LangGraph & LLM Agents Workflows Training in Marina Bay, Singapore

This course introduces participants to LangGraph, a framework for building graph-based Large Language Model (LLM) applications and agent workflows. It covers the fundamentals of LLM orchestration, agent design, state management, and workflow automation. By the end, learners will be able to design, implement, and deploy robust LLM-driven applications with multi-step reasoning, memory, and tool integrations.

Introduction to LangGraph & Agent Workflows

• Evolution of LLM Orchestration: From LangChain to LangGraph

• Introduction to LangGraph

  • Graph-based workflow design

  • Key concepts: nodes, edges, state, persistence

• LLM Agents Fundamentals

  • Single-agent workflows

  • Prompt-driven task orchestration

Advanced Agent Design & Multi-Agent Workflows

• Stateful Agents with LangGraph

  • Managing memory and context across workflows

  • Error handling and recovery

• Multi-Agent Collaboration

  • Agent communication patterns

  • Task delegation and orchestration

• Tool Integration

  • Connecting APIs, databases, and external tools

  • Using retrievers and vector databases

Scaling, Evaluation & Best Practices

• Workflow Optimization and Performance

  • Parallelism and concurrency in LangGraph

  • Handling long-running tasks

• Evaluation and Debugging

  • Tracing, logging, and error analysis

  • LLM evaluation metrics and testing

• Production Deployment & LLMOps

  • Scaling LangGraph workflows

  • Security, governance, and reliability

Hands-on Exercises

Summary and Conclusion