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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.

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Quick Comparison

AspectOpenAI AgentKitMCP Server with LangGraph
ApproachLow-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)
PricingUsage-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
StatusBeta (Agent Builder)✅ Production-ready
Vendor Lock-in⚠️ OpenAI only✅ Provider-agnostic
Best ForNon-developers, prototypingDevelopers, production

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Detailed Feature Comparison

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Development Experience

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

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LLM Provider Support

FeatureOpenAI AgentKitMCP 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 Providers1 (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)

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Agent Builder Comparison

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
Status: Production-readyCurrent 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)

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Authentication & Authorization

FeatureOpenAI AgentKitMCP 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)

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Deployment Options

  • OpenAI AgentKit Deployment
  • MCP Server with LangGraph Deployment
Single Option: OpenAI PlatformDeployment:
# 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
Winner for Simplicity: OpenAI AgentKit Winner for Flexibility & Cost: MCP Server with LangGraph

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Observability & Evaluation

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)
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)

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Connector Ecosystem

FeatureOpenAI AgentKitMCP 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)

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Pricing Comparison

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Cost Analysis

  • OpenAI AgentKit Costs
  • MCP Server with LangGraph 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)
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)

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When to Choose Each Option

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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

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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

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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)

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Migration Path

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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.

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Feature Maturity

FeatureOpenAI AgentKitMCP 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)

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Summary

CriteriaWinner
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

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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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