LangChain & LangGraph Course in Hyderabad
Build tool-using agents and stateful AI workflows with LangChain and LangGraph
The LangChain and LangGraph course teaches you to build production-minded LLM applications and agents in Python. You start with LLM API basics, prompt templates, structured output and tool calling, then use LangChain 1.0 to build agents with the create_agent function, middleware, memory and retrieval. You move to LangGraph for stateful, controllable workflows: state, nodes and edges, conditional routing, loops, checkpointing and persistence, human-in-the-loop approvals, streaming, subgraphs and multi-agent patterns. Both libraries reached version 1.0 and require a recent Python 3.x version, and the course explains what changed from older tutorials, including legacy chains moving to langchain-classic. Later modules cover retrieval-augmented generation, evaluation and tracing with LangSmith, guardrails and security, deployment with FastAPI and the LangGraph platform options, and cost control. The course suits developers and data professionals with intermediate Python. You build several agents and a capstone workflow. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
LLM Application Foundations
- •Chat model APIs, messages and parameters
- •Python environment, dependency management and secrets
- •Structured output with Pydantic schemas
- •Lab: Call a chat model and parse a typed response
- •Outcome: Set up a clean LLM development environment
LangChain 1.0 Core Concepts
- •Package layout: langchain, langchain-core, provider packages and langchain-classic
- •Prompt templates, runnables and the expression language
- •Migrating older chains and tutorials
- •Lab: Build and test a small chain and port an older example
- •Outcome: Read and write current LangChain code
Tools & Tool Calling
- •Defining tools with decorators and schemas
- •How tool calling works; parallel tools and validation
- •Error handling and retries for tools
- •Lab: Give a model search, calculator and database tools
- •Outcome: Let models act safely through tools
Agents with create_agent & Middleware
- •Building agents with create_agent and the agent loop
- •Middleware: before and after model hooks, summarisation and dynamic prompts
- •Structured responses and model selection
- •Lab: Build an agent with a custom middleware for logging
- •Outcome: Build maintainable single agents
Memory & Conversation State
- •Message history and trimming
- •Short-term thread state versus long-term memory stores
- •Summaries and user profiles
- •Lab: Add persistent memory to an assistant
- •Outcome: Make assistants remember what matters
Retrieval & RAG with LangChain
- •Document loaders, text splitters and embeddings
- •Vector stores, retrievers and RAG chains
- •Agentic RAG with retrieval as a tool
- •Lab: Build a document Q&A agent with citations
- •Outcome: Combine retrieval with agents
LangGraph: State, Nodes & Edges
- •State schemas, reducers and node functions
- •Edges, conditional routing and loops
- •Compiling and running graphs; visualising flows
- •Lab: Build a routing workflow with conditional edges
- •Outcome: Model workflows as explicit graphs
Persistence, Checkpointing & Time Travel
- •Checkpointers and thread identifiers
- •Durable execution, resuming after failure and replaying steps
- •Production checkpoint stores such as PostgreSQL
- •Lab: Interrupt and resume a workflow across process restarts
- •Outcome: Build workflows that survive failures
Human-in-the-Loop & Streaming
- •Interrupts and approval steps before sensitive actions
- •Editing state and resuming
- •Streaming tokens, updates and events to a UI
- •Lab: Add an approval step before an email is sent
- •Outcome: Keep humans in control of risky actions
Multi-Agent Patterns
- •Supervisor and swarm or handoff patterns
- •Subgraphs and shared versus private state
- •When a single agent is better than many
- •Lab: Build a supervisor with researcher and writer agents
- •Outcome: Split work across agents with clear contracts
Evaluation, Tracing & Security
- •Tracing runs and debugging with LangSmith or an open alternative
- •Datasets, evaluators and regression tests for agents
- •Prompt injection, tool permissions and secrets
- •Lab: Create an evaluation dataset and run it after each change
- •Outcome: Test agents like software
Deployment & Capstone
- •Serving agents with FastAPI and Docker; LangGraph platform options overview
- •Latency, caching and token cost control
- •Capstone: stateful support or research agent with tools, memory, approvals and tests
- •Portfolio write-up and interview practice
- •Outcome: A deployed, tested agent application
Tools Covered
Projects Included
- Tool-Using Research Agent: Build an agent that searches, summarises and cites sources; deliverable is a working agent and trace screenshots.
- Document Q&A Agent: Combine retrieval and tools to answer questions over PDFs; deliverable is a web demo and test sheet.
- Approval Workflow: Create a LangGraph flow with an interrupt before an external action; deliverable is a resumable workflow and demo.
- Supervisor Multi-Agent Writer: Coordinate researcher and writer agents on a shared state; deliverable is a graph and sample outputs.
- Capstone Support Agent: Build a stateful agent with tools, memory, approvals and tests; deliverable is a GitHub repository and architecture diagram.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the LangChain & LangGraph course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on LangChain & LangGraph.
Certificate of Completion
LangChain & LangGraph
This certifies that
[Your Name]
has successfully completed the
LangChain & LangGraph
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured LangChain & LangGraph course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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What is the difference between LangChain and LangGraph?
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Is LangSmith required?
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Best LangChain & LangGraph Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Generative AI Developer, AI Agent Engineer, AI Engineer, Python Developer (LLM applications); support is guidance with the job search, not a guarantee of a job.