Feb 24
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Comparing LangChain vs LangGraph is one of the most confusing topics for a lot developers discovering AI workflows. These two frameworks are often misunderstood or mixed up, so we’re here to explain how they differ and when to use each. In this comparison, we break down Langchain vs Langgraph in a practical way with easy to use mental models. You’ll learn how these frameworks approach workflow design, control, and system complexity, and why those differences matter when building your LLM-powered applications. Instead of focusing on dry code examples, we show how each framework fits real development scenarios, from simple pipelines to more advanced systems. LangChain and LangGraph are excellent tools to construct various AI-powered applications and can also be used with Oxylabs Web scraper API to create RAG systems for LLMs with real-time access to public online data. However, the choice between LangGraph vs LangChain is very important early on in your design phase, so we hope this video helps you pick the right framework for your future AI systems. Learn more about data-powered AI workflows 👉https://oxy.yt/Ucjc 📚 *ADDITIONAL RESOURCES* LangChain Documentation: 👉 https://docs.langchain.com/oss/python/langchain LangGraph Documentation: 👉 https://docs.langchain.com/oss/python/langgraph 🔧 *OUR DATA & SCRAPING SOLUTIONS* AI Data: 👉 https://oxy.yt/dckc Web Scraper API: 👉 https://oxy.yt/mclM Residential Proxies: 👉 https://oxy.yt/Kczg ISP Proxies: 👉 https://oxy.yt/acxW ⏳ *TIMESTAMPS* 0:00 – Intro 0:32 – What is LangChain? 1:10 – LangChain example 2:31 – What is LangGraph? 3:24 – LangGraph example 4:27 – When to use each framework? 6:03 – Common misconceptions 6:51 – Direct comparison 7:55 – Conclusion 🤝 *LET'S CONNECT* Join our Discord: https://discord.gg/6FAVVryt9W © 2026 Oxylabs. All rights reserved. #LangChain #LangGraph #AIWorkflows #LLM #MachineLearning