LangGraph Foundations Training introduces participants to graph-based prompting, chaining, and workflow orchestration for large language models using LangGraph. This course focuses on designing structured, stateful, and multi-step LLM workflows that go beyond linear prompt chains. Participants will learn core LangGraph concepts, graph design patterns, and practical use cases for building reliable and scalable LLM-powered applications.

• Understand the fundamentals of LangGraph and AI agent workflows.
• Build and manage multi-step AI applications using LangGraph.
• Design structured AI workflows with state and memory management.
• Integrate LLMs and tools into AI-driven processes.
• Apply best practices for scalable AI workflow development.

• AI engineers and developers
• Machine learning practitioners
• LLM application developers
• Technical architects and solution designers
• Developers working with LangChain or similar frameworks

• Basic understanding of large language models and prompting
• Familiarity with Python programming concepts
• Experience with APIs or LLM frameworks is helpful but not mandatory

• Understanding of graph-based LLM workflows and state management
• Ability to design and implement LangGraph-based prompt chains
• Skills to build multi-step, conditional, and branching LLM logic
• Improved reliability and control of LLM applications
• Awareness of best practices for scalable and maintainable LLM systems

By the end of this training, participants will be able to design and implement graph-based LLM workflows using LangGraph. Learners will gain practical skills to build reliable, scalable, and controllable LLM applications using structured prompting and chaining techniques.

Introduction to LangGraph and Graph-Based LLM Workflows
• Limitations of linear prompt chaining
• Overview of LangGraph architecture and components
• Use cases for graph-based LLM orchestration

Core Concepts of LangGraph
• Nodes, edges, and state management
• Execution flow and control logic
• Handling memory and context across steps

Designing Graph-Based Prompting Patterns
• Conditional branching and decision nodes
• Looping, retries, and fallback strategies
• Managing complex multi-step reasoning

Building Your First LangGraph Workflow
• Defining nodes and transitions
• Connecting LLM calls and tools
• Testing and debugging graph flows

Advanced LangGraph Patterns and Techniques
• Stateful workflows and long-running processes
• Error handling and recovery strategies
• Optimizing performance and execution

Integrating LangGraph with LLM Applications
• Combining LangGraph with LangChain components
• Tool usage and external API integration
• Managing prompts, outputs, and metadata

Real-World Use Cases and Architectures
• Conversational agents and assistants
• Multi-step reasoning and decision workflows
• Agentic and autonomous LLM systems

Best Practices, Ethics, and Reliability
• Reducing hallucinations and improving control
• Secure and responsible LLM workflow design
• Observability and monitoring considerations

Hands-on Practice and Workflow Exercises
• Guided LangGraph implementation tasks
• Real-world graph-based workflow scenarios
• Participant practice with feedback

• Introduction to LangGraph
• AI Agent and Workflow Fundamentals
• State and Memory Management
• LLM Integration and Tool Usage
• Workflow Design and Automation
• Practical LangGraph Exercises

• Participation in hands-on LangGraph exercises
• Practical workflow implementation assignments
• Scenario-based technical assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in LangGraph Foundations Training, validating their ability to build graph-based LLM workflows using LangGraph.