What Will You Learn?
Learn about LangGraph’s components and how they enable the development, debugging, and maintenance of AI agents. Integrate agentic search capabilities to enhance agent knowledge and performance. Learn directly from LangChain founder Harrison Chase and Tavily founder Rotem Weiss.About This Course
Provider: Coursera
Format: Online
Duration: 1 hour to complete [Approx.]
Target Audience: Intermediate level
Learning Objectives: In this course, you will learn to build an agent from scratch using Python and an LLM, and then you will rebuild it using LangGraph, learning about its components and how to combine them to build flow-based applications.
Assessment and Certification: NA
Instructor: Harrison Chase and Rotem Weiss.
Course Prerequisites: Intermediate Python knowledge.
Key Topics: Learn about Prompt Engineering, Data Persistence, Human Centered Design, Context Management, LangGraph, Agentic systems, LLM Application, AI Workflows, and LangChain.
Topic Covered:
- Build an agent from scratch, and understand the division of tasks between the LLM and the code around the LLM.
- Implement the agent you built using LangGraph.
- Learn how agentic search retrieves multiple answers in a predictable format, unlike traditional search engines that return links.
- Implement persistence in agents, enabling state management across multiple threads, conversation switching, and the ability to reload previous states.
- Incorporate human-in-the-loop into agent systems.
- Develop an agent for essay writing, replicating the workflow of a researcher working on this task.
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