Building Conversational Agents with LangChain Training Course

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications using composable graphs that maintain persistent state and offer control over execution.

This instructor-led, live training (available both online and onsite) is targeted at advanced-level AI platform engineers, DevOps for AI, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.

By the end of this training, participants will be able to:

• Design and optimize complex LangGraph topologies for improved speed, cost efficiency, and scalability.
• Ensure reliability through techniques such as retries, timeouts, idempotency, and checkpoint-based recovery.
• Effectively debug and trace graph executions, inspect state, and systematically reproduce issues encountered in production environments.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and costs.

Format of the Course

• Interactive lectures and discussions.
• Ample exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at developers and IT professionals of all skill levels who wish to automate tasks and processes using AI without writing extensive code.

By the end of this training, participants will be able to:

• Design and implement complex workflows using n8n's visual programming interface.
• Integrate AI capabilities into workflows using LangChain.
• Build custom chatbots and virtual assistants for various use cases.
• Perform advanced data analysis and processing with AI agents.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at beginner-level business analysts and automation engineers who wish to understand how to use LangChain and APIs for automating repetitive tasks and workflows.

By the end of this training, participants will be able to:

• Understand the basics of API integration with LangChain.
• Automate repetitive workflows using LangChain and Python.
• Utilize LangChain to connect various APIs for efficient business processes.
• Create and automate custom workflows using APIs and LangChain’s automation capabilities.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at advanced-level AI researchers and policy makers who wish to explore the ethical implications of AI development and learn how to apply ethical guidelines when building AI solutions with LangChain.

By the end of this training, participants will be able to:

• Identify key ethical issues in AI development with LangChain.
• Understand the impact of AI on society and decision-making processes.
• Develop strategies for building fair and transparent AI systems.
• Implement ethical AI guidelines into LangChain-based projects.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level web developers and UX designers who wish to leverage LangChain to create intuitive and user-friendly web applications.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of LangChain and its role in enhancing web user experience.
• Implement LangChain in web apps to create dynamic and responsive interfaces.
• Integrate APIs into web apps to improve interactivity and user engagement.
• Optimize user experience using LangChain’s advanced customization features.
• Analyze user behavior data to fine-tune web app performance and experience.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

• Understand the fundamentals of LangChain and its components.
• Integrate LangChain with large language models (LLMs) like GPT-4.
• Build modular AI applications using LangChain.
• Troubleshoot common issues in LangChain applications.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at advanced-level data engineers and DevOps professionals who wish to leverage LangChain's capabilities by integrating it with various cloud services.

By the end of this training, participants will be able to:

• Integrate LangChain with major cloud platforms such as AWS, Azure, and Google Cloud.
• Utilize cloud-based APIs and services to enhance LangChain-powered applications.
• Scale and deploy conversational agents to the cloud for real-time interaction.
• Implement monitoring and security best practices in cloud environments.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level data professionals who wish to use LangChain to enhance their data analysis and visualization capabilities.

By the end of this training, participants will be able to:

• Automate data retrieval and cleaning using LangChain.
• Conduct advanced data analysis using Python and LangChain.
• Create visualizations with Matplotlib and other Python libraries integrated with LangChain.
• Leverage LangChain for generating natural language insights from data analysis.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

• Grasp the fundamental principles of LangChain.
• Set up and configure the LangChain environment.
• Understand the architecture and how LangChain interacts with large language models (LLMs).
• Develop simple applications using LangChain.

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications through composable graphs that maintain persistent state and provide control over execution.

This instructor-led, live training (available both online and onsite) is tailored for intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based financial solutions with proper governance, observability, and compliance.

By the end of this training, participants will be able to:

• Create finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Incorporate financial data standards and ontologies into graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to ensure performance, cost efficiency, and service level agreements (SLAs).

Format of the Course

• Interactive lectures and discussions.
• Plenty of exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework designed for creating graph-structured LLM applications that support planning, branching, tool integration, memory management, and controlled execution.

This instructor-led, live training (available online or on-site) is tailored for beginner-level developers, prompt engineers, and data professionals who aim to design and build reliable, multi-step LLM workflows using LangGraph.

By the end of this training, participants will be able to:

• Understand key LangGraph concepts such as nodes, edges, and state, and know when to apply them.
• Create prompt chains that can branch out, call external tools, and maintain a memory state.
• Incorporate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and assess LangGraph applications for reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises focusing on design, testing, and evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these features are essential for compliance, interoperability, and developing decision-support systems that align with medical processes.

This instructor-led, live training (conducted online or on-site) is designed for intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

By the end of this training, participants will be able to:

• Create healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Implement best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications in healthcare production settings.

Format of the Course

• Interactive lectures and discussions.
• Practical exercises with real-world case studies.
• Hands-on implementation practice in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications through composable graphs that maintain persistent state and offer precise control over execution.

This instructor-led, live training (conducted online or on-site) targets intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.

By the end of this training, participants will be able to:

• Design legal-specific LangGraph workflows that ensure auditability and compliance.
• Integrate legal ontologies and document standards into the graph state and processing.
• Implement safeguards, human-in-the-loop approvals, and traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in production with observability and cost controls.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework designed to create graph-structured workflows for LLMs, enabling branching, tool usage, memory management, and controllable execution.

This instructor-led, live training (available online or on-site) is targeted at intermediate-level engineers and product teams who want to integrate LangGraph’s graph logic with LLM agent loops to develop dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.

By the end of this training, participants will be able to:

• Design graph-based workflows that effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallback mechanisms for robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and enhance the reliability and safety of agent behavior.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based design exercises and peer reviews.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a graph-based orchestration framework that facilitates conditional, multi-step workflows involving LLMs and tools, making it ideal for automating and personalizing content pipelines.

This instructor-led, live training (available online or on-site) is designed for intermediate-level marketers, content strategists, and automation developers who want to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.

By the end of this training, participants will be able to:

• Design graph-structured content and email workflows incorporating conditional logic.
• Integrate LLMs, APIs, and data sources for automated personalization.
• Manage state, memory, and context throughout multi-step campaigns.
• Evaluate, monitor, and optimize the performance and delivery outcomes of workflows.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs for implementing email workflows and content pipelines.
• Scenario-based exercises focusing on personalization, segmentation, and branching logic.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.