Executive Summary: OpenAI AgentKit and Dify represent two distinct approaches to AI agent development. AgentKit, announced at OpenAI’s DevDay 2025, offers a comprehensive, proprietary toolkit designed to streamline agent creation within the OpenAI ecosystem. Dify, an open-source platform, provides extensive flexibility with multi-provider LLM support and full infrastructure control. This guide examines both platforms in depth to help you make an informed decision.

🚀 Platform Overview

OpenAI AgentKit

Launched October 2025 at DevDay, AgentKit is OpenAI’s complete toolkit for building production-ready AI agents with minimal friction.

• Proprietary platform by OpenAI
• Cloud-based deployment
• Deep OpenAI ecosystem integration
• Enterprise-grade security built-in
• Visual drag-and-drop builder
• Rapid prototyping (agents in hours, not months)

Dify

Open-source LLMOps platform with 180,000+ developers, supporting comprehensive AI application development with full control.

• 100% open-source platform
• Self-hosted or cloud deployment
• Multi-provider LLM support (GPT, Claude, Llama, etc.)
• Complete data sovereignty
• Extensive RAG capabilities
• Active community of 180,000+ developers

🎯 OpenAI AgentKit – Core Features

🎨 Agent Builder

A visual canvas for creating and versioning multi-agent workflows using drag-and-drop functionality. Developers can design complex agent interactions without extensive coding.

• Visual workflow designer
• Version control for agent workflows
• Multi-agent orchestration
• Real-time collaboration
• 70% faster iteration cycles reported

💬 ChatKit

Embeddable, customizable chat interfaces that can be integrated directly into your applications with your own branding and workflows.

• White-label chat interfaces
• Custom branding options
• Pre-built UI components
• Seamless product integration
• Mobile-responsive design

🔌 Connector Registry

Centralized admin dashboard for managing secure connections between agents and both internal tools and third-party systems.

• Pre-built connectors: Dropbox, Google Drive, SharePoint, Teams
• Secure data access management
• Admin control panel
• Third-party MCP server support
• Enterprise-grade security controls

📊 Evaluation & Optimization

Comprehensive tools for measuring and improving agent performance with automated testing and optimization.

• Datasets for component testing
• End-to-end trace grading
• Automated prompt optimization
• Third-party model evaluation support
• Custom grading criteria

🔒 Security & Guardrails

Built-in security layers protecting against data leakage, jailbreaks, and unintended behaviors.

• PII leak detection and prevention
• Jailbreak protection
• Content filtering
• OpenAI’s standard security measures
• Compliance-ready infrastructure

⚡ Performance

Optimized for rapid development and deployment with impressive benchmarks demonstrated at DevDay 2025.

• Live demo: 2 agents built in <8 minutes
• Hours to deploy vs months traditionally
• Built on Responses API
• Integration with GPT-5 Codex
• Dynamic thinking time adjustment

🎯 Real-World Success Story

Ramp (Fintech): Built a complete procurement agent in just a few hours instead of months using AgentKit. Their teams reported a 70% reduction in iteration cycles, launching agents in two sprints rather than two quarters. Agent Builder enabled seamless collaboration between product, legal, and engineering teams on the same visual canvas.

🛠️ Dify – Core Features

🎯 Visual Workflow Builder

Intuitive canvas for building and testing AI workflows with comprehensive model support and visual orchestration.

• Drag-and-drop workflow design
• Support for 100+ LLM models
• Real-time debugging with node inspection
• Variable tracking during execution
• Instant step-by-step testing

🧠 Comprehensive Model Support

Seamless integration with hundreds of proprietary and open-source LLMs from multiple providers.

• OpenAI: GPT-3.5, GPT-4, GPT-5
• Anthropic: Claude models
• Open-source: Llama3, Mistral, Qwen
• Self-hosted model support
• Any OpenAI API-compatible model

📚 RAG Pipeline

Extensive Retrieval-Augmented Generation capabilities covering the entire document lifecycle.

• Document ingestion from multiple formats
• PDF, PPT, Word extraction
• Vector database integration
• Advanced retrieval strategies
• Metadata-based filtering for security

🤖 Agent Node System

Flexible agent architecture with customizable strategies for autonomous decision-making within workflows.

• Plug-in “Agent Strategies”
• Autonomous task handling
• Custom tool integration
• Multi-agent collaboration
• Dynamic workflow adaptation

🎛️ Prompt Engineering IDE

Intuitive interface for crafting, testing, and comparing prompts across different models.

• Visual prompt editor
• Model performance comparison
• A/B testing capabilities
• Text-to-speech integration
• Template management

📊 Observability & Operations

Full visibility into AI application performance with comprehensive logging and monitoring.

• Complete execution logs
• Cost tracking per execution
• Conversation auditing
• Performance metrics dashboard
• Version control for workflows

🏢 Enterprise Features

Production-ready infrastructure with enterprise-grade security and scalability.

• Self-hosted deployment options
• AWS Marketplace integration
• Custom branding and white-labeling
• SSO and access control
• Multi-tenant architecture

🌐 Open Source Advantage

Community-driven development with transparent roadmap and extensive customization options.

• 180,000+ developer community
• 34,800+ GitHub stars
• Regular feature updates
• Community plugins and extensions
• Full code access and customization

🎯 Real-World Success Story

Volvo Cars: Uses Dify for rapid AI validation and deployment, enabling teams to quickly design and deploy complex NLP pipelines. This approach significantly improved assessment product quality while reducing both cost and time to market. Dify’s democratized AI development allows even non-technical team members to contribute to AI initiatives.

⚖️ Detailed Comparison

✅ Pros & Cons Analysis

OpenAI AgentKit

Advantages

• Rapid Development: Build functional agents in hours rather than months with visual tools
• Seamless Integration: Deep integration with OpenAI ecosystem and GPT models
• Enterprise Security: Built-in guardrails, PII protection, and OpenAI security standards
• Managed Infrastructure: No DevOps burden, fully managed by OpenAI
• Cutting-Edge Models: Immediate access to latest GPT models and features
• Live Demo Success: Proven capability (2 agents in <8 minutes)
• Unified Toolkit: All necessary tools in one platform
• Evaluation Tools: Comprehensive testing and optimization features

Limitations

• Vendor Lock-in: Exclusively tied to OpenAI ecosystem
• Limited Model Choice: Cannot use Claude, Llama, or other non-OpenAI models
• New Platform: Just launched (Oct 2025), limited production track record
• Beta Features: Key components still in beta (Agent Builder, Connector Registry)
• No Data Sovereignty: Data managed by OpenAI, not self-hostable
• Closed Source: Cannot inspect or modify underlying code
• Pricing Uncertainty: Costs tied to OpenAI API pricing model
• Limited Customization: Constrained by platform design decisions

Dify

Advantages

• Open Source Freedom: Full code access, unlimited customization, no vendor lock-in
• Multi-Provider Support: Use any LLM – GPT, Claude, Llama, Mistral, or self-hosted models
• Data Sovereignty: Complete control over data, self-hosting options
• Extensive RAG: Comprehensive document processing and retrieval capabilities
• Large Community: 180,000+ developers, active development, extensive resources
• Production Proven: Battle-tested since 2023, used by major companies like Volvo
• Flexible Deployment: Cloud, self-hosted, or hybrid options
• Cost Control: Use cheaper models or self-hosted options, transparent pricing
• No Vendor Dependencies: Switch providers or models without platform changes

Limitations

• DevOps Responsibility: Self-hosting requires infrastructure management
• Learning Curve: More complex than managed solutions for beginners
• No Native OpenAI Features: Latest OpenAI-specific features may lag
• Security Setup: Must configure own security measures for self-hosted
• Community Support: Relies on community vs dedicated support team
• Integration Effort: May require more work to integrate custom tools
• Scalability Management: Need to handle scaling for high-traffic scenarios

💡 Use Cases & Applications

OpenAI AgentKit – Ideal Use Cases

🏢 Enterprise Rapid Prototyping

Large organizations already invested in OpenAI wanting to quickly deploy AI agents across multiple departments without heavy technical overhead.

🚀 Startup MVPs

Startups needing to build and iterate on AI-powered products rapidly with minimal infrastructure investment and maximum speed to market.

💼 Business Process Automation

Companies automating internal workflows like procurement, customer support, or data analysis using OpenAI’s latest models.

🔬 Research & Development

Teams exploring cutting-edge AI capabilities with OpenAI’s latest models and wanting managed infrastructure for experiments.

Dify – Ideal Use Cases

🏦 Regulated Industries

Banking, healthcare, or government organizations requiring full data sovereignty, self-hosting, and complete audit trails.

🌐 Multi-Model Applications

Projects needing to leverage multiple LLM providers for cost optimization, feature diversity, or redundancy.

🛠️ Custom AI Solutions

Development teams building highly customized AI applications requiring deep integration with existing systems and workflows.

📚 Knowledge Management

Organizations building comprehensive RAG systems with complex document processing, vector search, and metadata filtering needs.

🎓 Educational & Research

Academic institutions and researchers needing transparent, customizable AI systems with full control over model selection and data.

🌍 Global Operations

International companies needing to deploy AI across multiple regions with varying data residency requirements.

💰 Pricing Comparison

OpenAI AgentKit Pricing

Model: Included with standard OpenAI API pricing. You pay for:

• API calls to GPT models (token-based pricing)
• Standard OpenAI usage fees apply
• Enterprise features may have additional costs
• Connector Registry requires Global Admin Console (available for Enterprise/Edu)

Advantage: No separate platform fee, but tied to OpenAI’s pricing

Consideration: Costs can scale significantly with high usage; no control over rate changes

Dify Pricing

Sandbox (Free):

• 200 OpenAI calls included
• Core features access
• Ideal for testing and small projects

Professional ($59/month):

• For independent developers & small teams
• Production AI applications
• Increased resources and team collaboration

Team ($159/month):

• Medium-sized teams
• Higher throughput requirements
• Advanced collaboration features

Enterprise (Custom):

• Custom deployment options
• Dedicated support
• SLA guarantees
• On-premise or private cloud hosting

Self-Hosted (Free):

• Deploy on your own infrastructure at no platform cost
• Only pay for your chosen LLM provider (can use cheaper options)
• Complete cost control

🎯 Decision Framework: Which Platform Should You Choose?

Choose OpenAI AgentKit If:

• You’re already heavily invested in the OpenAI ecosystem
• You want the fastest possible time-to-market with minimal setup
• Your use case doesn’t require data to stay on-premise
• You prefer managed infrastructure over self-hosting
• You need the latest GPT models immediately upon release
• Your team lacks DevOps resources for infrastructure management
• Budget allows for OpenAI’s premium pricing model
• You value tight integration over flexibility
• Compliance allows cloud-based AI processing
• You’re comfortable with platform limitations for ease of use

Choose Dify If:

• You need to use multiple LLM providers or specific models
• Data sovereignty and privacy are critical requirements
• You want complete control over your AI infrastructure
• Your organization requires self-hosted solutions
• Cost optimization through model flexibility is important
• You have DevOps capability for self-hosting
• You need extensive RAG and document processing capabilities
• Open-source transparency is a requirement
• You want to avoid vendor lock-in
• Your use case requires deep customization
• You’re in a regulated industry (banking, healthcare, government)
• You prefer community-driven development

🔮 Future Outlook & Roadmap

OpenAI AgentKit Roadmap

OpenAI plans to add standalone Workflows API and agent deployment options to ChatGPT. Expect rapid iteration and new features as the platform matures beyond beta stage.

Dify Development

Active open-source development with regular releases. Community-driven feature requests and transparent roadmap on GitHub. Continuous improvements to RAG, workflows, and integrations.

Market Competition

Both platforms face competition from LangChain, n8n, Zapier Central, and others. The AI agent space is rapidly evolving with new players entering regularly.

Convergence Trends

Expect features to converge over time as both platforms mature. Visual builders, multi-agent orchestration, and evaluation tools are becoming industry standards.

🎓 Final Recommendation

For most organizations: The choice depends on your priorities. If you value speed, simplicity, and are committed to OpenAI, AgentKit offers the fastest path to production agents. If you need flexibility, data control, and multi-provider support, Dify provides superior long-term value despite requiring more initial setup.

Hybrid Approach: Some organizations use AgentKit for rapid prototyping and Dify for production deployments where data sovereignty and model flexibility matter. This combines the speed of AgentKit with the control of Dify.

cd openai-agentkit-sample

npm install

cp .env.example .env

OPENAI_API_KEY=sk-your-actual-api-key-here

npm start

npm run example:basic      # Basic agent
npm run example:tools      # Agent with tools
npm run example:advanced   # Advanced agent

npm run example:sdk-basic  # SDK basic agent
npm run example:sdk-tools  # SDK with tools
npm run example:sdk-multi  # Multi-agent system

const weatherTool = {
  type: 'function',
  function: {
    name: 'get_weather',
    description: 'Get the current weather in a given location',
    parameters: {
      type: 'object',
      properties: {
        location: {
          type: 'string',
          description: 'The city and country',
        },
        unit: {
          type: 'string',
          enum: ['celsius', 'fahrenheit'],
        },
      },
      required: ['location'],
    },
  },
};
// Manual tool execution
function executeFunction(functionName, args) {
  switch (functionName) {
    case 'get_weather':
      return getWeather(args.location, args.unit);
    // ... more cases
  }
}

import { tool } from '@openai/agents';
import { z } from 'zod';
const getWeatherTool = tool({
  name: 'get_weather',
  description: 'Get the current weather for a given location',
  parameters: z.object({
    location: z.string().describe('The city and country'),
    unit: z.enum(['celsius', 'fahrenheit']).optional().default('celsius'),
  }),
  async execute({ location, unit }) {
    // Tool implementation
    return JSON.stringify({ temperature: 22, condition: 'Sunny' });
  },
});
// Automatic execution - no switch statement needed!
const agent = new Agent({
  tools: [getWeatherTool],
});

// SDK automatically generates JSON schema from Zod!
z.object({
  city: z.string(),
  unit: z.enum(['celsius', 'fahrenheit']).optional(),
})

const coordinator = new Agent({
  handoffs: [weatherAgent, mathAgent, knowledgeAgent],
});
// Coordinator can automatically route to specialists

// SDK handles the entire conversation loop
const result = await run(agent, "What's the weather in Hanoi?");
console.log(result.finalOutput);

parameters: z.object({
  email: z.string().email(),
  age: z.number().min(0).max(120),
  role: z.enum(['admin', 'user', 'guest']),
})

async execute({ city }) {
  try {
    const result = await weatherAPI.get(city);
    return JSON.stringify(result);
  } catch (error) {
    return JSON.stringify({ error: error.message });
  }
}

// Good - specific tools
const getWeatherTool = tool({...});
const getForecastTool = tool({...});
// Bad - one giant tool
const weatherAndForecastAndHistoryTool = tool({...});

const weatherExpert = new Agent({
  name: 'WeatherExpert',
  tools: [getWeatherTool],
});
const mathExpert = new Agent({
  name: 'MathExpert',
  tools: [calculateTool],
});
const coordinator = new Agent({
  handoffs: [weatherExpert, mathExpert],
});