Building a Collaborative Multi-Agent system

  • IntermediateLevel

  • 881+Students Enrolled

  • 30 MinsDuration

  • 4.7Average Rating

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About this Course

  • Master the fundamentals of multi-agent communication for efficient task-solving through agent collaboration.
  • Gain hands-on experience using LangGraph to build and manage collaborative multi-agent systems.
  • Develop skills in building research & data visualization using multi-agent systems.

Learning Outcomes

Multi-Agent Communication

Learn the fundamentals of task-solving through agent collaboration.

LangGraph Hands-On

Hands-on experience in building multi-agent systems with LangGraph.

Data Visualization

Expertise in using multi-agent systems for research and visualization.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Hands On: Build a Collaborative Multi- Agent System

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Lead Data Scientist, Author & Consultant, has 10+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels

Get this Course Now

With this course you’ll get

  • 30 Mins

    Duration

  • Dipanjan Sarkar

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

LangGraph is a framework designed to simplify the creation of collaborative multi-agent systems by leveraging graph-based structures for efficient communication and coordination.

LangGraph is built around nodes (agents), edges (communication), and tasks. Nodes represent agents with specific roles, edges define interactions, and tasks output.

LangChain focuses on building workflows with LLMs, offering prompt engineering, chaining, and memory management for generative AI tasks. Unlike general NLP frameworks like NLTK or Hugging Face, it excels in integrating tools like vector databases and APIs, making it ideal for retrieval-augmented generation and dynamic task execution.

LangGraph is an advanced framework built on LangChain, focusing on structured workflows, graph-based chaining, and improved modularity for building complex LLM-driven systems.

Yes, the course includes practical, hands-on projects where you’ll build a collaborative multi-agent system with LangGraph.

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