Tutorial sugli AI Agents

Learn what Langflow is, how to install it, and how to build simple and custom AI agent workflows using Python.

Develop a multi-tool agent application using LangGraph, Tavily, Python tool, and Mistral AI API in the DataLab environment.

Learn how to use OpenAI's Codex software engineering agent to fix bugs, explain code, and generate pull requests directly from ChatGPT.

Learn how to use Haystack's dataclasses, components, document store, generator, retriever, pipeline, tools, and agents to build an agentic workflow that will help you invoke multiple tools based on user queries.

AI-powered terminal built with Rust, designed to enhance developer productivity by combining intelligent command suggestions, reusable workflows, collaboration tools, and seamless customization.

Learn how to use Qwen-Agent and Qwen3 to build a real-time webpage summarizer extension.

Learn how to use n8n to build AI agents that automate email processing and create a retrieval-augmented generation (RAG) agent for document question answering.

Learn how to build a multi-agent travel assistant using Google's Agent Development Kit (ADK) and the Agent2Agent (A2A) protocol.

Learn how to build a multi-agent system using LangManus to analyze a trending GitHub repository, scrape its commit history, visualize activity trends, and generate a markdown report.

Learn how to build intelligent AI applications with OpenAI's Agents SDK. This comprehensive guide covers creating agents, implementing tools, structured outputs, and coordinating multiple agents.

Learn how to build an Agentic RAG pipeline from scratch, integrating local data sources and web scraping to generate context-aware responses to user queries.

Explore the AI-powered IDE with features like Cascade, Supercomplete, and Memories, designed to boost developer productivity using the AI flow.