Multi Agents with LangGraph

The course Multi Agents with LangGraph from SpiralTrain teaches you how to design and build sophisticated multi-agent AI systems using LangGraph.

Introduction LangGraph

The course Multi Agents with LangGraph begins with a comprehensive introduction to LangGraph, exploring how it differs from traditional agent frameworks. Graph-based architectures, StateGraph concepts, nodes, edges, and conditional routing are discussed.

Graph Fundamentals

This module covers essential graph theory concepts including directed graphs, state machines, and different node and edge types. Participants learn about entry points, conditional edges, cyclic graphs, and techniques for graph compilation and visualization.

State Management

State management in LangGraph is explored in depth, covering state schema definition using TypedDict, state updates, reducers, and immutability. The module addresses checkpointing, state persistence, restoration, and debugging techniques.

Building Agents

Here participants learn to build agent nodes with tool-calling capabilities using the ReAct pattern. Topics include custom agent logic, agent state management, error handling, monitoring, testing, and established best practices for robust agent development.

Multi-Agent Patterns

This part focuses on architectural patterns for multi-agent systems including hierarchical structures, supervisor patterns, and manager-worker configurations. Sequential, parallel, and collaborative agent patterns are explored along with orchestration strategies.

Agent Communication

Communication between agents is addressed through message passing, shared state, and handoff mechanisms. The module covers communication protocols, event systems, inter-agent messaging, state broadcasting, and synchronization techniques.

Advanced Workflows

Complex workflow patterns are introduced including human-in-the-loop systems, approval workflows, and branching logic. Topics include loop detection, retry mechanisms, fallback strategies, subgraphs, and workflow composition for sophisticated multi-agent applications.

Production Deployment

Deployment considerations are covered with focus on the LangGraph API, scaling strategies, and streaming responses. The module addresses persistence backends, checkpoint storage, cloud deployment options, and cost optimization for production environments.

Audience Course Multi Agents with LangGraph

This course is intended for AI engineers, software developers, and data scientists who want to build multi-agent systems using LangGraph and orchestrate AI workflows.

Prerequisites Course Multi Agents with LangGraph

Participants should have Python skills and understanding of LLMs and AI agents. Familiarity with LangChain, graph theory, and asynchronous programming is beneficial.

Realization Training Multi Agents with LangGraph

The training combines theoretical instruction with hands-on exercises guided by an expert trainer. Participants build real multi-agent systems throughout the course.

Multi Agents with LangGraph Certificate

After successful completion, participants receive a certificate of participation in Multi Agents with LangGraph.

Modules

Module 1: Introduction LangGraph

• LangGraph Overview
• Agents vs Workflows
• Graph-Based Architecture
• StateGraph Concepts
• Nodes and Edges
• Conditional Routing
• LangGraph vs LangChain
• Use Cases
• Installation and Setup
• Development Environment

Module 2: Graph Fundamentals

• Graph Theory Basics
• Directed Graphs
• State Machines
• Node Types
• Edge Types
• Entry Points
• Conditional Edges
• Cyclic Graphs
• Graph Compilation
• Graph Visualization

Module 3: State Management

• State in LangGraph
• State Schema Definition
• TypedDict States
• State Updates
• State Reducers
• Immutable State
• State Persistence
• Checkpointing
• State Restoration
• State Debugging

Module 4: Building Agents

• Agent Nodes
• Tool-Calling Agents
• ReAct Pattern
• Agent Executors
• Custom Agent Logic
• Agent State
• Error Handling
• Agent Monitoring
• Agent Testing
• Agent Best Practices

Module 5: Multi-Agent Patterns

• Hierarchical Agents
• Supervisor Pattern
• Manager-Worker Pattern
• Sequential Agents
• Parallel Agents
• Collaborative Agents
• Competitive Agents
• Specialized Agents
• Agent Orchestration
• Design Patterns

Module 6: Agent Communication

• Message Passing
• Shared State
• Agent Handoffs
• Communication Protocols
• Event Systems
• Inter-Agent Messages
• State Broadcasting
• Conflict Resolution
• Synchronization
• Communication Debugging

Module 7: Advanced Workflows

• Complex Workflows
• Human-in-the-Loop
• Approval Workflows
• Branching Logic
• Loop Detection
• Retry Mechanisms
• Fallback Strategies
• Subgraphs
• Workflow Composition
• Performance Optimization

Module 8: Production Deployment

• LangGraph API
• Deployment Strategies
• Scaling Considerations
• Streaming Responses
• Persistence Backends
• Checkpoint Storage
• Cloud Deployment
• Monitoring Solutions
• Cost Optimization
• Production Best Practices

Module 9: Real-World Applications

• Customer Support Systems
• Research Automation
• Code Review Agents
• Data Analysis Workflows
• Content Generation Pipelines
• Decision Support Systems
• Process Automation
• Testing Frameworks
• Case Studies
• Capstone Project