Build Gen AI Agents

Lo que serás capaz de hacer

**Brand New 2026 Version** This course has been re-designed, re-recorded, and rebuilt from the ground up to support **LangChain v1+, LangGraph v1+, latest frameworks, and modern agentic workflows.** Master LangGraph v1 and Ollama – Build Gen AI Agents is a complete and beginner-friendly course for anyone who wants to build real AI agents using LangGraph, LangChain, Ollama, and open source LLMs like GPT-OSS, Qwen3 and Gemma3. This course starts from the basics. You will understand every concept step by step and build fully working AI agent systems with real tools, memory, routing, and database integration. By the end of the course, you will be able to build production-ready AI agents that can search the web, call tools, interact with databases, store memories, follow human approval steps, and solve real-world tasks. Every lecture includes a live demo and a working example to help you learn through hands-on experience. What You Will Learn Ollama and Open Source LLMs Install and set up Ollama with the latest LangChain v1 updates Work with models like Qwen3 and Gemma3 Test context handling and realtime search settings Do quick document analysis Inspect and benchmark models Understand how to select the right model for a project Use all important Ollama commands Create custom Ollama models Use Ollama message commands Make raw API requests Load uncensored GGUF models for educational research LangChain v1 Fundamentals Set up LangSmith for debugging and tracing Understand open source tracing tools like LangFuse and Opik Use ChatOllama with Gemma3 Produce responses and trace them in LangSmith Reuse prompts with ChatPromptTemplate Chain multiple runnables Format messages with StrOutputParser Create structured output with Pydantic LangGraph Beginner to Advanced Understand flow engineering and finite state machines Create custom states and nodes Learn how LangGraph updates states Execute nested nodes Build and visualize LangGraph workflows Invoke graphs and watch state changes happen Conditional Routing Build sentiment analysis workflows Create Pydantic models for classification Route outputs to different nodes Generate positive and negative responses using LangGraph Build a complete sentiment analysis graph ReAct Agent with LangGraph Learn Chain of Thoughts and Tree of Thoughts patterns Understand ReAct agent design Manage agent state Create tools for weather and calculations Build agent nodes and enable debugging Create conditional tool execution Build a complete ReAct agent Test tool calls, internal states, and parallel execution Agent Memory and Streaming Understand how agent memory works Set up memory notebooks Build agents with MemorySaver Stream agent output Recall chat history Short Term Memory Learn the idea of persistence Separate long term and short term memory Save agent memory to SQLite Create a free PostgreSQL database online Store and retrieve memory from PostgreSQL Long Term Memory Build long term memory storage Use store namespace and put functions Save, fetch, and delete long term memory items Create semantic search for memories Build tools to save and retrieve user memory Build long term and short term memory agents Interrupt and Human in the Loop Understand human approval workflows Add guardrails to protect PII Build interruptible tools like money transfer Create HITL nodes and routers Build SQLite persistence for agent state Execute workflows that need user approval Stress test guardrail and approval flows Reflection Agent Build a researcher agent with critique feedback Add free web search tools Build routing logic for self evaluation Combine agents into a reflection loop Evaluate and test reflection workflows MySQL ReAct Agent Connect LangGraph with a MySQL database Extract database schema Generate SQL queries using LangChain tools Validate queries and fix errors automatically Execute complex queries using agents Compare Qwen3 with GPT OSS models for database tasks Search Hotels with Airbnb MCP Servers Understand the Model Context Protocol Use the Airbnb MCP server Build LangGraph MCP client workflows Run MCP based agents in Jupyter Who This Course Is For Beginners who want to learn AI agents Developers working with LangChain or open source LLMs Students and professionals entering Gen AI development Python programmers exploring agent systems Anyone who wants to build complete production ready AI agents No advanced experience is required. Only basic Python knowledge is enough. Why This Course Is Different Beginner friendly language Covers everything end to end: LLMs, LangChain, LangGraph, Agents Live coding with practical examples Includes latest updates for LangChain v1 and Ollama Focuses on production ready workflows Uses open source models so there is no API cost By the End of This Course You Will Be Able To Build complete AI agents using LangGraph Use tools, databases, and APIs inside agents Add short term and long term memory Add human approval and guardrails Use Ollama to run powerful LLMs locally Create stateful and fully working Gen AI applications

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Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.

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