N8N & ADK Hybrid: N8N, Wrapper, ADK Bundle in Cloud | Cloud SQL & Long Term Agent Memory Part 3

Описание: 🚀 We're taking our AI agent architecture to the next level in Part 3 of the N8N & Google ADK Hybrid series! In this deep-dive session, we bridge the final gaps in our system, connecting every component from the custom Streamlit frontend to the cloud-hosted ADK Agent Bundle. Witness the power of a fully integrated, intelligent agent that can reason over a massive 15,000-word context file with incredible speed and 100% accuracy.

This isn't just theory—it's a practical, end-to-end implementation that challenges conventional thinking about complex retrieval systems. We validate our "Stark RAG" (full context injection) architecture in real-time and discover that for many use cases, providing the full ground truth to a powerful model like Gemini is simpler, faster, and more accurate than traditional RAG/CAG pipelines.

Join me as we build, debug, and deploy a sophisticated system where AI agents are orchestrated by n8n, managed via a FastAPI wrapper, and draw their intelligence from a centralized, cloud-native knowledge base.

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🔹 Key Topics Covered In This Video:

*Full-Circle Integration:* Watch how we connect the Streamlit UI to our FastAPI wrapper, which routes requests through an n8n workflow to the correct ADK agent, completing the full operational loop.
*Cloud-Native Context & Prompts:* We ditch local files for good! Learn how to store and dynamically fetch agent instructions and massive context files (our entire 31-product catalog) directly from Google Cloud Storage.
*Dynamic Agent Tooling:* I'll show you how to write specific, reliable Python utility functions and wrap them in the ADK's `FunctionTool`, giving your agents new capabilities on demand.
*"Stark RAG" Validated:* See the incredible performance of our full-context injection strategy. We test the `product_agent` on its 15k-word knowledge base and get instant, accurate results, proving the viability of this simplified, high-fidelity approach.
*Live Debugging & Problem-Solving:* We hit a critical `400 INVALID_ARGUMENT` error from the Gemini API and walk through the real-world process of diagnosing and fixing a subtle conflict between the ADK's `code_executor` and `tools` list.
*Dynamic UI:* We refactor the Streamlit frontend to dynamically fetch the list of available agents from our wrapper, creating a scalable and maintainable Mission Control interface.

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🕒 Timestamps:

00:00 - Mission Briefing & Recap of Part 1
00:00 - Creating the Massive `PRODUCTS.md` Context File
00:00 - Uploading Agent Context & Instructions to Google Cloud Storage
00:00 - Refactoring Python Utilities for Dynamic GCS Fetching
00:00 - Building a `FunctionTool` for the `product_agent`
00:00 - Hitting the "Tool Use Unsupported" Error: The Debugging Process
00:00 - The Real Fix: Diagnosing the `code_executor` vs. `tools` Conflict
00:00 - Refactoring the Streamlit UI for a Dynamic Agent Dropdown
00:00 - Live End-to-End Test: Asking the Agent Product Questions
00:00 - Final Analysis: Why "Stark RAG" is a Game-Changer
00:00 - Debrief & Next Steps: Supabase Authentication

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🔗 Resources & Links:

GitHub Repo for this Project: [LINK TO YOUR GITHUB REPO]
Watch Part 1 of this series: [LINK TO PART 1 VIDEO]
Google Agent Development Kit (ADK): https://github.com/GoogleCloudPlatfor...
n8n - Workflow Automation: https://n8n.io/
FastAPI: https://fastapi.tiangolo.com/
Streamlit: https://streamlit.io/

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If you're building with AI agents, Python, or modern automation tools, this is the series for you. Like and subscribe for the next chapter where we'll tackle persistent user memory with a full Supabase login/logout implementation!

#n8n #GoogleADK #AIAgent #Python #FastAPI #LangChain #HybridArchitecture #GoogleCloud #Streamlit #LLM #Gemini #SoftwareEngineering #AI #Automation