Deep Agent - Multi Agent RAG with Gemini and Langchain — 93% OFF Coupon

Langchain v1 AI Agents, Multi-Modal Deep Agents, Multi Agent Deep Advanced RAG, Google Gemini 3, Qdrant, Docker, Docling

★4.8 out of 5
2,589 students
English
Updated May 2026

Quick Facts — Deep Agent - Multi Agent RAG with Gemini and Langchain Overview

Here's a quick overview of everything you need to know about Deep Agent - Multi Agent RAG with Gemini and Langchain before you enroll:

•Course Name: Deep Agent - Multi Agent RAG with Gemini and Langchain
•Platform: Udemy
•Instructor: KGP Talkie | Laxmi Kant
•Coupon Last Verified: May 12, 2026
•Level: Advanced
•Topic: Development
•Subtopic: LangChain
•Total Time: 20h of video content
•Language: English
•Access Type: Unlimited lifetime access + updates
•Certificate: Included upon completion from Udemy
•Main Skills: Build production-ready AI agents using Google Gemini, LangChain v1, MCP, and modern agent design patterns. · Design and implement multimodal RAG pipelines using Docling, Gemini, Qdrant vector database, and hybrid search. · Process PDFs, tables, and images at scale using Docling, Docker, and structured data extraction techniques.
•Requirements: Basic Python knowledge is required. Familiarity with APIs, Docker, or RAG concepts is helpful but not mandatory.
•Current Price: $9.99 (was $149.99). You save $140.00 with 93% discount.
•How to Apply: Click the coupon button to activate your discount automatically
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Skills You'll Master in This Course

By the end of Deep Agent - Multi Agent RAG with Gemini and Langchain, you'll have these practical skills:

✓Build production-ready AI agents using Google Gemini, LangChain v1, MCP, and modern agent design patterns. .
✓Design and implement multimodal RAG pipelines using Docling, Gemini, Qdrant vector database, and hybrid search. .
✓Process PDFs, tables, and images at scale using Docling, Docker, and structured data extraction techniques. .
✓Implement hybrid search, re-ranking, memory, MCP tools, and cost-optimized context caching in real AI systems .
✓Create autonomous multi-agent research systems with orchestrator, researcher, and editor agents for finance use cases.

Prerequisites for This Course

Before enrolling in Deep Agent - Multi Agent RAG with Gemini and Langchain, make sure you have:

•Basic Python knowledge is required. Familiarity with APIs, Docker, or RAG concepts is helpful but not mandatory.

About This Udemy Course

The following is the full official course description for Deep Agent - Multi Agent RAG with Gemini and Langchain as published on Udemy by instructor KGP Talkie | Laxmi Kant:

This course is a complete, hands-on guide to building real-world AI agents and deep research 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, and structured outputs using Pydantic.
The course then introduces MCP through a finance use case. You will connect external MCP servers like Yahoo Finance, load them as LangChain tools, and build a complete stock research agent with structured prompts and planners.

Deep RAG and Multimodal Finance Systems
  • A large part of this course focuses on Deep RAG systems for finance.
  • You will learn why multimodal RAG is hard, what problems occur with PDFs, tables, images, and long documents, and how to design a reliable deep RAG pipeline.
  • You will extract data from financial PDFs using Docling. This includes converting PDFs to markdown, extracting tables with context, tracking page numbers, extracting images, and validating data integrity at scale.
  • You will then generate accurate image descriptions using multimodal Gemini models and store those descriptions in markdown so everything can be handled in a single text-based pipeline.
  • Next, you will ingest large amounts of multimodal data into Qdrant vector database. You will learn dense search, sparse search, hybrid search, metadata filtering, de-duplication using file hashes, and best practices for chunking and retrieval models.
  • On top of that, you will build advanced retrieval pipelines using hybrid search and cross-encoder re-ranking for better answer quality.
Building Real Multi-Agent Deep Research Systems
In the final sections, you will build full multi-agent deep research systems from scratch.
You will design autonomous agents that work like an expert research team with orchestrator, researcher, and editor agents. These agents will plan tasks, run deep research, synthesize results, and produce structured outputs.

You will learn how agent states are shared, how tools are injected at runtime, how files are managed by agents, and how prompts are designed differently for orchestrator, researcher, and editor roles.

You will also explore LangChain’s built-in deep agent architecture and build a complete deep finance research agent using sub-agents and a file backend.

Who this course is for
  • This course is for developers who want to go beyond basic chatbots and build serious AI systems.
It is ideal for:
  • AI engineers working with LLMs
  • Backend developers building RAG systems
  • Data scientists working with documents and research
  • Finance and analytics professionals interested in AI automation
  • Anyone who wants to understand how real multi-agent systems are built in production
Basic Python knowledge is recommended, but some prior agent or RAG experience is recommended.
By the end of this course, you will be able to design, build, and debug advanced AI agents, multimodal RAG pipelines, and autonomous multi-agent research systems using Gemini and LangChain.
You will not just understand concepts. You will have built complete, end-to-end systems that you can reuse in real projects, startups, or enterprise environments.

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Is the Deep Agent - Multi Agent RAG with Gemini and Langchain Coupon Worth It?

Expert review by Andrew Derek, Lead Course Analyst at CoursesWyn.•Last updated: May 12, 2026.

Based on analysis of the curriculum structure, student engagement metrics, and verified rating data, Deep Agent - Multi Agent RAG with Gemini and Langchain is a high-value resource for learners seeking to build skills inDevelopment. Taught by KGP Talkie | Laxmi Kant on Udemy, the 20h course provides a structured progression from foundational concepts to advanced techniques— making it suitable for learners at all levels. The current coupon reduces the price by 93%, from $149.99 to $9.99, removing the primary financial barrier to enrollment.

✓What We Like (Pros)

  • Verified 93% price reduction makes this course accessible to learners on any budget.
  • Aggregate student rating of 4.8 out of 5 indicates high learner satisfaction.
  • Strong enrollment base with over 2,589 students demonstrates course popularity and trust.
  • Includes an official Udemy completion certificate and lifetime access to all future content updates.

!Keep in Mind (Cons)

The following limitations should be considered before enrolling in Deep Agent - Multi Agent RAG with Gemini and Langchain:

  • The depth of Development coverage may be challenging for absolute beginners without the listed prerequisites.
  • Lifetime access is contingent on the continued operation of the Udemy platform.
  • Hands-on projects and quizzes require additional time investment beyond video watch time.
Final Verdict: Worth It
This course offers exceptional value with current pricing
✓

Course Rating Summary

Deep Agent - Multi Agent RAG with Gemini and Langchain has earned an aggregate rating of 4.8 out of 5 from 2,589 verified student reviews on Udemy. Below is the detailed rating distribution showing learner satisfaction across all star levels.

4.8
★★★★★
2,589 Verified Ratings
5 stars
75%
4 stars
15%
3 stars
6%
2 stars
2%
1 star
2%

* Rating distribution is approximated from the aggregate score. Sourced from Udemy.

About the Instructor — KGP Talkie | Laxmi Kant

Deep Agent - Multi Agent RAG with Gemini and Langchain is taught by KGP Talkie | Laxmi Kant, a Udemy instructor specializing in Development. For the full instructor biography, professional credentials, and a complete list of their courses, visit the official instructor profile on Udemy.

•Instructor Name: KGP Talkie | Laxmi Kant
•Subject Area: Development
•Teaching Approach: Practical, project-based instruction focused on real-world application of Development skills.

Frequently Asked Questions

The following questions and answers cover the most common queries about Deep Agent - Multi Agent RAG with Gemini and Langchain, its coupon code, pricing, and enrollment process.

About the Author

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Andrew Derek

Lead Course Analyst at CoursesWyn with 8+ years of experience evaluating online learning platforms. I've analyzed 500+ Udemy courses and helped thousands of learners choose the right courses for their career goals.

⭐4.8/5 Rating
✓Trusted by 10K+ Students

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