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Harrison Chase and Rotem Weiss

LangChain, a popular open source framework for building LLM applications, recently introduced LangGraph. This extension allows developers to create highly controllable agents.

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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!

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What's inside

Syllabus

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!

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores the popular LangChain framework and its new extension, LangGraph, for building controllable agents using Python and an LLM
Teaches agentic search, which provides multiple answers in a format suitable for agents, enhancing their built-in knowledge
Instructs learners on implementing persistence in agents, enabling state management and conversation switching
Provides hands-on experience in building an essay-writing agent, replicating the workflow of a researcher
Taught by instructors Harrison Chase and Rotem Weiss, who are recognized for their work in agent development
Highly relevant to practitioners in AI and natural language processing who want to build more controllable agents using LangGraph
Covers foundational concepts of agent development using LangGraph
Introduces human-in-the-loop concepts for agent systems design
Requires students to come in with some foundational knowledge of Python and LLMs
May be more suitable for intermediate learners who have a basic understanding of agent development

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI Agents in LangGraph with these activities:
Review Python coding skills
Review Python's syntax and semantics before the course begins to get up to speed quickly
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  • Go through online tutorials on Python fundamentals
  • Solve easy coding exercises on platforms like HackerRank or LeetCode
Python Refresher
Review Python basics to ensure you have a strong foundation for building agents with LangGraph.
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  • Review Python syntax and data structures
  • Complete Python coding exercises
Review Python basics
Refresh your understanding of basic Python concepts to strengthen your foundation for building LLM applications.
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  • Review Python data types, variables, and operators.
  • Practice writing simple Python functions and loops.
  • Complete a few coding exercises to test your understanding.
12 other activities
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Show all 15 activities
LangChain Basics Review
Review the basics of LangChain to ensure a foundational understanding of the LLM framework used in this course.
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Show steps
  • Review LangChain documentation
  • Complete LangChain tutorials
  • Build a simple agent using LangChain
LangGraph Workshop
Attend a workshop to delve deeper into LangGraph and its applications in agent development.
Show steps
  • Register for the workshop
  • Attend the workshop sessions
  • Participate in hands-on exercises
Practice building agents using LangChain
Build several simple agents to practice using LangChain before covering the more complex LangGraph
Browse courses on LangChain
Show steps
  • Create a new LangChain project
  • Create a simple agent that responds to user input
Build a simple agent using LangChain
Follow guided tutorials to build a basic agent using LangChain, reinforcing your understanding of the framework's core concepts.
Browse courses on LangChain
Show steps
  • Find a beginner-friendly LangChain tutorial.
  • Set up your development environment and install the necessary tools.
  • Follow the tutorial step-by-step to build your agent.
  • Test your agent and make any necessary adjustments.
Agentic Search Exploration
Explore agentic search techniques to enhance the accuracy and relevance of your agent's responses.
Show steps
  • Follow tutorials on agentic search
  • Implement agentic search in your LangGraph agent
  • Analyze the impact of agentic search on agent performance
Practice using agentic search
Practice using agentic search to find answers for your agents
Show steps
  • Create a new agentic search query
  • Review the results and identify the most relevant answers
LLM Prompt Engineering Exercises
Practice writing effective prompts for LLMs to improve your ability to create controllable agents.
Show steps
  • Analyze existing LLM prompts
  • Write prompts for various agent tasks
  • Evaluate the effectiveness of different prompts
Develop an agent for a specific task
Building an agent for a specific task will allow you to apply your skills and knowledge in a practical setting
Show steps
  • Identify a specific task for your agent
  • Design the architecture of your agent
  • Implement your agent using LangGraph
  • Test and evaluate your agent
Develop an essay-writing agent
Challenge yourself by developing an essay-writing agent using LangGraph, applying your knowledge of agentic search and persistence.
Browse courses on Essay Writing
Show steps
  • Define the requirements and specifications for your agent.
  • Design the architecture of your agent, including the components and their interactions.
  • Implement the agent using LangGraph, incorporating agentic search and persistence.
  • Evaluate the performance of your agent and make improvements as needed.
LangGraph Peer Mentorship
Mentor other students in LangGraph to solidify your understanding and help others succeed.
Show steps
  • Volunteer as a mentor
  • Meet with mentees regularly
  • Provide guidance and support on LangGraph topics
LangGraph Agent Project
Build a complete agent using LangGraph to demonstrate your understanding of agent design and implementation.
Show steps
  • Design the agent's architecture and functionality
  • Implement the agent using LangGraph
  • Test and evaluate the agent's performance
  • Document the agent's development process
Summarize key concepts from the course
Writing a summary of the course's key concepts will reinforce your understanding and help you retain information better
Show steps
  • Review course materials and lectures
  • Identify the key concepts, definitions, and examples
  • Write a clear and concise summary of each concept
  • Use visuals, diagrams, or examples to illustrate your points

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