KGP Talkie | Laxmi Kant

Deep Agent - Multi Agent RAG with Gemini and Langchain

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The Deep Agent course by KGP Talkie provides a comprehensive deep dive into the evolving ecosystem of AI agents, moving beyond simple chatbots to architecting complex, autonomous multi-agent systems. This curriculum is meticulously designed for developers who want to harness the power of LangChain v1, Google Gemini 3, and the Model Context Protocol (MCP) to solve real-world automation challenges. By integrating advanced tools like Qdrant for vector retrieval and Docling for sophisticated multimodal document processing, the course addresses the core difficulty of handling unstructured data such as dense financial reports, complex tables, and high-resolution images. Students gain practical experience in building production-ready architectures that leverage hybrid search, memory management, and cost-optimized context caching. What sets this course apart is its emphasis on multi-agent collaboration, teaching learners how to orchestrate specialized roles like research and editor agents to automate multi-step workflows. Whether you are looking to build autonomous research bots or enterprise-grade data extraction pipelines, this program bridges the gap between theoretical RAG concepts and the reality of deploying scalable, multi-modal AI agents in modern cloud environments using Docker.

Agentic AI Projects: FastAPI, MCP, AWS Deploy & Gemini 3

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Agentic AI Projects: FastAPI, MCP, AWS Deploy & Gemini 3 offers a comprehensive deep dive into the rapidly evolving landscape of autonomous AI systems. This course moves beyond basic chatbot development, focusing instead on the sophisticated architecture required to build functional, reasoning-based AI agents. Participants will master the integration of Google’s latest Gemini 3 models with LangChain and LangGraph to construct systems capable of performing multi-step tasks, tool orchestration, and complex reasoning. A standout feature of this curriculum is its emphasis on modern integration standards like the Model Context Protocol (MCP), which bridges the gap between AI models and real-world data sources. Beyond the AI logic, the course provides a crucial bridge to production engineering. By utilizing FastAPI to wrap agentic workflows and deploying these applications onto AWS EC2 instances, learners gain the technical proficiency needed to move projects from a local development environment into scalable, enterprise-grade cloud deployments. The inclusion of safety guardrails, human-in-the-loop approval processes, and sandboxed execution environments ensures that students learn how to manage the inherent risks of autonomous agents, making this an essential program for developers aiming to build secure, reliable, and intelligent AI applications that truly deliver business value.

Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents

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Mastering local AI orchestration is becoming a critical skill for modern developers, and this comprehensive course by Laxmi Kant (KGP Talkie) provides the exact blueprint needed to succeed. Focused on LangChain v1 and Ollama, the training guides you through running powerful open-source LLMs—like DeepSeek R1, LLaMA, and Gemma—entirely on your local machine. Students appreciate the deep dive into LangChain Expression Language (LCEL), which simplifies creating complex runnable pipelines, prompt templates, and structured JSON output parsers. The course shines in its practical, hands-on approach, teaching you how to build highly functional conversational chatbots with persistent memory, optimized Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents capable of tool calling. Beyond the basics, you will work on highly relevant real-world projects, including a sophisticated Text-to-SQL agent for MySQL databases, automated LinkedIn scrapers, and resume parsing pipelines. Finally, the curriculum bridges the gap between local development and production by walking through AWS EC2 deployment strategies. Whether you are an aspiring AI engineer or an experienced developer looking to transition to local LLM infrastructure, this course offers the practical skills needed to build secure, cost-effective, and robust AI applications.

MCP Mastery: Build AI Apps with Claude, LangChain and Ollama

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This comprehensive masterclass on Model Context Protocol (MCP) bridges the gap between raw LLMs and dynamic, real-world utility. Spearheaded by Laxmi Kant from KGP Talkie, the course offers an exhaustive, hands-on deep dive into constructing high-performance AI agents and custom servers from the ground up. Developers will master the art of extending Claude's capabilities, enabling local execution with Ollama, and structuring production-grade orchestration using LangChain v1 and LangGraph v1. Throughout the curriculum, you will build state-of-the-art MCP clients and servers, integrating them seamlessly with Python-based Streamlit interfaces and local vector databases like ChromaDB. Whether you are aiming to construct autonomous retrieval-augmented generation (RAG) pipelines or deploy resilient agentic workflows that securely consume external APIs, this course delivers the precise architectural patterns you need. It is perfectly tailored for software engineers, AI developers, and technical architects who want to break free from closed ecosystems and build highly adaptable, secure, and production-ready local AI applications using open-source tools and cutting-edge protocol design.

Agentic AI - Private Agentic RAG with LangGraph and Ollama

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This course provides a comprehensive, hands-on deep dive into building fully private, local Agentic Retrieval-Augmented Generation (RAG) pipelines. Led by KGP Talkie (Laxmi Kant), learners explore how to combine the power of LangGraph's stateful orchestration with Ollama's local LLM execution. By keeping everything local, developers and organizations can process sensitive data, such as financial reports, without relying on third-party APIs. Throughout the curriculum, students master advanced techniques like Corrective RAG (CRAG), Self-RAG, and Adaptive RAG, ensuring high-accuracy outputs through iterative reasoning loops, query rewriting, and self-correction. The course details practical document ingestion using modern tools like Docling for PDF processing, ChromaDB for high-performance vector storage, and BM25 re-ranking for superior retrieval precision. Whether you are an AI engineer, a software developer, or an enterprise architect looking to deploy secure, private AI agents, this training offers the ultimate roadmap. By mastering conditional routing, multi-step agentic workflows, and local embeddings, you will be equipped to build production-grade, sovereign AI systems that respect data privacy while delivering state-of-the-art cognitive capabilities.