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LangChain & LangGraph

Live Online (VILT) & Classroom Corporate Training Course

Build LLM-powered applications and agentic workflows using LangChain and LangGraph - models, prompts, output parsers, chains, runnables, retrievers, document loaders, vector stores, memory, tool calling, agents, stateful graph workflows, checkpoints, routing and human-in-the-loop patterns.

Expert-Led VILT & Classroom 16 Hours Level: Advanced Certificate of Completion
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Overview

Build LLM-powered applications and agentic workflows using LangChain and LangGraph - models, prompts, output parsers, chains, runnables, retrievers, document loaders, vector stores, memory, tool calling, agents, stateful graph workflows, checkpoints, routing and human-in-the-loop patterns.

What You Will Learn

By the end of this course, learners will be able to:

  • Explain the LangChain ecosystem and its major application-building abstractions.
  • Integrate chat models, prompts, structured outputs and output parsers.
  • Build composable chains and runnable pipelines using LCEL.
  • Load, split, embed and retrieve documents using LangChain components.
  • Implement memory, tools and function/tool calling.
  • Build and evaluate tool-using LangChain agents.
  • Design stateful workflows using LangGraph nodes, edges and state.
  • Implement checkpoints, persistence and conditional routing.
  • Build human-in-the-loop workflows for approval and intervention.
  • Develop and demonstrate an end-to-end Agentic RAG application using LangChain and LangGraph.

Prerequisites

Python programming, LLM fundamentals, APIs, embeddings and basic RAG concepts are recommended.

Course Outline

  • Introduction to LangChain
  • LangChain Ecosystem and Core Components
  • LLM Application Architecture
  • LangChain Components and Abstractions
  • Building Blocks of a LangChain Application
  • LangChain Development Workflow
  • Model, Prompt and Output Pipelines
  • Common LangChain Use Cases
  • LangChain Application Design Patterns

  • Integrating Chat Models
  • Model Configuration
  • Model Parameters and Runtime Configuration
  • Prompt Templates
  • System, User and Dynamic Messages
  • Chat Prompt Construction
  • Reusable Prompt Templates
  • Output Parsers
  • Structured Output Generation
  • Schema-Based Responses
  • Handling Invalid Model Outputs

  • What are LangChain Chains?
  • Chain Composition
  • Sequential Chains
  • Runnable Fundamentals
  • LangChain Expression Language (LCEL)
  • RunnableSequence
  • RunnableParallel
  • RunnablePassthrough
  • RunnableLambda
  • Composing Reusable Pipelines
  • Streaming and Runtime Execution
  • Error Handling in Runnable Pipelines

  • Document Loading Fundamentals
  • LangChain Document Loaders
  • Loading PDFs, Web Pages and Text Documents
  • Document Objects and Metadata
  • Text Splitting and Chunking
  • Embedding Documents
  • Vector Store Fundamentals
  • Storing and Indexing Embeddings
  • Similarity Search
  • Retriever Interfaces
  • Top-k Retrieval and Filtering
  • Building a Retrieval Pipeline

  • Memory in LLM Applications
  • Conversation History
  • Short-Term and Persistent Memory
  • Managing Context Windows
  • LangChain Tools
  • Tool Definitions and Schemas
  • Tool Calling
  • Tool Invocation and Results
  • Multiple Tool Integration
  • Tool Error Handling
  • Secure Tool Execution

  • What is an AI Agent?
  • LangChain Agent Architecture
  • Agent Decision-Making
  • Tool Selection by Agents
  • Agent Execution Loops
  • Planning and Multi-Step Tasks
  • Agent Memory and Context
  • Agent Error Handling
  • Agent Observability and Evaluation
  • Designing Reliable LangChain Agents

  • Introduction to LangGraph
  • Why LangGraph?
  • Graph-Based Agent Architecture
  • Nodes and Edges
  • Graph State
  • State Updates
  • Start and End Nodes
  • Building a Basic State Graph
  • Sequential Graph Workflows
  • Agentic Graph Workflows
  • Graph Execution and Streaming

  • Designing Graph State
  • Typed and Structured State
  • State Transitions
  • Checkpoints and Persistence
  • Resuming Graph Execution
  • Conversation State
  • Conditional Edges
  • Conditional Routing
  • Dynamic Workflow Decisions
  • Retry and Recovery Patterns
  • Debugging Stateful Graphs

  • Human-in-the-Loop Fundamentals
  • Why Human Oversight Matters
  • Interrupting Agent Workflows
  • Human Approval and Review
  • Editing or Correcting Agent State
  • Resume and Continue Execution
  • Handling Sensitive or High-Impact Actions
  • Failure Recovery and Escalation
  • Designing Reliable Human-Agent Collaboration

  • Define the Agentic RAG Use Case
  • Design the End-to-End Architecture
  • Set Up the LangChain Environment
  • Implement Document Loaders
  • Implement Text Splitting and Chunking
  • Generate Document Embeddings
  • Configure the Vector Store
  • Build the Retriever
  • Create the RAG Prompt
  • Implement Context Injection
  • Define Agent Tools
  • Implement Tool Calling
  • Build the LangChain Agent
  • Design the LangGraph State
  • Create Agent Nodes and Workflow Edges
  • Implement Conditional Routing
  • Add Checkpoints and State Persistence
  • Add Human-in-the-Loop Approval
  • Implement Error Handling and Recovery
  • Test Multi-Step Agentic RAG Workflows
  • Evaluate Retrieval and Answer Quality
  • Optimize Performance and Cost
  • Final Demonstration

The Synergific Advantage

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Authorised curriculum is the floor — not the differentiator. What sits above it is what gets renewed.

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Live Hands-On Cloud Labs

Every course ships with 24/7 access to production-grade AWS, Azure, GCP and OCI environments. Real consoles, real workloads, the bills handled by us.

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Hire-Train-Deploy

We source, train, and deploy certified engineers on your project. Single contract, billable from week one, replacement guarantee on attrition.

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Direct line to a senior solutions advisor — not a ticket queue, not an offshore desk. Reply within four business hours, every time.

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1,100+ exam vouchers from 50+ OEMs at enterprise pricing. Most certifications include retake protection — success measured after the exam.

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Available Training Modes

Pick the format that fits your team.

Same authorised curriculum, same trainers, same hands-on cloud labs — delivered the way that works for you.

Live Online (VILT)

Real-time instructor-led sessions over Zoom or Teams. Same classroom, different time zones.

Most popular

Classroom

Face-to-face training delivered at your office, our Bengaluru centre, or any partner venue worldwide.

Onsite

Self-Paced

Recorded sessions plus 24/7 access to cloud labs and assessments. Learn at the pace that works for each engineer.

On-demand

Blended

Live workshops with self-paced reinforcement and project-based labs. Best for hybrid teams across regions.

Hybrid teams
All modes include: hands-on cloud labs, recordings, assessments, certificate of completion. Talk to a solutions advisor →

Our Training Process

How a course becomes measurable skill.

One contract, five steps, zero handoffs. From discovery to deployment, the same Synergific team owns the outcome — not a chain of vendors.

5 Steps from your scoping call to certified, productive engineers.
01

Discover & set goals

We start with a scoping call to understand your team's current skill level, target outcomes, deadlines, and certification needs — then translate that into a measurable success plan with named owners on both sides.

02

Curate the right path

We map the optimal learning path — instructor-led, self-paced, or blended — with hands-on cloud labs, prerequisite refreshers, and certification vouchers built in. No filler modules, no padded curriculum.

03

Deliver hands-on training

Authorised trainers run live sessions backed by 24/7 cloud labs and real-world projects. Theory and practice on the same day — learners stop forgetting concepts before they get to apply them.

04

Assess & mentor

Continuous skill checks, mock exams, and 1:1 mentoring keep the program honest. If anyone falls behind, we course-correct in-flight — you'll never find out at the end that two engineers couldn't keep up.

05

Certify & apply on the job

Voucher-backed certification, post-training office hours, and 30-day reinforcement so skills land on real work — not just on the exam scorecard. Success measured after the course ends, not before.

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