AI Agents in LangGraph

LangChain, a popular open source framework for building LLM applications, recently introduced LangGraph. This extension allows developers to create highly controllable agents. 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. Additionally, you will learn about agentic search, which returns multiple answers in an agent-friendly format, enhancing the agent’s built-in knowledge. This course will show you how to use agentic search in your applications to provide better data for agents to enhance their output. In detail: 1. Build an agent from scratch, and understand the division of tasks between the LLM and the code around the LLM. 2. Implement the agent you built using LangGraph. 3. Learn how agentic search retrieves multiple answers in a predictable format, unlike traditional search engines that return links. 4. Implement persistence in agents, enabling state management across multiple threads, conversation switching, and the ability to reload previous states. 5. Incorporate human-in-the-loop into agent systems. 6. Develop an agent for essay writing, replicating the workflow of a researcher working on this task. Start building more controllable agents using LangGraph!

• AI Agents in LangGraph
• LangChain, a popular open source framework for building LLM applications, recently introduced LangGraph. This extension allows developers to create highly controllable agents. 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. Additionally, you will learn about agentic search, which returns multiple answers in an agent-friendly format, enhancing the agent’s built-in knowledge. This course will show you how to use agentic search in your applications to provide better data for agents to enhance their output. In detail: 1. Build an agent from scratch, and understand the division of tasks between the LLM and the code around the LLM. 2. Implement the agent you built using LangGraph. 3. Learn how agentic search retrieves multiple answers in a predictable format, unlike traditional search engines that return links. 4. Implement persistence in agents, enabling state management across multiple threads, conversation switching, and the ability to reload previous states. 5. Incorporate human-in-the-loop into agent systems. 6. Develop an agent for essay writing, replicating the workflow of a researcher working on this task. Start building more controllable agents using LangGraph!