Make vs OpenAI AgentKit: Which AI Agent Platform Is Right for You?
Compare Make's 2,500+ integrations with OpenAI AgentKit's 21 pre-built UI widgets. Learn key differences in architecture, use cases, and when each platform fits your enterprise needs.
Key Takeaways
Different categories: Make is workflow automation with AI add-on; OpenAI is a purpose-built agent toolkit
Make wins on integrations: 2,500+ apps vs OpenAI's focus on chat UI and model integration
OpenAI wins on UI components: 21 pre-built widgets vs Make's lack of agent-specific UI tools
Both lack enterprise features: Neither offers source attribution, multi-agent coordination patterns, or self-updating knowledge bases
Use case matters most: Backend automation favors Make; simple chat deployments favor OpenAI; sophisticated multi-agent systems require specialized frameworks
Choosing between workflow automation and visual agent builders can make or break your AI implementation strategy—especially when evaluating Make's extensive integrations against OpenAI Agent Kit's cutting-edge chat components.
Make is a mature workflow automation platform (similar to Zapier) with 2,500+ app integrations that recently added AI agent capabilities in open beta.
OpenAI Agent Kit is a newly launched (October 2025) toolkit combining visual workflow builder, embeddable chat UI components, and evaluation tools specifically designed for building conversational AI agents.
In this comprehensive guide, you'll learn how these platforms differ in architecture, use cases, ease of deployment, and when each makes sense for your team—plus discover why many enterprises need capabilities beyond what either platform offers.
What Is Make?
Make (formerly Integromat) is a visual workflow automation platform that competes with Zapier and n8n in the iPaaS (Integration Platform as a Service) category. Founded in 2012, Make has built a mature ecosystem of 2,500+ app integrations and 30,000+ actions for connecting business systems without code.
In July 2025, Make launched AI Agents in open beta—adding the ability to create autonomous agents that can interact with workflows, execute tasks, and leverage Make's extensive integration library. These agents use LLMs (like GPT-4, Claude) combined with system prompts, context, and tools to automate tasks within Make's workflow environment.
Key strengths:
- Extensive integration ecosystem (2,500+ apps including Salesforce, Slack, Google Workspace, databases)
- Mature visual workflow builder (Make Grid) with drag-and-drop scenario design
- Strong MCP (Model Context Protocol) support—both server (expose Make scenarios to Claude/ChatGPT) and client (consume external MCP tools)
- Real-time data fetching capabilities across thousands of APIs and services
- On-premise agent option for legacy system access
What Is OpenAI Agent Kit?
OpenAI Agent Kit is a comprehensive toolkit launched in October 2025 for building and deploying customer-facing AI agents. It combines three core components: Agent Builder (visual workflow canvas), ChatKit (embeddable UI components), and evaluation tools (trace grading and quality assurance).
Unlike workflow automation platforms, Agent Kit is purpose-built for conversational AI experiences. It emphasizes rapid deployment through hosted infrastructure, template-based workflows, and exceptional UI components that eliminate most chat interface development work.
Key strengths:
- Best-in-class UI component library (21 interactive widgets: forms, cards, buttons, date pickers, lists)
- Visual workflow builder with templates (customer support, homework helper, etc.)
- Native MCP integration for third-party tool connectivity
- Comprehensive evaluation system (multiple grader types, regression detection, trace logging)
- Fully managed backend infrastructure (no DevOps required)
- Strong realtime speech-to-speech capabilities for voice agents
Key Differences Between Make and OpenAI Agent Kit
1. Core Purpose & Architecture
Make: Designed as a workflow automation platform where AI agents serve as components within broader business process automation. Agents are isolated (single-agent architecture) and execute tasks within workflows that connect multiple apps and services. The architecture prioritizes sequential execution and app-to-app integration rather than conversational AI patterns.
OpenAI Agent Kit: Built specifically for conversational AI with visual workflow composition optimized for agent interactions. Workflows follow linear patterns with branching logic (if/else, while loops, human approval gates) designed to orchestrate chat-based interactions rather than cross-system data flows.
Takeaway: Make is workflow automation that added AI; OpenAI is conversational AI with workflow capabilities. Fundamentally different architectural purposes.
2. Multi-Agent Capabilities
Make: Single-agent architecture only. Each AI agent operates independently with its own LLM, system prompt, context, and tools. Make scenarios can call sub-scenarios (workflow nesting), but this is sequential execution rather than agent coordination. No handoff patterns, delegation patterns, or agent-to-agent communication.
OpenAI Agent Kit: Limited multi-agent coordination—handoff pattern for voice agents (permanent transfer of conversation to another agent) but no delegation pattern (task-and-return with context preservation). Workflows are linear with branching rather than graph-based, preventing sophisticated multi-agent orchestration where agents dynamically coordinate.
Takeaway: Both platforms have weak multi-agent capabilities; Make has none, OpenAI has basic handoff only.
3. Integration & Ecosystem
Make: Dominates with 2,500+ app integrations and 30,000+ actions covering CRM, databases, communication tools, project management, e-commerce, and more. Dual MCP implementation allows Make scenarios to be called by external AI systems (MCP Server) and Make workflows to consume external AI tools (MCP Client). Custom Apps SDK enables building proprietary integrations.
OpenAI Agent Kit: Focuses on MCP for tool connectivity rather than proprietary integrations. File Search for document retrieval, MCP servers for third-party tools, and API calls for custom integrations. Integration breadth is significantly narrower, prioritizing depth of chat experience over breadth of system connectivity.
Takeaway: Make offers 10x more pre-built integrations for connecting business systems; OpenAI relies on MCP ecosystem and custom development.
4. User Interface (UI) & Components
Make: No agent-specific UI components or embeddable chat widgets. Make Grid provides workflow visualization, but there are no JavaScript/React components for building agent chat experiences. Agents are configured entirely through Make's UI—no code-based agent development or bidirectional sync.
OpenAI Agent Kit: Industry-leading ChatKit UI library with 21 pre-built interactive widgets, native React and JavaScript SDKs, visual Widget Builder for no-code component design, and streaming responses with real-time UI updates. Components are production-ready and eliminate 80% of typical chat interface development work.
Takeaway: OpenAI's UI components are vastly superior—Make offers no agent-specific UI tooling whatsoever.
5. Developer Experience
Make: Custom Apps SDK for building integration modules (not for agent development). UI-only agent configuration—no TypeScript/Python SDK for programmatic agent definition. No bidirectional sync between visual builder and code. Developers can build workflow integrations but cannot define agents programmatically.
OpenAI Agent Kit: Visual builder exports code but cannot re-import code changes (one-way sync only). TypeScript and Python SDKs exist for ChatKit UI components but not for agent orchestration. Developers can build custom UI but cannot programmatically define workflows and sync them back to visual builder.
Takeaway: Both lack true bidirectional code-UI sync—Make has no agent SDK, OpenAI has one-way export only.
Quick Comparison Table
| Feature / Dimension | Make | OpenAI Agent Kit | Winner |
|---|---|---|---|
| Primary Use Case | Workflow automation + AI add-on | Conversational AI agent builder | — |
| App Integrations | ✅ 2,500+ apps, 30,000+ actions | ❌ MCP-based only, limited pre-builts | Make |
| UI Components | ❌ None (no agent chat widgets) | ✅ 21 widgets, best-in-class ChatKit | OpenAI |
| Multi-Agent Coordination | ❌ Single agents only | ⚠️ Handoff only (no delegation) | Tie (both lack) |
| Visual Builder | ✅ Mature workflow builder | ✅ Agent Builder with templates | Make |
| Developer SDK | ⚠️ Integrations only, no agent SDK | ⚠️ UI SDK only, one-way code export | OpenAI |
| Bidirectional Code-UI Sync | ❌ UI-only configuration | ❌ Export only, cannot re-import | Tie (both lack) |
| MCP Support | ✅ Dual (server + client) | ✅ Client integration | Make |
| Knowledge Management | ⚠️ Manual RAG setup | ⚠️ Manual vector store management | Tie |
| Source Attribution | ❌ No citations | ❌ No citations | Tie (both lack) |
| Agent Credentials | ⚠️ Scenario-level only | ⚠️ Workflow-level only | Tie (both lack) |
| Managed Infrastructure | ⚠️ Cloud-hosted workflows | ✅ Fully managed backend | Tie |
| Evaluation Tools | ❌ Basic workflow logs | ✅ Comprehensive trace grading | OpenAI |
| Voice Agents | ❌ Not supported | ✅ Realtime speech-to-speech | OpenAI |
Legend: ✅ Strong Support | ⚠️ Partial/Limited | ❌ Not Available
Which One Should You Choose?
Choose Make if you:
- Need workflow automation first, AI second: Your primary requirement is connecting business systems (CRM, databases, communication tools) with AI as an enhancement to workflows
- Require extensive integrations: You need to connect 100+ apps across your tech stack and leverage pre-built connectors
- Have operations team ownership: Non-technical operations teams will manage automation workflows, not AI/ML engineering teams
- Want MCP flexibility: You need both MCP server capabilities (expose workflows to Claude/ChatGPT) and MCP client (consume external tools)
- Need on-premise access: Legacy systems on local networks require on-premise agent deployment
- Are already a Make user: You're invested in Make's workflow platform and want to add AI capabilities
Example scenario: A marketing operations team automates lead routing across Salesforce, Slack, and HubSpot. They add AI agents to qualify leads, draft personalized emails, and trigger workflows based on sentiment analysis—all within Make's workflow environment.
Choose OpenAI Agent Kit if you:
- Need simple chat deployment fast: Your goal is an embeddable chatbot on docs site or product, deployed in under 2 weeks
- Prioritize UI quality: Exceptional chat experience with interactive widgets matters more than backend complexity
- Have minimal engineering resources: Team has fewer than 5 engineers and cannot manage infrastructure
- Are OpenAI model-committed: You're already invested in GPT models and want tight integration with OpenAI ecosystem
- Build voice-first experiences: Realtime speech-to-speech capabilities are critical for your use case
- Value brand recognition: OpenAI brand carries weight with stakeholders, even for just-launched products
- Accept hosted infrastructure: You're comfortable with fully managed backend and don't need self-hosting
Example scenario: A SaaS company with a small engineering team wants to deploy a documentation chatbot quickly. They use Agent Builder templates, customize ChatKit widgets, and launch an embedded chat experience without backend infrastructure work.
Choose Neither (consider Inkeep or alternatives) if you:
- Sophisticated multi-agent coordination: Graph-based orchestration with handoff AND delegation patterns where agents dynamically coordinate
- Source attribution for compliance: Automatic citations and artifact tracking to meet regulatory requirements
- Self-updating knowledge bases: Automated ingestion from docs, Notion, Confluence without manual management
- Omnichannel deployment: Native Slack bots, Discord integrations, Zendesk copilots beyond API integrations or chat embed
- Agent-level governance: Per-agent credentials, permissions, and security controls with least-privilege access
- Bidirectional development: Full code-to-visual and visual-to-code sync for technical and non-technical collaboration
For these requirements, specialized multi-agent AI frameworks like Inkeep are necessary (see section below).
Why Inkeep Beats Both Platforms
While Make and OpenAI Agent Kit serve important but narrow use cases, Inkeep is a purpose-built enterprise multi-agent AI framework designed for sophisticated conversational AI requiring advanced orchestration, governance, and knowledge automation.
Inkeep's Decisive Advantages
| Capability | Make | OpenAI Agent Kit | Inkeep | Enterprise Impact |
|---|---|---|---|---|
| Graph-Based Multi-Agent Architecture | ❌ Single agents only | ❌ Linear workflows | ✅ True directed graphs | Enables complex team dynamics and sophisticated orchestration unavailable in either competitor |
| Handoff + Delegation Patterns | ❌ None | ⚠️ Handoff only (voice) | ✅ Both patterns | Agents can permanently transfer (handoff) OR task-and-return with context (delegation)—critical for supervisor patterns |
| Source Attribution System | ❌ No citations | ❌ No citations | ✅ Automatic artifact tracking | Creates clear chain of evidence with clickable citations—required for compliance in regulated industries |
| Self-Updating Knowledge Base | ❌ Manual workflows | ❌ Manual vector stores | ✅ Auto-ingestion from docs/Notion/Confluence | Eliminates weekly operational burden of manually updating agent knowledge |
| Agent-Level Credential Management | ❌ Scenario-level only | ❌ Workflow-level only | ✅ Per-agent isolation | Implements least-privilege security with role-based agent access controls |
| Bidirectional Code-UI Sync | ❌ UI-only config | ❌ One-way export | ✅ Full 2-way sync | Developers define agents in TypeScript; visual builder auto-generates graphs for stakeholders (and vice versa) |
| Omnichannel Native Deployment | ⚠️ API integrations | ❌ Chat embed only | ✅ Slack, Discord, Zendesk, Salesforce bots | Deploy same agent across team chat, CRM, support platforms without custom integration work |
| AI Knowledge Intelligence | ❌ None | ⚠️ Trace logging only | ✅ Gap analysis + feature tracking | Automatically identifies missing documentation and surfaces feature requests from conversations |
| Proven Enterprise Scale | ⚠️ Beta AI (July 2025) | ❌ Just launched (Oct 2025) | ✅ Anthropic, Clay, Midjourney | Years of production deployments in demanding environments vs unproven beta/launch products |
| Confidence-Gated Automation | ❌ Manual checks only | ⚠️ Basic guardrails | ✅ Confidence scoring + escalation | Only answers when confident; automatically escalates to humans when uncertain—protects brand reputation |
| Advanced RAG | ❌ Basic file uploads | ⚠️ Basic File Search | ✅ Hybrid search with re-ranking | Optimized retrieval combining semantic search, keyword matching, and relevance re-ranking |
When Inkeep Is The Right Choice
Enterprises choose Inkeep when:
- Multiple agents must coordinate dynamically (e.g., triage agent delegates to specialist agents, then synthesizes responses)
- Compliance teams require source attribution with audit trails for every AI response
- Knowledge bases span multiple systems (docs, Notion, Confluence, internal wikis) and must stay current automatically
- Agents deploy across Slack, Zendesk, web chat, and mobile with unified orchestration
- Security requires granular per-agent permissions (not workflow-level credentials)
- Both technical teams (TypeScript SDK) and business users (visual builder) need to configure agents
- Proven production scale matters more than brand recognition of just-launched products
Bottom line: Make excels at workflow automation. OpenAI offers great chat UI.
Inkeep is the only enterprise framework with graph-based multi-agent orchestration, source attribution, knowledge automation, and omnichannel deployment—capabilities neither competitor can match.
Conclusion
Make and OpenAI Agent Kit serve fundamentally different purposes in the AI agent landscape:
- Make excels at workflow automation with extensive integrations (2,500+ apps), making it ideal for operations teams connecting business systems with AI enhancements
- OpenAI dominates chat UI with exceptional components (21 widgets) and rapid deployment, perfect for mid-market teams needing simple chatbot experiences quickly
Key decision factors:
- Choose Make if your primary need is backend workflow automation across multiple apps
- Choose OpenAI if you need chat UI deployed fast with minimal engineering resources
- Choose Inkeep for multi-agents if you need sophisticated multi-agent coordination, enterprise governance, source attribution, or knowledge automation
Critical gaps in both platforms: Neither Make nor OpenAI provides graph-based multi-agent orchestration, source attribution systems, self-updating knowledge bases, agent-level credentials, or bidirectional code-UI sync. For enterprise deployments requiring these capabilities, purpose-built multi-agent frameworks are necessary.
Final recommendation: Evaluate your primary use case first—workflow automation, simple chat deployment, or sophisticated multi-agent systems. The answer determines which category of platform fits your needs. Don't choose workflow automation tools for agent orchestration, or agent builders for workflow automation.
Next Steps
- Try Inkeep Demo — See graph-based orchestration and source attribution in action
- Read Inkeep vs OpenAI Comparison — Deeper dive on architectural differences
- Read Inkeep vs Make Comparison — Integration strategies and complementary use cases
- Explore Inkeep Documentation — Technical architecture and multi-agent patterns
Frequently Asked Questions
Technically yes—you could use Make for backend workflow automation and OpenAI for frontend chat UI. However, you'd still lack multi-agent coordination, source attribution, self-updating knowledge bases, and agent-level governance. Most teams find managing two separate systems creates more operational burden than value.
OpenAI Agent Kit is faster to deploy for simple chat experiences (template-based, hosted infrastructure). Make requires more setup but offers broader integration capabilities if workflow automation is the primary need. For startups needing sophisticated multi-agent systems, specialized frameworks like Inkeep provide both ease of use and advanced capabilities.
Not necessarily. Integration breadth matters for workflow automation across business systems, but enterprise AI agent deployments require governance features neither Make nor OpenAI provides: source attribution for compliance, agent-level permissions, confidence scoring, and audit trails. Integration quantity doesn't replace governance quality.
Limited—Agent Kit is optimized for OpenAI's models with tight vertical integration. While theoretically possible to use other models, you lose many benefits and create maintenance complexity. This model lock-in is a strategic risk if pricing changes or regulatory requirements mandate specific model hosting.
You can't with either platform's native capabilities. Make supports single agents only. OpenAI supports handoff (permanent transfer) but not delegation (task-and-return with context preservation). True multi-agent orchestration with graph-based relationships requires purpose-built frameworks.
Neither platform offers enterprise-grade security for customer-facing AI. Both lack agent-level credential management (only workflow/scenario-level), source attribution for compliance, confidence-gated automation, and automatic escalation mechanisms. Enterprise deployments typically require specialized frameworks with comprehensive governance features.
Migration difficulty varies. Make workflows are proprietary to Make's platform. OpenAI Agent Kit creates model lock-in with OpenAI's ecosystem. Platforms with standards-based architectures (MCP, OpenAI-compatible APIs) and bidirectional code-UI sync reduce migration risk by enabling programmatic agent definition that's portable across systems.
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