LangGraph in Action: Develop Advanced AI Agents with LLMs
This product hasn't been reviewed yet! Be the first to share your experience.
Top alternatives
Reviews, Scores & Student Outcomes
Unclaimed ProfileLangGraph in Action: Develop Advanced AI Agents with LLMs is an industry-focused guide tailored for software engineers, AI developers, and tech enthusiasts looking to transcend basic prompt engineering. Led by expert instructor Markus Lang, this intermediate-level course dives deep into LangGraph (Version 1.0.0), a powerful orchestration framework designed to build stateful, multi-agent architectures. Throughout this curriculum, learners will move beyond simple linear LLM chains to construct robust, cyclical computational graphs that can handle complex reasoning tasks. By exploring essential primitives such as nodes, edges, state management, and checkpointers, students acquire the theoretical foundation and practical skills needed to deploy resilient AI agents. Additionally, the course emphasizes production-grade engineering principles, guiding you through integrating short-term and long-term memory systems, creating hierarchical multi-agent subgraphs, and packaging your intelligent agents using FastAPI and Docker containers. Whether you are aiming to build enterprise-level customer support chatbots or autonomous research assistants, this comprehensive training provides the hands-on labs and architectural patterns required to transform raw LLM capabilities into highly reliable, production-ready cognitive applications.
About the creator
Markus Lang
View full profileFollow on social
Not found
—
Reviews
Markus Lang is an experienced software engineer and AI architect specializing in building complex agentic workflows and LLM-powered applications. With a strong background in software development, Markus bridges the gap between raw machine learning models and reliable, deterministic production systems. He has spent years designing software architectures that integrate Large Language Models (LLMs) to solve real-world problems. In his course, 'LangGraph in Action: Develop Advanced AI Agents with LLMs,' Markus channels this practical experience to teach developers how to transcend basic retrieval-augmented generation (RAG) pipelines and build sophisticated, stateful, multi-agent architectures. His structured teaching philosophy emphasizes hands-on coding, robust state…Show more
Ratings & reviews
This product hasn't been reviewed yet! Be the first to share your experience.
Not enough reviews yet
0 reviews
Rating breakdown
Results mentioned in reviews are individual experiences and are not guaranteed or typical.
Program Overview
Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Master LangGraph's core primitives, including state schemas, computational nodes, conditional edges, and state-saving checkpointers for robust agent coordination.
- Implement persistent memory solutions within agent architectures, enabling agents to retain context across both short-term interactions and long-term sessions.
- Architect hierarchical multi-agent systems using subgraphs to divide complex tasks into specialized, manageable agent operations.
- Design and manage custom human-in-the-loop validation patterns to pause agent loops, request user input, and resume safely.
- Wrap LangGraph workflows into high-performance REST APIs using FastAPI, establishing robust asynchronous communication channels.
- Deploy and test your production-ready AI agents efficiently by utilizing Docker containerization and writing automated unit tests.
Best For
- Software engineers and AI developers proficient in Python seeking to build production-grade agentic workflows.
- Professionals who have mastered basic LangChain and want to transition to complex, stateful multi-agent architectures.
- Engineers building autonomous systems that require human-in-the-loop oversight and long-term memory persistence.
- Architects aiming to learn how to deploy and containerize LLM-based applications using FastAPI and Docker.
Not For
- Absolute beginners to programming who lack experience with Python or foundational LLM concepts.
- Developers looking for a basic introduction to prompt engineering or how to use a simple LLM API.
- Students seeking a theoretical course on LLM research or natural language processing mathematics.
- Those who require a no-code or low-code platform solution rather than custom architectural development.
What's included
Still confused?
Get a reply from the creator within 24 hours.
Top alternatives
Compare Similar AI Agents Programs
Not sure LangGraph in Action: Develop Advanced AI Agents with LLMs is the right fit? See how it stacks up against the top-rated alternatives in ai agents, all rated by verified students.
More categories
Before you buy
Compare alternatives
See how LangGraph in Action: Develop Advanced AI Agents with LLMs stacks up against top-rated programs in ai agents.
Compare nowRun a credibility check
Use AllPros Guru Detector to review trust signals before you spend.
Open Guru Detector