LangGraph & LLM Agents Workflows Training in UK

This course introduces participants to LangGraph, a framework for building graph-based Large Language Model (LLM) applications and agent workflows. It covers the fundamentals of LLM orchestration, agent design, state management, and workflow automation. By the end, learners will be able to design, implement, and deploy robust LLM-driven applications with multi-step reasoning, memory, and tool integrations.

Welcome to the official LangGraph & LLM Agents Workflows Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in UK. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Basic knowledge of Python programming

• Understanding of LLMs and NLP concepts

• Familiarity with LangChain basics (preferred, not mandatory)

• Awareness of APIs and integration with external tools

• Proficiency in LangGraph framework

• Designing graph-based agent workflows

• Building stateful and collaborative LLM agents

• Tool and API integration with LLMs

• Debugging, evaluating, and optimizing LLM pipelines

• Applying LLMOps practices for real-world deployment

By completing this course, participants will:

1. Understand LangGraph architecture and its role in LLM orchestration.

2. Build agent workflows with stateful reasoning.

3. Implement multi-agent collaboration for complex tasks.

4. Integrate tools, APIs, and memory into LLM workflows.

5. Design scalable graph-based applications.

6. Apply best practices for reliability, evaluation, and optimization of LLM agents.