Udemy – Production AI Agents with LangChain + LangGraph [2026] 2026-5

Updated August 10, 2026 8 GB

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Descriptions

Production AI Agents with LangChain + LangGraph [2026], This course teaches you to build production-ready AI agent systems using LangChain v0.3, LangGraph 1.0, and FastAPI. You will move beyond simple demos to ship AI agents that handle real workloads, covering RAG pipelines, multi-agent orchestration, security, testing, LangSmith observability, and Docker deployment. The curriculum is project-focused, guiding you to build a RAG-powered Customer Support Agent to reduce support tickets, a Multi-Agent Research System to cut research time, and a production-grade API with a full request pipeline including security, caching, and metrics.

You will master LangChain Expression Language (LCEL), complete RAG pipelines with advanced retrieval patterns, and dive deep into LangGraph for stateful agents with conditional routing and human-in-the-loop workflows. The course emphasizes production-readiness by covering security against prompt injection and PII leakage, a full suite of testing methodologies from unit to semantic evaluation, and deployment practices using Docker and Render. By the end, you will have a portfolio of real-world, deployed AI projects and the skills to engineer robust, scalable AI solutions.

What you’ll learn

Build composable LLM chains using LangChain v.1’s LCEL with structured output, streaming, batch processing, and multi-provider switching
Implement production RAG pipelines with intelligent chunking, vector stores, and 4 advanced retrieval patterns: Multi-Query, Contextual Compression, Hybrid Search
Design stateful AI agents with LangGraph state machines, conditional routing, self-correcting loops, and human-in-the-loop approval workflows
Orchestrate multi-agent systems using supervisor patterns, agent handoffs, parallel execution, and hierarchical team structures
Secure LLM applications against prompt injection, PII leakage, and output manipulation with production-grade defense layers
Test and evaluate LLM systems using unit tests, integration tests, and semantic evaluation across correctness, relevance, and coherence
Deploy production APIs with FastAPI, rate limiting, response caching, structured logging, metrics, LangSmith tracing, and Docker
Build 3 real-world applications: Customer Support Agent, Multi-Agent Research System, and Code Review Agent, each with measurable business ROI

Who this course is for

Python developers who want to add AI agent skills to their toolkit and build real applications, not just follow along with tutorials
Backend or full-stack developers who want to integrate AI agents into existing products and APIs
Engineers stuck after basics — you’ve done LangChain tutorials and can call an LLM, but don’t know how to build a system that handles errors, scales, and doesn’t break in production
Career switchers targeting the AI engineer role — companies are paying 25% premiums for these skills in 2026

Specificatoin of Production AI Agents with LangChain + LangGraph [2026]

Publisher : Udemy
Teacher : Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor
Language : English
Level : Intermediate
Number of Course : 155
Duration : 17 hours and 6 minutes

Content of Production AI Agents with LangChain + LangGraph [2026]

Production AI Agents with LangChain + LangGraph [2026]

Requirements

Python intermediate level — comfortable with functions, classes, decorators, and type hints
Basic command line familiarity — creating directories, running scripts, installing packages
A code editor such as VS Code (free) and an OpenAI API key (costs $2–5 for the entire course)
No prior LangChain or LangGraph experience needed — Section 1 covers all foundations from scratch