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DataCamp

Designing Agentic Systems with LangChain

via DataCamp

Overview

Get to grips with the foundational components of LangChain agents and build custom chat agents.

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.

Syllabus

  • The Essentials of LangChain agents
    • 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.
  • 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.
  • 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.

Taught by

Dilini K. Sumanapala, PhD

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