Agentic AI Engineer with LangChain and LangGraph

Get introduced to the course, meet your instructors, and learn how to set up and use your Vocareum OpenAI API key and assigned budget for hands-on activities.

Learn to build a simple LangChain app: integrate LLMs, manage chat history, use prompt templates, and apply few-shot prompting to create customizable chatbots with memory.

Discover structured outputs in AI: transform responses into actionable JSON for integration. Utilize schemas, parsers, and function calls to enhance reliability and automation in workflows.

Learn to parse and structure LLM outputs in LangChain using output parsers, TypedDict, Pydantic models, and automatic error correction for robust workflows.

Learn to build flexible, multi-step AI workflows in LangChain using Runnables and LCEL for composing, batching, and managing complex chains with ease and scalability.

Explore how AI evolves from basic LLM calls to fully autonomous agents, learn agentic frameworks, applications, and how to design effective agent-driven workflows.

Discover four key agentic design patterns—reflection, tool use, planning, and multi-agent collaboration—to boost LLM performance and reliability in complex tasks.

Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.

Learn how to encapsulate Python functions as tools in LangChain, enabling LLMs to call functions, interact with APIs, and handle external computations in AI workflows.

Learn to build dynamic, agent-driven AI workflows using LangGraph, leveraging nodes, edges, and routing for modular, adaptive application control and automation.

Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.

Learn structured state management in LangGraph for robust AI workflows, with focus on TypedDicts, reducers, routing, handlers, persistence, and practical multi-turn agent design.

Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.

Learn how LangGraph uses checkpointers and threads for agent memory, allowing persistent, isolated, and customizable conversations with snapshots, time travel, and personalized assistance.

Meet your instructor and get an overview of the course on building agentic systems in LangGraph.

Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.

Learn to integrate external APIs as tools in LangChain via LangGraph, enabling agents to fetch real-time data and enrich LLM responses using structured workflows and router logic.

Equip agents to access and modify structured data by using SQL for interaction and vector databases for semantic tasks, ensuring seamless integration with private systems.

Learn to build LangGraph database agents that use AI to convert natural language into SQL queries, enabling users to query databases conversationally without writing SQL.

Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.

Explore single-agent Retrieval-Augmented Generation in LangGraph, adding agentic decision-making for dynamic web search and improved knowledge-grounded AI responses.

Explore human-in-the-loop and observability techniques to monitor, debug, and optimize AI agents, ensuring oversight, transparency, and reliable performance at scale.

Learn how to use MLflow with LangGraph and LangChain to trace, log, and inspect each step of agent workflows, enabling better debugging, transparency, and observability.

Explore how to add human approvals or edits at key points in LangGraph agent workflows, enhancing safety and control with human-in-the-loop techniques.

Discover how to build reliable AI agents by defining success metrics, effective evaluation, continuous monitoring, and applying best practices for trustworthiness and real-world impact.

Learn to build, evaluate, and benchmark RAG pipelines and LangGraph agent workflows using RAGAS and MLflow for quality and correctness assessment.

Meet your instructors and get an overview of advanced agentic systems and course structure.

Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.

Learn how to persist agent memory in LangGraph using databases like SQLite and enhance long-term AI memory with vector storage via LangMem for robust, session-aware agents.

Explain the core components of multi-agent systems and how to design their high-level architecture.

Explore foundational multi-agent architecture patterns using LangGraph. Design, implement, and visualize orchestrated and peer-to-peer agent workflows for real-world scenarios.

Evaluate methods for tracking and updating agent state across multi-turn interactions.

Learn to design, implement, and orchestrate multi-agent workflows using LangGraph for structured, auditable, and automated content pipelines with specialized agents and custom state.

Apply orchestration techniques to coordinate multiple agent actions and achieve complex workflows.

Learn to orchestrate multi-agent workflows with LangGraph, using supervisors, handoffs, tool-calling, and structured state management for scalable, modular agent systems.

Configure routing mechanisms to manage data flow among agents in multi-agent systems.

Learn to implement content-based, round-robin, and priority-based data routing in agentic systems using LangGraph, Pydantic, and LangChain frameworks.