LangChain Advanced Agent Development Training

Build Production-Ready Multi-Agent AI Systems

Master the Leading Framework for Enterprise AI Applications

Learn to build sophisticated AI applications using LangChain’s latest 2025 features including modular agents, advanced memory systems, and enterprise integrations. From simple chains to complex multi-agent orchestration.

🎯 Course Overview

This intensive 3-day course covers LangChain’s cutting-edge capabilities for building enterprise AI applications. You’ll master agent development, memory management, and integration with the latest models including GPT-5 Turbo, Gemini 2 Ultra, and LLaMA 4.

What You’ll Master

• 🤖 Modular Agents: Planner, Executor, Communicator, and Evaluator patterns
• 🧠 Memory Systems: Short-term and long-term context management
• 🔗 Advanced Chains: RAG, router chains, and multi-modal pipelines
• 🏢 Enterprise Integration: Salesforce, Snowflake, Databricks, ServiceNow
• 🚀 Production Deployment: Streaming, scaling, and monitoring

Who Should Attend

• Developers building AI-powered applications
• Architects designing multi-agent systems
• Engineers integrating AI into enterprise platforms
• Teams migrating from legacy frameworks
• Anyone building production LangChain applications

📚 Detailed Curriculum

Day 1: Foundations & Core Architecture

Morning Session: LangChain 2025 Fundamentals

• Framework Evolution

  • What’s new in 2025: Modular agents and enhanced memory
  • Migration from older versions
  • Architecture patterns for scale
  • When to use LangChain vs. alternatives

• Core Components Deep Dive

  • Models: GPT-5, Gemini 2, Claude, LLaMA 4 integration
  • Prompts: Dynamic templates and prompt management
  • Chains: Sequential, parallel, and conditional execution
  • Memory: Conversation and entity memory modules

• Hands-On Lab 1: Build Your First Agent

  • Set up development environment
  • Create a basic conversational agent
  • Implement memory persistence
  • Add tool integration

Afternoon Session: Advanced Chain Development

• Chain Types & Patterns

  • Simple sequential chains
  • Router chains with conditional logic
  • RAG chains for knowledge retrieval
  • Multi-modal chains (text + image + audio)
  • Map-reduce for document processing

• Chain Optimization

  • Token management strategies
  • Parallel execution patterns
  • Error handling and retry logic
  • Cost optimization techniques

• Hands-On Lab 2: Complex Chain Implementation

  • Build a multi-step research assistant
  • Implement conditional routing
  • Add fallback mechanisms
  • Monitor performance metrics

Day 2: Agent Development & Memory Systems

Morning Session: Modular Agent Architecture

• Agent Components (2025 Pattern)

  • Planner Module: Task decomposition and strategy
  • Executor Module: Action implementation
  • Communicator Module: Inter-agent messaging
  • Evaluator Module: Result validation and improvement

• Multi-Agent Collaboration

  • Agent communication protocols
  • Shared memory and context
  • Task distribution strategies
  • CrewAI integration patterns

• Hands-On Lab 3: Build a Multi-Agent System

  • Create specialized agents for different tasks
  • Implement agent communication
  • Build shared knowledge base
  • Coordinate complex workflows

Afternoon Session: Advanced Memory Management

• Memory Architecture

  • Short-term vs. long-term memory
  • Vector memory for semantic recall
  • Graph memory for relationships
  • Hybrid memory strategies

• Context Management

  • Conversation continuity across sessions
  • Entity tracking and updates
  • Memory compression techniques
  • Privacy-aware memory handling

• Hands-On Lab 4: Production Memory System

  • Implement persistent memory store
  • Build context-aware retrieval
  • Add memory search capabilities
  • Handle memory overflow

Day 3: Enterprise Integration & Deployment

Morning Session: Enterprise Platform Integration

• Native Integrations

  • Salesforce: CRM data and automation
  • Snowflake Cortex: Data warehouse AI
  • Databricks: ML pipeline integration
  • ServiceNow: IT service automation

• Custom Tool Development

  • Tool interface design
  • Authentication handling
  • Rate limiting and quotas
  • Error recovery patterns

• Hands-On Lab 5: Enterprise Integration

  • Connect to enterprise data sources
  • Build custom tools for your APIs
  • Implement secure authentication
  • Create audit trails

Afternoon Session: Production Deployment

• Streaming & Real-time

  • Streaming response implementation
  • WebSocket integration
  • Event-driven architectures
  • Real-time agent coordination

• Scaling & Performance

  • Horizontal scaling strategies
  • Caching layer implementation
  • Load balancing agents
  • Performance monitoring

• Hands-On Lab 6: Deploy to Production

  • Containerize your application
  • Implement auto-scaling
  • Set up monitoring dashboard
  • Deploy multi-region setup

🛠️ Real-World Projects

Project 1: Enterprise Assistant Platform

Build a comprehensive AI assistant that:

• Integrates with multiple enterprise systems
• Handles complex multi-turn conversations
• Maintains context across sessions
• Scales to thousands of users

Project 2: Automated Research System

Create a multi-agent research platform that:

• Decomposes complex queries
• Distributes tasks to specialized agents
• Synthesizes findings from multiple sources
• Generates comprehensive reports

Project 3: Customer Success Automation

Develop an intelligent system that:

• Monitors customer interactions
• Predicts and prevents churn
• Automates support workflows
• Integrates with CRM and ticketing

💡 Advanced Topics Covered

Latest Model Integration

• GPT-5 Turbo optimization strategies
• Gemini 2 Ultra multi-modal capabilities
• LLaMA 4 fine-tuning integration
• Claude advanced reasoning features

Production Best Practices

• Security and compliance patterns
• Cost management at scale
• A/B testing frameworks
• Incident response procedures

Performance Optimization

• Token usage optimization
• Response time improvement
• Memory efficiency techniques
• Batch processing strategies

Monitoring & Observability

• Distributed tracing setup
• Custom metrics collection
• Alert configuration
• Performance dashboards

📋 Prerequisites

Required Knowledge

• Python programming (intermediate level)
• Basic understanding of APIs
• Familiarity with async programming
• Command line proficiency

Recommended Experience

• Prior exposure to LLMs (helpful)
• REST API development (beneficial)
• Basic DevOps concepts (useful)

Technical Requirements

• Laptop with 16GB+ RAM
• Python 3.9+ installed
• Docker Desktop
• IDE (VS Code recommended)
• Cloud account for deployment labs

💰 Pricing & Options

Training Formats

On-Site Training

• Price: $15,000 for up to 12 participants
• Duration: 3 consecutive days
• Includes: Customized examples for your use cases
• Bonus: Architecture review of your AI plans

Virtual Training

• Price: $10,000 for up to 12 participants
• Duration: 3 days (6 hours per day)
• Platform: Interactive online with breakout rooms
• Support: 30-day post-training assistance

Public Classes

• Price: $1,995 per participant
• Schedule: Bi-monthly sessions
• Locations: SF, NYC, Seattle, Austin, Remote
• Next Session: Check Calendar

What’s Included

• Comprehensive course materials (500+ pages)
• Production-ready code templates
• API credits for all labs
• LangChain Enterprise trial (3 months)
• Certificate of completion
• Alumni community access
• Quarterly update webinars

🎯 Learning Outcomes

By course completion, you will:

✅ Build production-grade multi-agent systems
✅ Master LangChain’s 2025 modular architecture
✅ Implement advanced memory management
✅ Integrate with enterprise platforms
✅ Deploy scalable AI applications
✅ Handle complex agent orchestration
✅ Optimize for performance and cost
✅ Monitor and maintain AI systems

👨‍🏫 Expert Instructors

Learn from practitioners building with LangChain daily:

• Current experience: Deploying LangChain in production
• Community leaders: LangChain contributors and advocates
• Enterprise background: Fortune 500 implementations
• Continuous learning: Updated with each LangChain release

🚀 Register Today

Transform Your AI Development Capabilities

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📚 Resources & Community

Pre-Training Resources

• LangChain 2025 Overview
• Agent Architecture Patterns
• Memory System Comparison
• Migration Guide

Post-Training Support

• 30 days of instructor Q&A
• Private Discord channel
• Monthly virtual meetups
• Early access to new features

Related Training

• OpenAI Advanced Integration
• Vector Database Mastery
• CrewAI Multi-Agent Systems

❓ Frequently Asked Questions

Q: How current is this training?
A: We update content monthly. This course includes all 2025 features including modular agents and enhanced memory systems.

Q: Do you cover CrewAI integration?
A: Yes! Day 2 includes CrewAI compatibility patterns and multi-agent orchestration.

Q: What about deprecated features?
A: We focus on current best practices and help you migrate from legacy patterns.

Q: Can we use our company’s data?
A: Absolutely! On-site training can use your actual use cases and data.

Q: Is this suitable for beginners?
A: Basic Python knowledge is required. We recommend our AI Fundamentals course first if you’re new to LLMs.

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🏆 Success Stories

🎓 Certification Path

Complete all labs and projects to earn:

• LangChain Developer Certificate
• Digital badge for LinkedIn
• Project portfolio entry
• Community recognition

Advanced certification available through:

• Extended project submission
• Peer review process
• Instructor evaluation