I Ditched Google ADK for LangGraph
The author shares their experience transitioning from Google ADK to LangGraph for creating AI Agents. They highlight the frustrations with Google ADK's dense documentation and telemetry data collection, which hindered their development process. In contrast, LangGraph offers more flexibility, easier state management, and a clearer programming flow, making it a preferred choice for the author.
- ▪The author built a prototype for a news aggregation website using Google ADK but faced challenges with its documentation and execution control.
- ▪LangGraph provides a more intuitive framework for creating AI Agents, allowing for easier reasoning about program flow and state changes.
- ▪The author appreciates LangGraph's flexibility and control, comparing the transition to moving from TensorFlow to PyTorch.
Opening excerpt (first ~120 words) tap to expand
I Ditched Google ADK for LangGraph 25 May, 2026 LangGraph is by far the best framework I've used for creating AI Agents. The framework provides complete control over the execution of individual Agents and the state that flows through them, which you frankly don't get from the Google Agent Development Kit (ADK). Jumping off from the 5-Day AI Agents Intensive Course with Google on Kaggle, I built the prototype for Ikka, a Zimbabwean news aggregation website using the Google ADK. The trouble with the Google framework began when I needed to implement looping for my editor Agent. The documentation for the ADK is very dense and, at the time, did not provide a clear way of mixing sequential and looping execution.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Maybe-Ray.
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