When comparing AgentKit vs ADK, OpenAI recently released AgentKit. Google’s Agent Development Kit (ADK) has been out since May 2025, giving it a significant head start. Both promise to make building AI agents easier. Both launched with impressive demos and enterprise customers already using them in production.
But here’s the thing: they’re solving fundamentally different problems.
I spent time researching both frameworks, diving into the docs, reading security analyses, and comparing real-world implementations. If you’re trying to choose between them—or wondering if Google’s ADK is even a direct competitor to OpenAI’s AgentKit—this comparison will help.
Quick Context: What Are These Frameworks?
OpenAI AgentKit
OpenAI AgentKit is a complete platform for building, deploying, and optimizing AI agents. It centers around Agent Builder, a visual canvas where you drag and drop nodes to compose agent workflows. Think “Canva for building agents,” as Sam Altman described it.
The platform includes four main components:
- Agent Builder: Visual workflow designer with drag-and-drop logic
- ChatKit: Embeddable chat interface for your apps
- Connector Registry: Central management for data and tool connections
- Evals for Agents: Performance measurement and optimization tools
Philosophy: Get agents from prototype to production fast. Abstract away complexity. Make it accessible to developers who aren’t AI specialists.
Google Agent Development Kit (ADK)
Google ADK is an open-source, code-first toolkit for building sophisticated multi-agent systems. It’s optimized for Gemini and the Google Cloud ecosystem, but it’s model-agnostic and deployment-agnostic by design.
ADK is both a framework and a development platform. It includes:
- Command-line interface (
adk) - Local web UI for debugging
- Built-in evaluation tools
- Rich pre-built tool ecosystem
- Native Google Cloud service integrations
Philosophy: Make agent development feel like software development. Give developers fine-grained control. Support complex, production-scale architectures from day one.
Framework Comparison Table
| Dimension | OpenAI AgentKit | Google ADK |
|---|---|---|
| Core Approach | Visual-first, low-code | Code-first, developer-centric |
| Model Support | OpenAI models (primarily) | Model-agnostic (any LLM) |
| Multi-Agent | Supported via visual builder | Native, hierarchical architecture |
| Deployment | OpenAI infrastructure | Deploy anywhere (Cloud Run, Vertex AI, Docker) |
| Open Source | Closed platform | Fully open source (Python & Java) |
| State Management | Managed by OpenAI | Your choice (Firestore, Spanner, etc.) |
| Security Model | Built-in guardrails (PII, jailbreak) | Callback-based, extensible |
| Learning Curve | Low (visual builder) | Moderate (code-first) |
| Pricing | API usage pricing | Free (framework) + cloud costs |
| Best For | Rapid prototyping, startups | Enterprise, complex systems |
Developer Experience: How They Feel to Use
OpenAI AgentKit: Visual Simplicity
AgentKit is designed for speed. According to OpenAI’s launch announcement, their visual canvas can “slash iteration cycles by 70% and get an agent live in two sprints rather than two quarters.”
Companies like Ramp reported going “from a blank canvas to a buyer agent in just a few hours.” That’s the promise: fast time-to-value.
The visual builder abstracts away prompt engineering, function calling, and context management. You focus on what the agent should do, not how to wire everything together. For small teams or solo developers building MVPs, this is compelling.
The trade-off: You’re locked into OpenAI’s ecosystem. Your agent runs on OpenAI infrastructure, uses OpenAI’s state management, and lives within their platform. If you need custom orchestration logic or want to switch models, you’ll hit limits.
Google ADK: Code-First Control
ADK feels like building with a modern software framework—because that’s exactly what it is. Installation is straightforward:
pip install google-adk(other languages are support as well, with different levels of parity to the Python-first AI world)
From there, you define agents as code. You compose multi-agent systems using familiar programming patterns. You have full control over orchestration, state management, and deployment.
The documentation is comprehensive, with guides for Python and Java. The local debugging UI is excellent. The framework is designed for developers who want to own their agent architecture.
The trade-off: Higher initial learning curve. You’re writing more code. You need to make architectural decisions that AgentKit abstracts away. For rapid prototyping or non-technical builders, this is slower.
My Take
If you’re a developer who’s worked with modern frameworks (React, Django, Spring), ADK will feel natural. You get the control and flexibility you’re used to.
If you’re a product manager, founder, or developer who just wants to ship something fast, AgentKit’s visual builder is genuinely impressive.
But here’s the nuance: they’re optimized for different stages of maturity. AgentKit excels at getting to v1 quickly. ADK excels at scaling from v1 to production-grade systems.
Architecture: Where Your Agent Lives
OpenAI AgentKit: Managed Infrastructure
AgentKit runs on OpenAI’s infrastructure. This is both a feature and a constraint.
The upside: Zero DevOps overhead. No containerization. No deployment pipelines. Your agent is live as soon as you save it in Agent Builder. OpenAI handles scaling, availability, and monitoring.
The architecture: AgentKit uses the Responses API, which combines chat completions with structured function calling. Conversation history and context live on OpenAI’s servers. Function calls execute synchronously—the agent waits for each step to complete before moving forward.
This synchronous model is simple and predictable. But it means long-running tasks block. You can’t easily implement async workflows or parallel agent coordination without workarounds.
State management: Managed by OpenAI. You don’t control where or how state is stored.
Google ADK: Deploy Anywhere
ADK is deployment-agnostic. You can run agents:
- Locally (for development)
- On Cloud Run (serverless containers)
- On Vertex AI Agent Engine (managed scaling)
- In Docker (any cloud or on-prem)
The framework supports both sync and async execution patterns. You can build agents that coordinate work across microservices. State management is yours to configure—Firestore, Spanner, Bigtable, or any external system.
ADK’s multi-agent architecture is native. You compose agents into hierarchies. Different agents can run in different environments. The framework handles message passing and coordination.
The trade-off: You’re responsible for deployment, scaling, and operations. ADK gives you the tools, but you need to use them.
When Architecture Matters
For prototypes and MVPs: AgentKit’s managed infrastructure is a huge win. You’re not a startup if you’re spending weeks setting up Kubernetes.
For production systems at scale: ADK’s flexibility becomes critical. You need control over where agents run, how they scale, and how they fail. You need observability into every component.
For our Chip AI assistant (which uses Google’s ADK), we needed fine-grained control over tool execution and state management. AgentKit’s managed approach wouldn’t have worked.
Security: Guardrails vs. Extensibility
Security is where these frameworks diverge most sharply.
OpenAI AgentKit: Built-In Guardrails
AgentKit includes pre-built guardrails that “mask or flag PII, detect jailbreaks, and apply policy constraints alongside agent execution,” according to OpenAI.
The system uses soft guardrails (LLM-based detection for jailbreaks and hallucinations) and hard guardrails (deterministic rules for PII and content moderation).
For PII protection, you can configure it to scan for:
- Names, emails, IP addresses
- Credit card numbers
- Country-specific identifiers (SSNs, NHS numbers, passport numbers)
This sounds great. But security researchers have identified significant limitations:
“While these guardrails seem promising and give the impression of a complete safety net, they are not nearly as strong as they appear. In practice, each one has blind spots and failure modes that can be exploited or simply missed.”
The core issue: soft guardrails depend on LLM reasoning. If an attacker can manipulate the input to confuse the safety model, they can bypass protections. This is the classic prompt injection problem, applied to security.
Hard guardrails are more reliable but limited. PII detection works for known patterns (email addresses, SSNs) but misses business-specific identifiers, internal codes, or non-standard secrets.
The bottom line: AgentKit’s guardrails are a good starting point, but you shouldn’t rely on them exclusively. You still need application-level security controls.
Google ADK: Callback-Based Extensibility
ADK takes a different approach: security as code.
The framework provides callbacks that let you intercept and modify agent behavior at key execution points:
def before_tool_callback(context, tool_info, parameters):
# Validate tool execution against policies
# Check parameters for sensitive data
# Apply custom business logic
return modified_parametersADK also includes:
- Google Cloud IAM integration: Fine-grained access control
- Sandboxed code execution: Server-side code interpreter with no network access
- OAuth support: User and agent authentication
- Audit trails: Log all agent actions for compliance
The ADK docs emphasize that security should be distributed across the execution pipeline. You implement multiple layers: input validation, tool authorization, output filtering, and audit logging.
The trade-off: You’re responsible for implementing security. The framework gives you the hooks, but you write the code.
My Take on Security
If you’re building internal tools or prototypes, AgentKit’s built-in guardrails are convenient. They catch obvious issues without extra work.
If you’re building production systems handling sensitive data, ADK’s callback-based approach is essential. You need to implement security policies specific to your domain. Generic PII detection isn’t enough.
Both frameworks acknowledge a hard truth: agent security is still an unsolved problem. Soft guardrails are bypassable. Hard rules are incomplete. The best defense is layered security plus constant monitoring.
For more on agent security considerations, I’ve written about the importance of sandboxing, least-privilege access, and audit logging. ADK makes these patterns easier to implement.
Model Flexibility: Locked In or Model-Agnostic?
OpenAI AgentKit: Optimized for OpenAI Models
AgentKit is built on OpenAI’s models—GPT-4o, GPT-4o-mini, and future releases. The platform is tightly integrated with OpenAI’s APIs.
The documentation doesn’t explicitly list support for external models. The visual builder, evals system, and guardrails are designed around OpenAI’s function calling and response format.
This is strategic: OpenAI wants AgentKit to drive API usage. The easier they make agent development, the more you consume their models. It’s the AWS playbook—make the experience seamless, lock in the infrastructure.
Cost implications: You pay OpenAI API prices for every agent interaction. At scale, this adds up. You can’t swap in cheaper models or run models on your own infrastructure.
Vendor lock-in: If OpenAI changes pricing, deprecates a model, or has an outage, you’re stuck. Your agents are coupled to their platform.
Google ADK: Model-Agnostic by Design
ADK is explicitly model-agnostic. While it’s optimized for Gemini, it works with any LLM through:
- Vertex AI Model Garden: Access to open-source models
- LiteLLM integration: Connect to OpenAI, Anthropic, Cohere, etc.
- Custom model endpoints: Bring your own model infrastructure
You can mix models within the same agent system. Use GPT-4 for reasoning, Gemini for vision tasks, and a local model for privacy-sensitive operations.
Cost control: You’re not locked into one vendor’s pricing. You can optimize cost per task based on model capabilities.
Future-proofing: As new models emerge, you can integrate them without rewriting your agent architecture.
The trade-off: You manage model configuration and API keys yourself. The abstraction is leaner—you’re closer to the model APIs.
When Model Flexibility Matters
For early-stage products: Model lock-in isn’t a dealbreaker. Pick the best model for your use case and ship. Worry about costs when you have revenue.
For enterprise deployments: Model flexibility is critical. You need to optimize costs, meet data residency requirements, and avoid single-vendor dependency.
For hybrid architectures (like using Gemini for some tasks and Claude for others), ADK is the only real option.
Use Cases: When to Choose Which Framework
Choose OpenAI AgentKit If:
You’re prototyping or building an MVP Time-to-market matters more than control. You need to validate product-market fit before optimizing infrastructure.
Your team isn’t deeply technical Product managers, designers, or founders can build with Agent Builder. You don’t need a senior engineer to own the agent logic.
You’re building on OpenAI models anyway If GPT-4o is already your model of choice, the platform integration is seamless.
You want zero DevOps overhead No containers, no deployment pipelines, no scaling decisions. Just build and ship.
Examples: Customer support bots, internal tools, simple automation agents, chatbot MVPs.
Choose Google ADK If:
You’re building production-scale systems You need observability, custom orchestration, and fine-grained control over agent behavior.
You’re in the Google Cloud ecosystem Native integrations with BigQuery, Vertex AI Search, Gmail, Drive, and other Google services are game-changing.
You need model flexibility You want to optimize costs, experiment with models, or avoid vendor lock-in.
You have engineering resources Your team can write code, deploy containers, and manage infrastructure.
You’re building complex multi-agent architectures Hierarchical agent systems, parallel workflows, and distributed coordination are first-class patterns in ADK.
Examples: Enterprise knowledge assistants, data analysis platforms, complex automation pipelines, multi-agent systems with specialized roles.
When to Choose Neither
Sometimes the answer is don’t use a framework at all.
If you’re building something simple—like a chatbot that calls two APIs—you don’t need AgentKit or ADK. Use OpenAI’s SDK directly or call the Gemini API. Frameworks add overhead.
If you need maximum control over every prompt and interaction, frameworks abstract too much. Spec-driven development with raw model APIs might be a better fit.
If you’re exploring agent orchestration patterns at a research level, start with lower-level tools (LangChain, AutoGen) before committing to a platform.
Are They Even Competitors?
Here’s where my initial question gets interesting: Is Google ADK a direct competitor to OpenAI AgentKit?
Sort of, but not really.
They target different buyer personas:
| Persona | Likely Choice | Why |
|---|---|---|
| Solo founder | AgentKit | Speed, simplicity, no ops |
| Startup engineering team (5-10 people) | AgentKit | Fast iteration, validate before scaling |
| Mid-size company (50-200 people) | Either | Depends on existing cloud setup |
| Enterprise with Google Cloud | ADK | Integration, security, control |
| Enterprise with multi-cloud | ADK | Model flexibility, avoid lock-in |
| AI research team | ADK | Experiment with models, custom architectures |
They’re competing for different stages of adoption:
- AgentKit: Wins at the prototype and early-product stage
- ADK: Wins at the scaling and enterprise stage
You might start with AgentKit to validate your idea. When you hit scale or need custom logic, you migrate to ADK (or build something custom). They’re not mutually exclusive over a product’s lifecycle.
What I Found Surprising
After diving into both frameworks, a few things stood out:
1. Both Are Remarkably Mature for 2025 Launches
Companies like Ramp (using AgentKit) and Google’s internal teams (using ADK) are running these in production. That’s rare for frameworks that just launched.
The documentation, tooling, and onboarding experiences are polished. These aren’t experimental releases.
2. Security Is Still the Hard Part
Both frameworks acknowledge that agent security is unsolved. AgentKit’s guardrails have known bypasses. ADK gives you the tools but not the solutions.
If you’re handling sensitive data or high-stakes decisions, you still need to think deeply about agent failure modes.
3. Model-Agnostic Matters More Than I Expected
I underestimated how valuable model flexibility would be. As I researched, I kept finding use cases where you’d want different models for different tasks—GPT-4 for reasoning, Gemini for structured data, Claude for long-context tasks.
ADK’s model-agnostic design isn’t just about avoiding lock-in. It’s about composing best-of-breed models within a single agent system.
4. The Visual Builder Is Genuinely Good
I’m usually skeptical of low-code tools. But AgentKit’s visual builder is well-designed. The drag-and-drop interface isn’t dumbed-down—it’s an abstraction that works.
For the right use case (rapid prototyping, non-technical teams), it’s a legitimate advantage.
5. Open Source vs. Closed Platform Will Define Adoption
ADK being fully open source (Python and Java on GitHub) changes the game. Developers can fork it, extend it, and build on it without dependency on Google’s goodwill.
AgentKit being a closed platform means you’re betting on OpenAI’s long-term support and pricing stability. That’s a business risk.
Lessons from Building with Agents
Here’s what I’ve learned from working with AI agents (including building Chip AI with Google’s ADK):
Start Simple, Scale Later
Don’t over-engineer. Both frameworks make it tempting to build complex multi-agent systems on day one. Resist.
Start with a single-agent MVP. Validate the use case. Then add complexity.
Security Should Be Paranoid
Agents are surfaces for attack. Assume every input is hostile. Log everything. Sandbox aggressively.
Neither framework gives you security by default. You have to build it.
Context Management Is Harder Than You Think
Keeping agents grounded in relevant context—without overwhelming the context window—is an unsolved UX problem.
Both frameworks handle context at a low level (message history, tool outputs). But designing what context matters is on you.
Observability Is Non-Negotiable
When agents fail (and they will), you need to know why. Did the model hallucinate? Did a tool call fail? Was the prompt ambiguous?
ADK’s local debugging UI and callback hooks make this easier. AgentKit’s managed platform abstracts this away—which is great until something breaks.
Iteration Speed Compounds
The faster you can test changes, the faster you find what works. AgentKit’s visual builder excels here. ADK requires more code changes but gives you more control.
Choose based on what limits your iteration speed: Is it infrastructure complexity (choose AgentKit) or framework abstractions (choose ADK)?
Frequently Asked Questions
Which framework is better for production?
It depends on scale and control needs.
For small-to-medium production systems (< 10k users), AgentKit’s managed infrastructure is excellent. You avoid operational overhead.
For large-scale or enterprise systems, ADK’s flexibility and observability become essential. You need to own the deployment pipeline.
Can I switch between AgentKit and ADK later?
Not easily.
AgentKit agents are tightly coupled to OpenAI’s platform. Migrating to ADK would require rewriting agent logic as code.
ADK agents are more portable (especially if you use model-agnostic patterns), but migrating to AgentKit would still require rebuilding workflows in the visual builder.
Design for the framework you’ll need at scale, not the one that’s easiest to start with. Migrations are costly.
Do I need to use OpenAI models with AgentKit?
Effectively, yes.
While the documentation doesn’t explicitly forbid other models, AgentKit is designed around OpenAI’s APIs. The visual builder, evals, and guardrails assume OpenAI response formats.
If you want model flexibility, ADK is the better choice.
What’s the pricing model for each?
OpenAI AgentKit: Pricing is based on API usage (tokens consumed). The Agent Builder tool itself is included at no additional cost. You pay standard OpenAI API rates for model inference.
Google ADK: The framework itself is free (open source). You pay for:
- Compute (if deploying on Cloud Run or Vertex AI)
- Model API calls (Gemini or third-party models)
- Storage (Firestore, Spanner, etc., if used)
ADK gives you more cost control because you can optimize deployment and model choices.
Is AgentKit or ADK better for beginners?
AgentKit is better for beginners due to its visual builder and lower learning curve. You can build and deploy an agent without writing code.
ADK requires programming knowledge (Python or Java) and understanding of deployment concepts. It’s better suited for developers with backend experience.
If you’re non-technical or just learning AI development, start with AgentKit. If you’re a software engineer, ADK gives you more control.
How long does it take to build an agent with each framework?
AgentKit: Hours to a few days for basic agents. Ramp built a buyer agent in “just a few hours” using the visual builder.
ADK: Days to weeks, depending on complexity. Initial setup and learning the framework takes longer, but you gain more flexibility.
For prototypes and MVPs, AgentKit is faster. For production-scale systems, the upfront time investment in ADK pays off.
Final Take: Which Framework Actually Works?
Both of them. But they work for different things.
Choose AgentKit if:
- You need to ship fast
- You’re validating a new product
- Your team isn’t deeply technical
- You’re okay with vendor lock-in for convenience
Choose ADK if:
- You’re building for production scale
- You need model flexibility
- You’re in the Google Cloud ecosystem
- Your team can own deployment and ops
- You want open-source extensibility
For me, after researching both, I’d reach for ADK for anything production-scale and AgentKit for rapid prototyping. That’s not a criticism of AgentKit—it’s just acknowledging they’re optimized for different stages.
The real competition isn’t AgentKit vs. ADK. It’s frameworks vs. raw APIs. Both AgentKit and ADK abstract complexity that some developers will want to own directly. That’s okay. Use the right tool for the job.
If you’re building with AI agents, think through your agent design patterns before picking a framework. The architecture you need should drive your tooling choice, not the other way around.
And if you’re navigating the hype around AI agents, remember: the framework matters less than whether you’re solving a real problem.
Building with AI agents? I write about agent patterns, production lessons, and developer tools. Follow along or reach out on LinkedIn.