#LangChain

'A practical tutorial demonstrating how to build local multi-agent AI workflows with AutoGen concepts, LangChain chains, and Hugging Face models, including full runnable code examples.'

'Learn how LangChain's DeepAgents add planning, subagents, and persistent file memory to create robust, multi-step LLM workflows, with a practical example and code snippets.'

'A practical tutorial showing how to combine LangChain tools with XGBoost to create an interactive end-to-end ML pipeline that generates data, trains a model, evaluates performance, and visualizes insights.'

'Tutorial showing how to use LangGraph with a Gemini model and a Wikipedia tool to add dialogue steps, record checkpoints, replay conversation history, and resume from prior states.'

'Agentic RAG adds autonomous planning, tool use, and iterative verification to traditional RAG, enabling deeper, multi-step reasoning and more reliable results across industries.'

Learn to build a sophisticated multi-agent research pipeline using LangGraph and Google's Gemini AI that automates research, analysis, and report generation for actionable insights.

This tutorial walks through building a modular text analysis pipeline with LangGraph, incorporating classification, entity extraction, summarization, sentiment analysis, and advanced conditional flow control.

This tutorial demonstrates building a modular AI system integrating Nomic embeddings and Gemini LLM for semantic understanding and contextual multi-agent orchestration, supporting both research and conversational tasks.

Learn how to build a GPU-accelerated Ollama LangChain workflow with integrated RAG agents and multi-session chat memory management, including performance monitoring for efficient local LLM deployment.

Discover a step-by-step guide to automate PubMed literature searches and analyze research trends using LangChain and AI-powered tools.

Discover how to create a multi-agent AI research team using LangGraph and Google’s Gemini API that automates data collection, analysis, and report writing seamlessly.

A detailed tutorial on setting up a multi-agent AI pipeline with CrewAI and Google Gemini in Colab, featuring research, analysis, and content creation agents working together.

Context engineering enhances AI performance by optimizing the input data fed to large language models, enabling more accurate and context-aware outputs across various applications.

Learn how to build a custom AI data analysis tool combining machine learning and statistics with LangChain to empower AI agents with actionable insights.

Learn how to build a versatile AI agent using Nebius and Llama 3 that integrates Wikipedia search and calculation tools for enhanced real-time reasoning capabilities.

This tutorial demonstrates how to create a groundedness verification tool using Upstage API and LangChain, featuring single and batch checks, multi-domain testing, and result analysis.

Learn how to build a sophisticated multi-tool AI assistant using Streamlit, LangChain, and Google Gemini API that supports real-time chat, web search, calculations, memory, and more.

Discover how to create a sophisticated web scraper combining BrightData proxies with Google's Gemini AI for enhanced data extraction and analysis.

'Learn how to build a secure, multi-tool AI agent in Google Colab by integrating Riza’s sandboxed Python execution with Google Gemini’s generative model using LangChain tools.'

Discover how to combine Google Gemini with Pandas and LangChain to perform natural language data analysis on the Titanic dataset, enabling interactive and code-free data insights.

Explore an advanced Python tutorial combining SerpAPI and Google Gemini-1.5-Flash to perform enriched web searches and AI-powered analysis for comprehensive research workflows.

Discover how to build a powerful web intelligence agent combining Tavily and Google Gemini AI to extract and analyze web content interactively.

Discover how to create an intelligent AI assistant using LangChain, Gemini 2.0, and Jina Search that provides real-time, well-sourced answers to user queries.

This tutorial details building an AI agent that writes, executes, and validates Python code live using LangChain and Anthropic Claude API, enabling robust computational workflows with automated correctness checks.

This guide walks through building a versatile multi-tool AI agent powered by LangGraph and Claude, covering setup, tool creation, workflow design, and interactive usage.

LangGraph Multi-Agent Swarm is a Python library that enables dynamic collaboration among specialized AI agents, improving multi-agent workflows through seamless handoffs and state management.

This guide shows how to build a semantic search and question-answering system using Together AI embeddings, FAISS vector search, and LangChain, all in a modular and efficient pipeline.

This tutorial explains how to build a memory-enabled chatbot combining Anthropic's Claude model with mem0 for context-rich conversations in Google Colab.

ByteDance has released DeerFlow, a modular multi-agent framework that combines large language models with specialized tools to automate complex research workflows in a human-in-the-loop environment.

This tutorial guides you through creating a REACT-style AI agent with Fireworks AI and LangChain that can fetch web content, generate SQL queries, and keep conversational context for complex AI tasks.

A comprehensive tutorial on integrating Dappier AI's real-time search and recommendation tools with OpenAI's chat API to build intelligent conversational applications with live data and personalized content.

Explore a step-by-step tutorial on integrating Arcade tools into LangGraph agents using Google's Gemini Developer API for creating powerful autonomous AI workflows.