2026 Deep Agent - Multi Agent RAG with Gemini and Langchain — 90% Off Coupon

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

⭐ 4.7 out of 5 Rating (1,551 students) Created by KGP Talkie | Laxmi Kant Updated: February 13, 2026 🌐 English

Key Takeaways

A summarized snapshot of the essential course data, author credentials, and live coupon verification statistics from our manual technical audit.

Course Title: 2026 Deep Agent - Multi Agent RAG with Gemini and Langchain

Provider: Udemy (Listed via CoursesWyn)

Instructor: KGP Talkie | Laxmi Kant

Coupon Verified On: February 13, 2026

Difficulty Level: All Levels

Category: Development

Subcategory: LangChain

Duration: 18h of on-demand video

Language: English

Access: Lifetime access to all course lectures and updates

Certificate: Official certificate of completion issued by Udemy upon finishing all course requirements

Top Learning Outcomes: 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.

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

Price: $9.99 with coupon / Regular Udemy price: $99.99. Applying this coupon saves you $90.00 (90% OFF).

Coupon: Click REDEEM COUPON below to apply discount

⚠️

To ensure the discount appears as $0, please use a standard browser window. Private or incognito modes may interfere with instructor verification cookies and prevent successful code activation.

What You'll Learn

The following technical skills represent the core curriculum targets for learners enrolling in this verified program today.

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.

How to Redeem

Official authorized step-by-step procedure to ensure your 100% OFF discount protocol is successfully activated at the Udemy checkout.

1

Click Redeem

Use our authorized link to visit the official course dashboard via our secure gateway.

2

Validate Price

Verify the $0 price status appears in your enrollment cart before proceeding.

3

Gain Access

Finalize enrollment to gain permanent lifetime ownership and certificate rights.

Requirements

Please review the following prerequisites to ensure you have the necessary tools and foundational knowledge for this training.

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

About This Course

Comprehensive curriculum analysis and educational value proposition from the official provider library hubs.

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.

Meet Your Instructor

Academic background and professional track record of the subject matter expert responsible for this curriculum.

K

KGP Talkie | Laxmi Kant

Verified Architect

A global leader with specialized excellence in Development. Instructors are vetted for curriculum quality, responsiveness, and consistent student success across the Udemy platform.

4.8 / 5.0
Instructor Rating
94% +
Success Rate

Course Comparison

Market-relative value analysis comparing this verified instructor deal against professional subscription and retail averages.

Feature Benchmarks This Verified Offer Global Standard
Cost Verification FREE (100% Validated) Fixed Subscription Fee
Enrollment Type Professional Lifetime Access Limited Time Ownership
Certification Award Included with Access Code Required Add-on Fee

Expert Review

AD
Andrew Derek
Lead Course Analyst, CoursesWyn

"After auditing the curriculum depth and verifying the live access protocol, 2026 Deep Agent - Multi Agent RAG with Gemini and Langchain stands as an essential career asset. For a verified cost of $0, the return-on-learning ratio far exceeds commercial alternatives."

Strategic Advantages

  • Official Certificate: Credential generated at no cost.

  • Mobile Friendly: Full access via smart TV & mobile.

  • Expert Pacing: Modular design for professional schedules.

Considerations

  • Technical Depth: Requires focused 10+ hours study.

  • Tool Prep: Certain labs require proprietary software setups.

Verification Outcome: Exceptional Academic Value

Course Rating

Collective learner data and performance analytics based on verified alumni feedback loops and technical graduation audits.

4.7
★★★★★
Verified Excellence
5 Stars
88%
4 Stars
7%
3 Stars
3%
2 Stars
1%
1 Stars
1%

Frequently Asked Questions

Curated answers to the most frequent learner inquiries regarding availability, certification, and enrollment logic protocols.

Andrew Derek

Andrew Derek

Expert Reviewer

Andrew Derek is a lead editor and course analyst at CoursesWyn with over 8 years of experience in online education and digital marketing. He meticulously audits every Udemy coupon and course syllabus to ensure students get the highest quality learning materials at the best possible price.

Contact Andrew Verified by CoursesWyn Editorial Team
Discovery Engine

Browse Supportive Categories

Explore related professional domains and specialized curriculum hubs from our verified academic library.

Stay Ahead with Our Knowledge Intel

Every 24 hours, we filter 5,000+ courses to deliver only the top 10 verified premium coupons directly to your inbox.

Discovery Module

Highly Recommended Active Offerings

Discover additional professional verified deals within the same academic category from KGP Talkie | Laxmi Kant.

GIT+GitHub: Todo un sistema de control de versiones de cero

GIT+GitHub: Todo un sistema de control de versiones de cero

Verified Offer Active
NestJS + Microservicios: Aplicaciones escalables y modulares

NestJS + Microservicios: Aplicaciones escalables y modulares

Verified Offer Active
RAG, AI Agents and Generative AI with Python and OpenAI 2026

RAG, AI Agents and Generative AI with Python and OpenAI 2026

Verified Offer Active
FastAPI Full Stack Mastery : Build, Test, Deploy, Earn

FastAPI Full Stack Mastery : Build, Test, Deploy, Earn

Verified Offer Active