Hands-On Generative AI with Multi-Agent LangChain: Building Real-World Applications
41mIntermediate2024-02-27
Authors
Nayan Saxena
Course details
As a developer in today’s rapidly evolving and constantly surprising AI landscape, it’s become critical to know how to leverage multiagent simulations and generative models. This course is designed to meet the burgeoning demand for AI professionals skilled in these domains. Join instructor Nayan Saxena for a comprehensive exploration of the process of building and running dynamic and interactive multiagent simulations using LangChain, the popular AI-powered framework. Get the skills you need to start building your first generative agents, equipping them with tools, and modeling complex generative agent scenarios. Along the way, test out your new technical know-how in the exercise challenges at the end of each section.
Skills covered
LangChainPyTorchMobile Device ManagementChatGPTNatural Language Processing (NLP)OpenAIFull-Stack Web DevelopmentGenerative AISoftware Development ToolsArtificial Intelligence (AI)Web DevelopmentNetwork and System AdministrationOpen SourceSoftware DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Generative agents - What and why
- 02 - What you should know
- 03 - Setting up your environment
1. Building Your First Generative Agents
- 04 - Understanding the role of memory
- 05 - Implementing your first generative agent
- 06 - Interacting and providing context to generative characters
- 07 - Setting up and running your first multi-agent simulation
- 08 - Challenge - Run a generative agent trivia night in LangChain
- 09 - Solution - Run a generative agent trivia night in LangChain
2. Modelling Complex Generative Agent Scenarios
- 10 - Implementing the dialogue agent class
- 11 - Implementing the dialogue simulator class
- 12 - Authoritarian vs. decentralized speaker selection
- 13 - Bidding for decentralized speaker selection
- 14 - Challenge - Simulate a startup pitch to investors
- 15 - Solution - Simulate a startup pitch to investors
3. Equipping Agents with Tools
- 16 - Overview of agent tools in LangChain
- 17 - Enabling an agent to access various tools
- 18 - Simulating a debate with tool integration
Conclusion
- 19 - Next steps in building real-world applications
Related courses
- Hands-On AI: Building AI Agents with Model Context Protocol (MCP) and Agent2Agent (A2A)
- Introduction to Agentic AI: Getting Started with AutoGen Studio
- Build with AI: Building a Project with the ChatGPT API
- Model Context Protocol (MCP): Hands-On with Agentic AI
- OpenAI API: Agents
- Build with AI: Creating AI Agents with GPT‑5
- Chat with Your Data Using ChatGPT
- Hands-On Agentic AI: Building AI Agents with LlamaIndex
Related learn paths
- Building Agentic AI Systems for Developers
- Building AI Agents: Advanced Techniques for Developers
- Getting Started with AI and Machine Learning
- Advance Your Skills in Deep Learning and Neural Networks
- Build AI Products Using Azure AI Services in Your Development Lifecycle
- Working Globally as an Individual Contributor
- Building Trust, Competence, and Collaboration for Global Success
- Hands-On Projects for OpenAI-Powered Apps