Deep Agent - Multi Agent RAG with Gemini and Langchain

About this course

This course is not for absolute beginners in AI - you should first learn LangChain fundamentals, then LangGraph, and only after that take this Deep Agent & Multi-Agent course for the best learning experience.

This course is a complete, hands-on guide to building real-world AI agents and deep research (DeepAgent) systems using Google Gemini, LangChain v1, MCP, and modern RAG techniques.

You will start from the absolute basics of AI agents and slowly move towards building advanced autonomous multi-agent systems used for deep financial research. The course is designed in a progressive way so that beginners can follow along, while experienced developers will still learn advanced production-grade patterns.

The focus of this course is not only theory. You will build everything step by step using Python notebooks, real APIs, real documents, and real data pipelines.

What this course covers

You will first understand what an AI agent really is. You will learn different agent patterns, how agents reason, how they take actions, and how to choose the right agent design for a real project.

You will then set up Google Gemini AI Studio and LangSmith properly. This includes creating API keys, understanding pricing, rate limits, and tracing agent executions so you can debug and monitor your agents like a professional.

After that, you will go through a complete Gemini and LangChain bootcamp. You will learn how to use Gemini models in Python, how messages work internally, how streaming responses work, how multimodal inputs are handled, and how to use tools, function calling, reasoning mode, grounding, and context caching to reduce cost and improve performance.

Once the foundations are clear, you will move into LangChain agents. You will build agents with memory, state management, summarization middleware, fallback models, PII protection, planners, streaming responses, an

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