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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.
Read more
LangChain, a popular open source framework for building LLM applications, recently introduced LangGraph. This extension allows developers to create highly controllable agents.
Read more
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!
Register for this course and see more details by visiting:
OpenCourser.com/course/hidbdx/ai
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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
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
Save this course
Save AI Agents in LangGraph to your list so you can find it easily later:
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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
Show steps
Review Python's syntax and semantics before the course begins to get up to speed quickly
Browse courses on
Python
Show steps
-
Go through online tutorials on Python fundamentals
-
Solve easy coding exercises on platforms like HackerRank or LeetCode
Python Refresher
Show steps
Review Python basics to ensure you have a strong foundation for building agents with LangGraph.
Browse courses on
Python
Show steps
-
Review Python syntax and data structures
-
Complete Python coding exercises
Review Python basics
Show steps
Refresh your understanding of basic Python concepts to strengthen your foundation for building LLM applications.
Browse courses on
Python
Show steps
-
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
Expand to see all activities and additional details
Show all 15 activities
LangChain Basics Review
Show steps
Review the basics of LangChain to ensure a foundational understanding of the LLM framework used in this course.
Browse courses on
LangChain
Show steps
-
Review LangChain documentation
-
Complete LangChain tutorials
-
Build a simple agent using LangChain
LangGraph Workshop
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review Python coding skills
Show steps
Review Python's syntax and semantics before the course begins to get up to speed quickly
Browse courses on
Python
Show steps
Show steps
Show steps
- Go through online tutorials on Python fundamentals
- Solve easy coding exercises on platforms like HackerRank or LeetCode
Python Refresher
Show steps
Review Python basics to ensure you have a strong foundation for building agents with LangGraph.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data structures
- Complete Python coding exercises
Review Python basics
Show steps
Refresh your understanding of basic Python concepts to strengthen your foundation for building LLM applications.
Browse courses on
Python
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all 15 activities
LangChain Basics Review
Show steps
Review the basics of LangChain to ensure a foundational understanding of the LLM framework used in this course.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Review LangChain documentation
- Complete LangChain tutorials
- Build a simple agent using LangChain
LangGraph Workshop
Show steps
Attend a workshop to delve deeper into LangGraph and its applications in agent development.
Show steps
Show steps
Show steps
- Register for the workshop
- Attend the workshop sessions
- Participate in hands-on exercises
Practice building agents using LangChain
Show steps
Build several simple agents to practice using LangChain before covering the more complex LangGraph
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Create a new LangChain project
- Create a simple agent that responds to user input
Build a simple agent using LangChain
Show steps
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
Show steps
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
Show steps
Explore agentic search techniques to enhance the accuracy and relevance of your agent's responses.
Show steps
Show steps
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
Show steps
Practice using agentic search to find answers for your agents
Show steps
Show steps
Show steps
- Create a new agentic search query
- Review the results and identify the most relevant answers
LLM Prompt Engineering Exercises
Show steps
Practice writing effective prompts for LLMs to improve your ability to create controllable agents.
Show steps
Show steps
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
Show steps
Building an agent for a specific task will allow you to apply your skills and knowledge in a practical setting
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Mentor other students in LangGraph to solidify your understanding and help others succeed.
Show steps
Show steps
Show steps
- Volunteer as a mentor
- Meet with mentees regularly
- Provide guidance and support on LangGraph topics
LangGraph Agent Project
Show steps
Build a complete agent using LangGraph to demonstrate your understanding of agent design and implementation.
Show steps
Show steps
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
Show steps
Writing a summary of the course's key concepts will reinforce your understanding and help you retain information better
Show steps
Show steps
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
Career center
Learners who complete AI Agents in LangGraph will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/hidbdx/ai
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hidbdx/ai
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Harrison Chase
Rotem Weiss
Session
August 12, 2024
- August 26, 2024
Language
English
Good to know
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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