Framework switching is expensive. It means rewriting code, retraining teams, and explaining decisions to stakeholders. Yet our recent interviews revealed that multiple development teams have made exactly this move – abandoning established tools like LangChain and Microsoft Semantic Kernel for Google’s relatively new Agent Development Kit.
What’s driving these switches? We conducted over a dozen in-depth interviews with developers and engineering teams who have hands-on experience with ADK: from solo developers building personal projects to enterprise teams selecting frameworks for client deployments. We spoke with machine learning engineers, full-stack developers, product managers balancing technical and business requirements, and consulting engineers choosing foundations for their clients.
Their experiences span industries: healthcare, finance, general consulting, and use cases ranging from rapid hackathon prototypes to enterprise production systems. We’ve distilled their experiences into eight key factors that consistently emerged across our conversations.
Read on to see what we discovered.
What Is Google Agent Development Kit (ADK)?
Google’s Agent Development Kit is an open-source framework designed to simplify building and deploying AI agents. Released in early 2024, the framework represents Google’s entry into the increasingly competitive agent development space—arriving after established players like LangChain and LangGraph, but bringing fresh perspectives informed by observing their challenges.
The framework adopts a code-first approach, enabling developers to create agents using Python classes while providing built-in tools for common tasks such as orchestration, debugging, and deployment.
The framework addresses three fundamental challenges in agent development:
- Multi-Agent Orchestration: ADK provides built-in patterns for Sequential, Parallel, and Loop agents that handle complex workflows without requiring custom coordination code. This contrasts with frameworks like LangChain, which require manual implementation of agent communication.
- Developer Experience: The framework includes a native web UI for testing and debugging agents. Developers can launch an agent interface with a simple adk run command, eliminating the need to build custom interfaces during development.
- Production Readiness: ADK powers agents within existing Google products like Agentspace and the Google Customer Engagement Suite, demonstrating its enterprise capabilities.
While optimized for Google’s ecosystem and Gemini models, ADK works with multiple LLM providers, including GPT, Claude, and Mistral. The framework also introduces the Agent-to-Agent (A2A) protocol, enabling ADK agents to communicate with external systems and other agent frameworks.
The design philosophy centers on making agent development feel more like traditional software development – providing structure and helpful tools while maintaining developer control over agent behavior and decision-making processes.
Why Are Developers Switching to Google ADK? 8 Real Reasons from Framework Migration Interviews
Understanding the technical capabilities is one thing, but real adoption happens when frameworks solve actual developer pain points. Our interviews revealed several key factors driving teams toward ADK. Let’s examine them in detail.
1. Existing Framework Pain Points
The pattern was consistent across our interviews: developers were struggling with the frameworks they had been using. It emerged across different teams, company sizes, and use cases – suggesting systemic framework challenges rather than isolated incidents.
LangChain presented multiple issues beyond abstractions. A senior developer described the core problem: “LangChain abstracts too much. With raw LLMs, you control system prompts, inputs, and outputs. With LangChain’s conversational agents, all of that is hidden.” Another developer experienced quality issues: “With LangChain, I built some stuff against Gemini models. At first, I got results and thought: ‘My God, I’ve done it!’ But when I evaluated them, they were garbage — hallucinations, stitching random data together.”
LangGraph overwhelmed teams with complexity. One developer noted: “I found it a bit too… like, it was too much overhead with LangGraph. Too much overhead, like a learning curve. Not to say it was hard to learn – just that I didn’t have time to put into learning LangGraph to develop agents in it.”
Microsoft Semantic Kernel had different challenges. One team explained: “We realized there were orchestration issues. The framework was hallucinating — it was randomly choosing numbers on its own, even though I explicitly prompted it not to assume anything.”
Microsoft Autogen presented documentation challenges. A developer comparing options found: “I found Autogen relatively complex to start with. It has many functionalities, but the documentation is messy compared to Google ADK.”
Ignition blocked teams with bugs. As one developer mentioned: “Unfortunately, there were some bugs at that time. I even reported those bugs to their team on the community forum. They might have fixed them since, but at that point, we were blocked.”
2. Built-in Debugging and Transparency
What drew developers to ADK was straightforward: it worked the way they expected. A machine learning engineer explained: “If you know how to call an LLM API and define your own tools, you can straightforwardly build an agent and UI. You can launch it by just typing ‘adk run’. The UI is rich. You know exactly what requests went to the LLM, what prompts were sent, how the system prompt worked, how the agent transferred, what tools were called, and what they returned. Debugging is very easy.“
3. Low Barrier to Entry
For teams new to agent development, the learning curve emerged as a key factor. One developer noted: “LangGraph seems promising, with more advanced options, but I came to the conclusion that if you want to start building agents, you must start with ADK. It’s beginner-friendly.”
4. Multi-Agent Orchestration Made Simple
ADK’s approach to multi-agent systems stood out as a major advantage. One developer explained: “ADK was easy to use, because making those agents was very simple. Like, almost like they had an agent for everything you needed. There was a sequential agent, there was just a normal LLM agent, I think there was a loop agent as well.”
Another emphasized the complexity this solves: “ADK supports loop agents, parallel agents, sequential agents – coordinating sub-agents and agent-to-agent interactions. This is powerful, because coding it yourself is very hard. With ADK, much is handled in the background. It saves a lot of time, especially for complex projects.”
5. Cloud Integration and Deployment Ready
The deployment story attracted developers who wanted to move quickly from prototype to production. “It is also deployment-ready: with just a few commands I can deploy into Vertex Engine or Cloud Run. These features make this framework very cool. I don’t have to write a FastAPI layer. I can get agents to endpoints and expose them to any UI of my choice or any microservice.”
6. Flexibility and Integration with Existing Tools
ADK’s compatibility approach impressed teams already invested in other frameworks. “Their approach to tools and integrations is very good. They provide integrations for existing LangChain, MCP tools, and other technologies. So we can do everything within ADK without relying on existing LangChain tools.”
ADK’s flexibility extends beyond Google’s ecosystem. “ADK doesn’t have to run on Google Cloud. In our case, we only have commercial access to Azure, so we’ll host ADK on Azure. It’s doable,” explained an engineer selecting frameworks for client projects.
7. Clear Documentation
Documentation quality emerged as a surprisingly decisive factor. One ML engineer working on financial advisory systems explained his framework selection process: “The biggest reason for me was documentation. LangGraph’s documentation is hard to follow. There was even a Reddit post where someone said they had to use an LLM just to understand it. ADK’s docs are very clear and easy to follow.”
8. Google Brand Trust in Enterprise Sales
Business considerations also played a role. An engineer from a consulting firm explained, “When we tell clients that our foundation layer is built on Google ADK, it gives credibility. Customers trust it more.”
This wasn’t just about brand recognition. Teams appreciated that ADK powers existing Google products, suggesting it’s built for real-world use rather than just experimentation.
Making the Switch: What You Need to Know
Google ADK represents a new approach to agent development that prioritizes developer experience without sacrificing control. The eight factors we’ve outlined reflect real pain points that teams face when building production AI systems.
However, these switching factors don’t mean ADK is perfect. The framework is still evolving, and our interviewees noted several challenges they encountered. If you want to learn more about these production challenges, read our article: “9 Google ADK Production Challenges and How to Solve Them”
But understanding why developers switch only tells part of the story. What happens when you actually build with ADK? Our next article follows three real implementation stories: a healthcare system built during a 5-day hackathon, a financial advisory agent for specialized markets, and a messaging bot migrating from raw API calls to a full agent framework.
Need Help with ADK Implementation?
At DLabs.AI, we’re implementing ADK in client projects and have hands-on experience navigating its capabilities and limitations. If you’re considering ADK for your next project or need help evaluating agent frameworks, contact us.
