototyping/Learning → Start with CrewAI (faster, easier learning curve)
  • Non-technical team → Use OpenAI AgentKit (visual builder, no code required)
  • 100% Google Cloud → Google ADK may be simpler if you’ll never leave GCP
  • Claude-only forever → Claude Agent SDK has better auto-context management
  • Sub-10K requests/month → Managed platforms may be simpler despite higher per-request costs
MCP Server excels at production scale, multi-cloud flexibility, and enterprise requirements. If those aren’t your priorities, a simpler framework may serve you better.

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Still Not Sure?

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Decision Flowchart

Start Here:
  1. Developer? → Yes
  2. Production? → Yes
  3. Enterprise? → Yes
Result: MCP Server with LangGraph
Honest Advice: Most teams will eventually need production features (security, observability, scaling). Starting with MCP Server with LangGraph saves migration effort later, even if the initial learning curve is steeper.