Master Advanced Agentic Ai + Langgraph + Rag+memory -Jan'26

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Master Advanced Agentic Ai + Langgraph + Rag+memory -Jan'26
Published 1/2026
Created by Vignesh S
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 82 Lectures ( 8h 45m ) | Size: 8.4 GB [/center]
2026-ONLY COURSE on Production-Ready AI Agents with LANGCHAIN + LANGGRAPH + RAG + MULTI-AGENT + MEMORY for QA Automation
What you'll learn
Master Langchain and LangGraph frameworks for building production-grade AI agents
Implement RAG (Retrieval-Augmented Generation) with vector databases for intelligent knowledge retrieval
Build Multi-Agent Systems where specialized agents collaborate to solve complex QA tasks
Add Memory and Context to agents for remembering past interactions and learning patterns
Integrate agents with TestRail, Jira, and Slack for real-world QA workflow automation
Requirements
Understanding of basic agent concepts like prompts, LLM calls, and JSON parsing
Description
[THE ENTIRE COURSE HAS BEEN CREATED IN 2026 JANUARY WITH THE LATEST LANGCHAIN AND LANGGRAPH FRAMEWORKS]Ready to transform your basic agents into production-ready, intelligent systems used by top tech companies?Welcome to the ONLY course on Udemy that takes your existing AI agents from the beginner level to enterprise-grade systems using Langchain, LangGraph, RAG, Memory, and Multi-Agent orchestration.The demand for these skills is exploding. Senior QA roles paying $120K - $150K + require Langchain/LangGraph experience. This course gives you that.Why These Skills Matter in 2026:Companies hiring AI/ML Engineers for QA teams expect:✓ Langchain/LangGraph experience (now industry standard)✓ RAG implementation for proprietary knowledge✓ Multi-agent orchestration patterns✓ Production deployment knowledge✓ Cost optimization and scalabilityThis course covers ALL of that.By the End of This Course:✓ Build production-grade agents with Langchain and LangGraph✓ Implement RAG for company-specific knowledge retrieval✓ Create multi-agent systems for complex workflows✓ Add memory so agents learn from past interactions✓ Deploy agents that integrate with real QA tools✓ Optimize costs and performance at scale✓ Confidently discuss advanced agentic AI in interviews✓ Have portfolio projects that demonstrate enterprise skillsWhat Makes This Course Different?We don't start from scratch. We take the TestCase Generator and Log Analyzer agents you already built and progressively upgrade them with powerful capabilities. Every code change is tracked on GitHub with tags - you can see exactly how your agents evolve from basic to advanced.You'll Build On Your Existing Code:In the beginner course, you built agents in the src/agents/ folder using vanilla Python. In this course:- Section 4: Migrate to Langchain (src/agents_v2/)- Section 5-8: Rebuild with LangGraph graphs (src/graph/)- Section 9-14: Add RAG, Memory, and Multi-Agent capabilitiesThis progressive approach prevents confusion and lets you compare vanilla Python vs frameworks side-by-side.What You'll Master in This Course:1. Langchain Framework (Foundation)Learn the industry-standard framework for LLM applications. Understand chains, prompts, output parsers, and when to use Langchain vs vanilla Python. Migrate your existing agents to Langchain in under 20 lines of code.2. LangGraph (Main Framework for Complex Agents)Master stateful, graph-based agent workflows. Build agents with conditional routing, error recovery, retry logic, and human-in-the-loop approval. LangGraph is what production systems use for reliability.3. RAG & Vector DatabasesStop generating generic outputs. Teach your agents company-specific knowledge using ChromaDB. Implement semantic search to retrieve relevant test cases, logs, and documentation. Your agents will reference YOUR data, not generic internet knowledge.4. Memory & Context ManagementBuild agents that remember past conversations and learn from previous interactions. Implement short-term memory (conversation history) and long-term memory (persistent vector storage). Your agents will get smarter over time.5. Multi-Agent SystemsOne agent is good. Multiple specialized agents working together is unstoppable. Learn the Supervisor Pattern where a coordinator agent orchestrates specialist agents (Log Analyzer → Root Cause Investigator → Solution Recommender). Real production systems work this way.6. Human-in-the-Loop WorkflowsProduction agents need human oversight. Implement approval nodes where agents pause for human review before taking critical actions. Build feedback loops for iterative refinement.Real-World Integration:- TestRail API: Push generated test cases directly to test management- Jira API: Auto-create bugs from log analysis- Slack: Send agent reports to team channels- Webhook Triggers: Start agents from external eventsWhat You Get:- 7+ hours of advanced hands-on tutorials- Complete source code on GitHub with tags for every topic- Side-by-side comparison: Vanilla → Langchain → LangGraph- Production-ready patterns and best practices- Real integration examples (TestRail, Jira, Slack)- Support for the latest Langchain and LangGraph- Lifetime access and free updatesWhy Wait? Your Agents Are Ready for an Upgrade.Enroll now and build the advanced AI systems that companies actually deploy in production.See you inside!
Who this course is for
QA Engineers who completed the beginner course and want production-ready agent skills
Senior Test Automation Engineers looking to integrate AI into enterprise testing workflows
Python Developers building LLM-powered applications for QA and testing domains
Tech Leads evaluating Langchain and LangGraph for team adoption and scalability
AI Engineers specializing in agentic systems for software quality assurance

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