Agentic AI with LangChain

The course Agentic AI with LangChain from SpiralTrain teaches you how to build intelligent, autonomous AI agents that can reason, plan, and execute complex tasks.

Intro Agentic AI

The course Agentic AI with LangChain begins with a comprehensive introduction to agentic AI systems, exploring how they differ from traditional chatbots and what makes an agent truly autonomous. The architecture patterns, core components, and the role of LLMs as reasoning engines are discussed, along with common challenges and real-world use cases.

LangChain Fundamentals

This module provides a thorough foundation in the LangChain framework, covering its architecture, the distinction between chains and agents, and essential components like prompt templates, memory modules, and document loaders.

Building First Agent

Here participants create their first functional AI agent from scratch. The module covers choosing appropriate LLMs, defining clear agent goals, writing effective prompts, integrating tools, managing state, and implementing robust error handling.

Agent Tools and Actions

This part focuses on expanding agent capabilities through tools and actions. Participants learn to create custom tools, integrate APIs, connect to databases, implement search functionality, enable web scraping, and handle tool execution errors properly.

Memory and Context

Memory management is explored in depth, covering different memory types including short-term, long-term, conversation buffers, and vector stores. The module addresses entity memory, knowledge graphs, and techniques for optimizing memory.

Multi-Agent Systems

This module introduces collaborative multi-agent systems using frameworks like LangGraph. Topics include agent collaboration patterns, message passing between agents, task decomposition, workflow orchestration, and evaluating multi-agent performance.

RAG and Knowledge

Retrieval Augmented Generation is covered comprehensively, including document processing, embeddings, vector databases, and semantic search. Participants learn chunking strategies, and methods for evaluating RAG system performance.

Production Deployment

Deployment considerations are addressed with attention to API development, scalability, performance optimization, caching, rate limiting, and security best practices. The module also covers monitoring, observability, cost management, and testing strategies.

Audience Course Agentic AI with LangChain

This course is intended for software developers, data scientists and AI engineers, who want to build autonomous AI systems using LangChain.

Prerequisites Course Agentic AI with LangChain

Participants should know Python programming and a basic understanding of machine learning.

Realization Training Agentic AI with LangChain

The training combines theoretical instruction with hands-on exercises guided by an experienced trainer. Participants build working agents throughout the course.

Agentic AI with LangChain Certificate

After successful completion, participants receive a certificate of participation in Agentic AI with LangChain.

Modules

Module 1: Introduction to Agentic AI

• What is Agentic AI
• Agents vs Chatbots
• Agent Architecture Patterns
• LLMs as Reasoning Engines
• Agent Core Components
• Autonomy and Decision-Making
• Agent Frameworks Overview
• LangChain Introduction
• Use Cases and Applications
• Common Challenges

Module 2: LangChain Fundamentals

• LangChain Architecture
• Models and Prompts
• Chains vs Agents
• Prompt Templates
• Memory Modules
• Document Loaders
• Output Parsers
• Streaming Responses
• Tool Integration Basics
• LangSmith Debugging

Module 3: Building First Agent

• Choosing an LLM
• Defining Agent Goals
• Writing Effective Prompts
• Tool Selection and Integration
• Managing Agent State
• Error Handling Strategies
• Multi-Step Task Planning
• Agent Personality Design
• Logging and Monitoring
• Sandbox Environments

Module 4: Agent Tools and Actions

• Tool Abstractions
• Custom Tool Creation
• API Integration
• Search Tools
• Calculator and Math Tools
• Database Connections
• File System Access
• Web Scraping Tools
• Code Execution Tools
• Tool Error Handling

Module 5: Memory and Context

• Memory Types Overview
• Short-Term Memory
• Long-Term Memory
• Conversation Buffer
• Vector Store Memory
• Entity Memory
• Knowledge Graphs
• Memory Retrieval Strategies
• Context Window Management
• Memory Optimization

Module 6: Multi-Agent Systems

• Multi-Agent Concepts
• Agent Collaboration Patterns
• LangGraph Framework
• Agent Roles and Responsibilities
• Message Passing
• Task Decomposition
• Goal Refinement
• Workflow Orchestration
• Conflict Resolution
• Evaluation Strategies

Module 7: RAG and Knowledge

• Retrieval Augmented Generation
• Document Processing
• Embeddings and Vectors
• Vector Databases
• Semantic Search
• Chunking Strategies
• Hybrid Search
• Reranking Techniques
• Citation and Sources
• RAG Evaluation

Module 8: Production Deployment

• Agent Deployment Patterns
• API Development
• Scalability Considerations
• Performance Optimization
• Caching Strategies
• Rate Limiting
• Security Best Practices
• Monitoring and Observability
• Cost Management
• Testing Strategies

Module 9: Advanced Applications

• Coding Assistants
• Research Agents
• Customer Service Bots
• Finance and Analytics Agents
• Enterprise Automation
• Real-Time Agent Systems
• Guardrails and Safety
• Ethical Considerations
• Future of Agentic AI
• Capstone Project