Google ADK vs LangGraph: 3 Months of Debugging Hell? (2025)

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Google and LangChain both launched AI agent frameworks promising seamless development. One framework costs developers three months of debugging nightmares. The other creates vendor lock-in you can't escape. I spent two weeks investigating which framework actually deserves your production deployment.

In this investigation, I'll reveal:

❌ Why Google ADK developers report token limit problems and documentation gaps that slow production
⚠️ The debugging nightmares and breaking API changes plaguing LangGraph deployments
💸 How simple tasks in LangGraph require hundreds of configuration lines versus Google ADK's abstractions
🚩 The walled garden integration trap Google ADK creates with Google Cloud commitment
🔍 Why LangGraph's surgical control creates operational overhead and full-time configuration management
💬 The critical revelation about developers switching frameworks and hidden default failures in regulated workflows

Both Google ADK and LangGraph promise to solve AI agent orchestration, but the architectural differences create drastically different production experiences. One framework hides complexity behind Google infrastructure. The other forces you to understand distributed systems directly.

Have you tried building with Google ADK or LangGraph? Drop your framework horror stories in the comments.

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Disclosures & Disclaimer

🧠 Opinions: This video reflects my own opinions and research. It is for educational and informational purposes only. Do your own research before buying anything.

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