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Designing Agentic Systems with LangChain

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3 hr
2,800 XP
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Course Description

Agentic workflows that integrate LLMs and tools to perform nuanced tasks are at the forefront of the AI transformation. In this course, you'll learn the key principles behind LangChain agents, including configuring prompts, integrating tools, and managing complex workflows. By the end of this course, you'll be able to build intelligent systems that automate complex tasks, enhance productivity, and provide dynamic solutions tailored to specific business needs.

Master the Essentials of LangChain Agents

You'll learn how to integrate prompts, language models, and tools into workflows using the Reasoning and Action (ReAct) framework. Following that, you'll be able to set up agentic workflows, configure tools, and understand the core principles of LangChain agents while visualizing these workflows with LangGraph. You'll build custom agents, set up tools for accessing external data sources like the Wikipedia API, and manage agent states. You'll be guided through defining nodes and edges, creating conditional pathways, and assembling complex workflows that adapt to varying conditions.

Build Dynamic Chat Agents

Finally, you'll learn to monitor messages, define nodes for flexible function calling, and configure your chatbot for multiple-tool handling. By the end of this course, you'll be able to build intelligent systems that automate complex tasks, enhance productivity, and provide dynamic solutions tailored to specific business needs.
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  1. 1

    The Essentials of LangChain agents

    Free

    Build intelligent agentic systems! Discover the key components of LangChain agents, including how prompts, LLMs, and tools work together for reasoning and action. You'll set up an agent with OpenAI's API, define custom tools, and tackle real-world tasks like math calculations. Plus, explore how LangChain organizes data using graphs, nodes, and edges.

    Play Chapter Now
    Agents in LangChain
    50 xp
    Creating a ReAct agent
    100 xp
    Components of LangChain agents
    50 xp
    Building custom tools
    50 xp
    Create a tool for math calculations
    100 xp
    Integrating custom tools and queries
    100 xp
    Conversation with a ReAct agent
    50 xp
    Conversation setup
    100 xp
    Ask questions about conversation history
    100 xp
  2. 2

    Building Chatbots with LangGraph

    Build dynamic, tool-augmented chatbots with LangChain and LangGraph! You’ll explore how to create a chatbot that adapts based on user input by defining states and integrating external APIs for real-time information retrieval. You'll connect these components into a responsive graph structure, enabling smooth transitions between conversation and tool-assisted responses. By the end, you’ll have a visually represented chatbot framework with enhanced reasoning and multi-step workflows.

    Play Chapter Now
    Building graphs for chatbots
    50 xp
    Graph and agent states
    100 xp
    Adding nodes and edges
    100 xp
    Generating chatbot responses
    50 xp
    Queries and responses
    100 xp
    Generate a graph diagram
    100 xp
    Adding external tools to a chatbot
    50 xp
    Build a Wikipedia tool
    100 xp
    Add a tool to a graph
    100 xp
    Adding memory and conversation
    50 xp
    Visualize the graph and return tool outputs
    100 xp
    Adding graph memory
    100 xp
    Using graph memory for conversation
    100 xp
  3. 3

    Build Dynamic Chat Agents

    Expand your chatbot with dynamic tools and memory! Define and integrate multiple tools into flexible workflows, build functions for dynamic tool calling, and configure your chatbot for multiple-tool handling. Organize memory and outputs to enable interleaved, multi-turn conversations. By the end, you'll have created a sophisticated chatbot capable of complex interactions.

    Play Chapter Now
    Defining multiple tools
    50 xp
    Build a tool that invokes the LLM
    100 xp
    Build a tool with Python code
    100 xp
    Binding multiple tools
    100 xp
    Defining nodes and edges for flexible function calling
    50 xp
    Define a function that stops the chatbot
    100 xp
    Create a function to return an LLM response
    100 xp
    Create the graph workflow for multiple tools
    100 xp
    Organize chatbot outputs with memory
    50 xp
    Configure outputs for multiple tools
    100 xp
    Enable multi-turn conversation with memory
    100 xp
    Congratulations!
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

collaborators

Collaborator's avatar
James Chapman
Collaborator's avatar
Jasmin Ludolf
Collaborator's avatar
Francesca Donadoni

prerequisites

Developing LLM Applications with LangChain
Dilini K. Sumanapala, PhD HeadshotDilini K. Sumanapala, PhD

Founder & AI Engineer, Genverv Ltd.

Dilini K. Sumanapala, PhD, is a published cognitive neuroscientist with a background using machine learning to study how the brain processes and learns new information. After delivering national edtech interventions for her postdoctoral work at Birkbeck, University of London, she transitioned to machine-learning within industry. She later founded Genverv, Ltd. to focus on natural language processing, text-based agents, and generative AI. Dilini regularly speaks at AI panels in London and has given TEDx talks on neuroscience and responsible science communication.
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