MCP Server with LangGraph vs OpenAI AgentKit

Comparison between MCP Server with LangGraph and OpenAI AgentKit for agent development

Overview

Last Updated: November 2025 (v2.8.0) | View all framework comparisons →

OpenAI AgentKit is OpenAI’s agent platform announced at DevDay 2025, featuring Agent Builder (visual workflow designer), ChatKit (embeddable chat), and Evals (evaluation framework). It’s designed for low-code/no-code agent development tightly integrated with OpenAI models.

This comparison reflects our research and analysis. Please review OpenAI’s official documentation for the most current information. See our Sources & References for citations.

MCP Server with LangGraph is a code-first, production-ready MCP server with 100+ LLM providers, enterprise security, and multi-cloud deployment flexibility.

Quick Comparison

Aspect	OpenAI AgentKit	MCP Server with LangGraph
Approach	Low-code/no-code (visual)	Code-first (Python)
LLM Support	❌ OpenAI only	✅ 100+ providers
Development	✅ Visual Agent Builder	✅ Code-first with type safety
Deployment	⚠️ OpenAI Platform only	✅ Multi-cloud (GCP, AWS, Azure)
Pricing	Usage-based (API costs)	Self-hosted OR Platform
Enterprise Auth	⚠️ Basic	✅ JWT + Keycloak + OpenFGA
Disaster Recovery	❌ Not available	✅ Complete (automated backups, multi-region)
Observability	⚠️ Basic (Evals only)	✅ LangSmith + OTEL + Grafana
Status	Beta (Agent Builder)	✅ Production-ready
Vendor Lock-in	⚠️ OpenAI only	✅ Provider-agnostic
Best For	Non-developers, prototyping	Developers, production

Detailed Feature Comparison

Development Experience

OpenAI AgentKit

Agent Builder (Visual):

• Drag-and-drop workflow canvas
• Node-based agent composition
• No-code orchestration
• Visual debugging

Example Workflow:

1. Open Agent Builder in browser
2. Drag nodes (agents, tools, conditionals)
3. Connect with edges
4. Test in playground
5. Deploy to OpenAI Platform

ChatKit (Embeddable):

<!-- Embed chat interface -->
<script src="https://cdn.openai.com/chatkit.js"></script>
<div id="chatkit" data-agent-id="your-agent"></div>

Strengths:

• Zero code needed for simple agents
• Visual workflow is intuitive
• Quick prototyping
• Easy for non-developers

Limitations:

• Limited to visual builder capabilities
• Code customization difficult
• Still in beta
• Less control over agent logic

MCP Server with LangGraph

Code-First Development:

from langgraph.graph import StateGraph
from mcp_server_langgraph.core import AgentState

# Define state
class MyAgentState(AgentState):
    query: str
    result: str

# Create graph
graph = StateGraph(MyAgentState)
graph.add_node("search", search_function)
graph.add_node("summarize", summarize_function)
graph.add_edge("search", "summarize")

# Compile and run
app = graph.compile(checkpointer=checkpointer)
result = app.invoke({"query": "research topic"})

Visual Builder (Coming Soon):

• React Flow-based graph editor
• Drag-and-drop with code export
• Import existing code
• Live preview

Strengths:

• Complete control over logic
• Testable, versionable code
• Production-grade patterns
• Type safety with Pydantic

Considerations:

• Requires Python knowledge
• Steeper learning curve
• More powerful but more complex

Winner for Non-Developers: OpenAI AgentKit (visual, no-code) Winner for Developers: MCP Server with LangGraph (code control, flexibility)

LLM Provider Support

Feature	OpenAI AgentKit	MCP Server with LangGraph
OpenAI Models	✅ GPT-4, GPT-4 Turbo, GPT-4o	✅ All OpenAI models
Anthropic Claude	❌ No	✅ Claude 3.5 Sonnet, Opus
Google Gemini	❌ No	✅ Gemini 2.5 Flash, Pro
Azure OpenAI	❌ No	✅ Supported
AWS Bedrock	❌ No	✅ Supported
Local Models	❌ No	✅ Ollama (Llama, Mistral)
Total Providers	1 (OpenAI)	100+ via LiteLLM
Fallback/Retry	❌ No	✅ Automatic
Cost Optimization	⚠️ OpenAI pricing only	✅ Switch to cheaper providers

Better for multi-provider: MCP Server with LangGraph (100+ providers, prevents vendor lock-in) Better for OpenAI-only: OpenAI AgentKit (optimized for OpenAI ecosystem, simpler setup)

Agent Builder Comparison

OpenAI Agent Builder (Visual)

Status: Beta (as of Oct 2025)

Features:

• Visual canvas for workflows
• Drag-and-drop nodes
• Pre-built agent templates
• Connector registry for integrations
• No-code orchestration

Node Types:

• Agent nodes (with tools)
• Conditional logic
• Data transformations
• API calls via connectors

Deployment:

• One-click deploy to OpenAI Platform
• Automatic scaling
• Built-in hosting

Pricing:

• Design is FREE (no charge for using builder)
• Pay only for API usage in production
• $10 per 1k web search calls

Strengths:

• Most user-friendly
• No code needed
• Quick iteration
• Centralized connector management

Limitations:

• Beta quality (bugs expected)
• Limited to OpenAI Platform
• Can’t self-host
• Less customization
• OpenAI models only

MCP Server Code-First Approach

Status: Production-ready

Current Features:

• Type-safe Python development (Pydantic)
• Full code control and customization
• Version control friendly (Git)
• Testable (437 test suite included)
• CI/CD ready
• IDE support with autocomplete

Approach:

• Code-first development
• Maximum flexibility and control
• Production-grade patterns

Strengths:

• Full code control
• Works with any LLM provider
• Can self-host anywhere
• Production-grade output
• Mature, stable framework

Considerations:

• Requires Python knowledge
• No visual builder (code only)

Winner for Non-Developers: OpenAI AgentKit (visual builder available now) Winner for Developers: MCP Server with LangGraph (code control, flexibility, production-ready)

Authentication & Authorization

Feature	OpenAI AgentKit	MCP Server with LangGraph
Authentication	⚠️ API keys	✅ JWT + Keycloak SSO
Authorization	⚠️ Basic (per-agent)	✅ OpenFGA (Google Zanzibar model)
SSO Integration	❌ No	✅ SAML, OAuth, OIDC
Service Principals	⚠️ Limited	✅ Full support
Fine-Grained Permissions	❌ No	✅ Relationship-based (OpenFGA)
Audit Logging	⚠️ Basic	✅ Complete security events
Multi-Tenancy	⚠️ Account-based	✅ Tenant isolation

Better for enterprise security: MCP Server with LangGraph (comprehensive security features) Better for simple use cases: OpenAI AgentKit (basic auth sufficient, faster setup)

Deployment Options

OpenAI AgentKit Deployment

Single Option: OpenAI Platform

Deployment:

# Visual builder: Click "Deploy" button
# OR CLI (if available):
openai deploy

Characteristics:

• Fully managed serverless
• Zero infrastructure
• Automatic scaling
• Global CDN
• No control over hosting

Pricing:

• No separate AgentKit fee
• Pay for API usage:
  • GPT-4: $10-30 per 1M tokens
  • GPT-4o: $2.50-10 per 1M tokens
• Web search: $10 per 1k calls
• ChatKit: $0.10 per GB-day storage

Pros:

• Simplest deployment
• No DevOps needed
• Handles scaling

Cons:

• Cannot self-host
• Vendor lock-in
• No private cloud
• Expensive at scale
• OpenAI Platform only

MCP Server with LangGraph Deployment

Multiple Options:

1. LangGraph Platform (2 min):

langgraph deploy

2. Google Cloud Run (10 min):

gcloud run deploy --source .

3. Kubernetes (1-2 hours):

helm install mcp-server ./deployments/helm

4. Docker (15 min):

docker compose up -d

5. AWS ECS, Azure Container Instances, etc.

Pricing:

• Platform: $0.001/node execution
• Cloud Run: $0.40-2.00 per 1M requests
• Kubernetes: ~$200-500/mo (cluster)
• 10-50x cheaper at scale

Pros:

• Deployment flexibility
• No vendor lock-in
• Cost optimization options
• Private cloud support
• Multi-region

Cons:

• Requires infrastructure knowledge
• More complex (but documented)

Winner for Simplicity: OpenAI AgentKit Winner for Flexibility & Cost: MCP Server with LangGraph

Observability & Evaluation

OpenAI AgentKit Observability

Evals (Evaluation Framework):

• Dataset management
• Trace grading
• Automated prompt optimization
• Third-party model support for evals

Characteristics:

• Focused on evaluation
• Good for testing/optimization
• Basic production monitoring

Limitations:

• No infrastructure metrics
• Limited tracing
• No custom dashboards
• Evals-focused (not ops-focused)

MCP Server with LangGraph Observability

Dual Observability Stack:

LangSmith (LLM-focused):

• Complete trace visualization
• Prompt engineering insights
• Evaluation datasets
• Cost tracking per request
• Debugging tools

OpenTelemetry (Infrastructure):

• Distributed tracing (Jaeger)
• Prometheus metrics
• Grafana dashboards (pre-built)
• Alert manager
• Custom metrics

Production Features:

• Structured JSON logging
• Trace correlation
• Infrastructure metrics (CPU, memory, latency)
• Business metrics dashboards
• On-call alerting

Strengths:

• Complete production visibility
• LLM + infrastructure monitoring
• Enterprise-grade alerting

Winner for Evaluation: OpenAI Evals (focused tool) Winner for Production Ops: MCP Server with LangGraph (complete stack)

Connector Ecosystem

Feature	OpenAI AgentKit	MCP Server with LangGraph
Connector Registry	✅ Centralized	🔄 Coming soon (Plugin Registry)
Admin Management	✅ Yes	⚠️ Manual currently
Pre-built Connectors	✅ OpenAI ecosystem	⚠️ MCP tools + custom
Third-Party	✅ Via registry	✅ MCP protocol support
Custom Connectors	⚠️ Limited	✅ Full flexibility

Current Winner: OpenAI AgentKit (centralized registry) Future: MCP Server with LangGraph (plugin marketplace planned)

Pricing Comparison

Cost Analysis

OpenAI AgentKit Costs

No AgentKit Fee:

• Agent Builder: FREE
• Connector Registry: FREE
• Evals: FREE
• ChatKit: $0.10 per GB-day (after 1GB free)

Pay for Usage:

• API calls (standard OpenAI pricing)
• Web search: $10 per 1k calls

Example: 1M requests/month

• 5M tokens (avg 5 tokens/request)
• GPT-4: $150/month (input/output)
• Web search (50% use): $5,000/month
• Total: ~$5,150/month

Characteristics:

• No infrastructure costs
• Usage-based (predictable)
• Expensive at high volume
• No way to optimize (locked to OpenAI)

MCP Server with LangGraph Costs

Infrastructure:

• LangGraph Platform: $0.001/node
• Cloud Run: $0.40-2.00 per 1M requests
• Kubernetes: $200-500/month (fixed)

LLM Usage:

• Flexible providers (choose cheapest)
• Gemini Flash: $0.075 per 1M tokens (20x cheaper)
• Claude Haiku: $0.25 per 1M tokens (6x cheaper)
• GPT-4o Mini: $0.15 per 1M tokens

Example: 1M requests/month

• Infrastructure (Cloud Run): $500/month
• LLM (Gemini Flash): $7.50/month
• Search (Tavily): $100/month
• Total: ~$607/month

Savings: 8.5x cheaper than OpenAI AgentKit at 1M requests/month

Characteristics:

• Flexible cost optimization
• Switch to cheaper providers
• Self-host to reduce costs
• LLM fallback (use cheap models first)

Better for high volume (>1M req/mo): MCP Server with LangGraph (5-10x cheaper when self-hosting) Better for low volume (<100K req/mo): OpenAI AgentKit (no DevOps costs, pay-per-use)

When to Choose Each Option

Choose OpenAI AgentKit When:

• ✅ Non-Technical Team - No developers, need visual builder
• ✅ OpenAI Commitment - Already using OpenAI exclusively
• ✅ Quick Prototyping - Need to demo in hours
• ✅ No DevOps - Want zero infrastructure management
• ✅ Simple Use Cases - Basic agent workflows
• ✅ ChatKit Needed - Want embeddable chat component
• ✅ Small Scale - Low volume (<10K requests/month)

Example Use Cases:

• Marketing team building content agents
• Customer support triage (low volume)
• Internal tools for non-developers
• Rapid prototyping/demos
• Simple FAQ bots

Choose MCP Server with LangGraph When:

• ✅ Developer Team - Have Python developers
• ✅ Production Scale - High volume (>100K requests/month)
• ✅ Cost Optimization - Want to control LLM costs
• ✅ Multi-LLM - Need provider flexibility (not OpenAI-only)
• ✅ Enterprise Security - Need JWT, SSO, OpenFGA
• ✅ Self-Hosting - Want/need to host on own infrastructure
• ✅ Compliance - GDPR, HIPAA, SOC 2 required
• ✅ Complex Workflows - Advanced agent patterns
• ✅ MCP Protocol - Building MCP-compatible system
• ✅ Multi-Cloud - Want deployment flexibility

Example Use Cases:

• Enterprise production applications
• High-volume customer support (>100K/mo)
• Financial services (compliance required)
• Healthcare applications (HIPAA)
• Multi-region deployments
• Cost-sensitive high-volume apps

Hybrid Approach

Can You Use Both? Technically yes, but they serve different audiences. Consider:

• Prototype with OpenAI AgentKit (fast, visual)
• Rebuild with MCP Server with LangGraph for production (when you need scale, security, cost optimization)

Migration Path

From OpenAI AgentKit to MCP Server with LangGraph

1. Export Agent Logic

   Document your Agent Builder workflows:

   • Node types and configurations
   • Tool/connector integrations
   • Conditional logic
   • Data transformations
2. Recreate in LangGraph

   # Map Agent Builder nodes to LangGraph
   graph = StateGraph(AgentState)
   
   # Add nodes (agents/tools from AgentKit)
   graph.add_node("step1", function1)
   graph.add_node("step2", function2)
   
   # Add edges (connections from visual builder)
   graph.add_edge("step1", "step2")
   
3. Integrate Tools

   • Replace OpenAI connectors with MCP tools
   • Add LiteLLM for multi-provider support
   • Configure authentication (JWT)
4. Deploy

   • Start with LangGraph Platform (same serverless experience)
   • Migrate to Cloud Run or Kubernetes for cost optimization
   • Enable observability (LangSmith + OTEL)

Migration Effort: Typical visual workflow migrates in 1-3 days. Most effort in recreating visual logic as code, not integration complexity.

Feature Maturity

Feature	OpenAI AgentKit	MCP Server with LangGraph
Agent Builder	⚠️ Beta	❌ Not available (code-first)
ChatKit	✅ GA	❌ Not available
Evals	✅ GA	✅ LangSmith (production)
Production Ready	⚠️ Beta (expect bugs)	✅ 100% test pass (437/437)
Documentation	⚠️ In progress	✅ Complete
Enterprise Features	❌ Limited	✅ Complete

Maturity Winner: MCP Server with LangGraph (production-ready now)

Summary

Criteria	Winner
Visual Builder	🏆 OpenAI AgentKit (exists now)
Code Control	🏆 MCP Server with LangGraph
LLM Flexibility	🏆 MCP Server with LangGraph
Ease of Use	🏆 OpenAI AgentKit
Production Ready	🏆 MCP Server with LangGraph
Enterprise Security	🏆 MCP Server with LangGraph
Cost at Scale	🏆 MCP Server with LangGraph
Deployment Flexibility	🏆 MCP Server with LangGraph
Observability	🏆 MCP Server with LangGraph
Quick Prototyping	🏆 OpenAI AgentKit

Overall:

• OpenAI AgentKit: Best for non-developers and quick prototypes
• MCP Server with LangGraph: Best for developers and production deployments

Ideal Strategy:

1. Prototype: Use OpenAI AgentKit visual builder (if non-developer) OR MCP Server with LangGraph quick-start (if developer)
2. Production: Use MCP Server with LangGraph for scale, security, and cost optimization

When NOT to Use MCP Server with LangGraph:

Choose OpenAI AgentKit instead if:

• ❌ Non-technical team - MCP Server requires Python development skills
• ❌ Need visual workflow builder NOW - MCP Server is code-first only (no visual builder)
• ❌ OpenAI models are sufficient - No need for multi-provider complexity if OpenAI meets all needs
• ❌ Zero DevOps capacity - OpenAI AgentKit requires no infrastructure management
• ❌ Low volume (under 10K requests/month) - OpenAI’s pay-per-use is simpler for low traffic

MCP Server is overkill if:

• You’re building simple chatbots or FAQ agents (OpenAI AgentKit’s visual builder is faster)
• Your team prefers drag-and-drop over code
• You’re okay with OpenAI vendor lock-in for the convenience
• You need a working demo in the next 2 hours (visual builder wins for speed)

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