Master the design of AI-driven agents with LangChain and LangGraph. Automate workflows, implement RAG pipelines, and create interactive systems for knowledge management and application development.
Overview
Syllabus
- Course Introduction
- Meet your instructors and discover the skills you'll need to succeed in this AI agents course.
- From LLM Calls to Agents
- Explore how AI evolves from basic LLM calls to fully autonomous agents, learn agentic frameworks, applications, and how to design effective agent-driven workflows.
- OpenAI APIs
- Explore setting up and using the OpenAI SDK with Vocareum keys to build Python applications that leverage LLMs for content creation and business solutions.
- Agent Implementation
- Learn to build custom AI agents by abstracting LLMs, defining agent roles, managing responses, and adding memory for multi-turn conversations.
- Agentic Design Patterns
- Discover four key agentic design patterns—reflection, tool use, planning, and multi-agent collaboration—to boost LLM performance and reliability in complex tasks.
- Implementing Agent Self-Reflection
- Learn to enhance AI agents with memory and self-reflection, enabling them to critique, refine, and improve responses iteratively based on conversation history.
- Agent Function Calling
- Implementing Agentic Function Calling
- Learn how to enable AI agents to detect when to call functions, execute them with arguments, and integrate results into conversational responses using memory and tool abstractions.
- The ReAct Agent
- Discover the ReAct Agent, which combines reasoning and acting to help AI models plan and execute complex, multi-step tasks using external tools for improved decision-making.
- Implementing A ReAct Agent
- Learn to build a ReAct agent that plans, reasons, uses tools, and iterates until task completion, combining LLM reasoning with function execution using Python and OpenAI APIs.
- Multi-Agent Interaction
- Discover how multiple AI agents collaborate, communicate, and specialize to solve complex problems efficiently, with applications and key challenges in multi-agent systems.
- Implementing Multi-Agent Interaction
- Learn to implement multi-agent collaboration with specialized agents using function calls, supervision, and memory for coordinated task delegation in Python.
- Agentic Frameworks
- Discover agentic AI frameworks like LangChain, AutoGen, CrewAI, LangGraph, and PydanticAI to accelerate building robust, scalable AI applications with prebuilt tools and best practices.
- Concerns with AI Agents
- Creating a Simple LangChain application
- 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.
- LangChain Streaming
- Learn how to use LangChain’s streaming to get AI model responses chunk-by-chunk, enabling real-time, interactive, and responsive experiences in chatbots and AI applications.
- LangChain Structured Outputs
- Learn to parse and structure LLM outputs in LangChain using output parsers, TypedDict, Pydantic models, and automatic error correction for robust workflows.
- Multi-Step Workflows in LangChain
- 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.
- LangChain RAG
- Discover LangChain RAG: enhance LLMs with external knowledge by retrieving, augmenting, and generating accurate, context-aware responses using structured pipelines and prompt templates.
- Functions as Tools in LangChain
- 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.
- LangChain Agent Implementation
- Learn to build a simple LangChain agent that combines LLMs, memory, and tools for automated, multi-step workflows and natural user interactions.
- Agentic Workflows with LangGraph
- Learn to build dynamic, agent-driven AI workflows using LangGraph, leveraging nodes, edges, and routing for modular, adaptive application control and automation.
- LangGraph Agent State
- Learn to manage agent state in LangGraph using TypedDict for simple schemas or Pydantic for robust validation, supporting reliable, flexible AI workflow state machines.
- LangGraph Agent Implementation
- Learn to implement flexible, reliable AI agents using LangGraph by designing agentic workflows, managing parallel updates with reducers, and customizing behavior via configuration.
- LangGraph Database Agents
- 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.
- LangGraph Messaging
- Learn techniques in LangGraph for efficient message handling—limiting, trimming, summarizing messages, and using multiple state schemas for safe, modular agentic workflows.
- Short-Term Agent Memory
- Learn how short-term memory in LangGraph uses state, checkpoints, and threads to enable persistent, multi-turn agent workflows that remember conversation context across runs.
- Agent Knowledge
- Learn how to expand AI agents' knowledge and reliability using APIs, memory, context, and evaluation to build effective, safe, and scalable workflow solutions.
- Integrating External Tools and APIs with LangChain
- 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.
- Long-Term Agent Memory in LangGraph
- 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.
- Agentic RAG
- Explore Agentic RAG: how retrieval-augmented generation pipelines empower AI agents with up-to-date, relevant knowledge using embeddings, vector databases, and semantic search.
- Single-Agent Retrieval Augmented Generation in LangGraph
- Explore single-agent Retrieval-Augmented Generation in LangGraph, adding agentic decision-making for dynamic web search and improved knowledge-grounded AI responses.
- Agent Reliability & Evaluation
- Learn to ensure AI agent reliability through clear metrics, evaluation, testing, and ongoing monitoring for trustworthy, consistent, and scalable performance in real-world applications.
- Human-in-the-Loop & Observability
- Explore human-in-the-loop and observability techniques to monitor, debug, and optimize AI agents, ensuring oversight, transparency, and reliable performance at scale.
- LangGraph Agents with Human-in-the-Loop
- 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.
- LangGraph Agent Observability
- 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.
- LangGraph Agent Evaluation
- Learn to build, evaluate, and benchmark RAG pipelines and LangGraph agent workflows using RAGAS and MLflow for quality and correctness assessment.
- Security Concerns
- Learn key security concerns in AI deployments, common threats, and best practices like access control, input validation, explainability, and monitoring to ensure safe, resilient AI systems.
- Congratulations!
- Celebrate your achievement in mastering the core skills to design, build, and optimize advanced AI agentic systems beyond basic LLM use.
- HealthBot: AI-Powered Patient Education System
- Develop an AI agent to enhance patient education by delivering personalized, on-demand health information through summaries, comprehension checks, and quizzes about relevant medical topics.
Taught by
Henrique Santana
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