LangChain versus LangGraph скачать в хорошем качестве

LangChain versus LangGraph

📹 VIDEO TITLE 📹 LangChain versus LangGraph ✍️VIDEO DESCRIPTION ✍️ In this video, we dive into the key differences between LangChain and LangGraph, two popular frameworks for building AI-powered workflows and pipelines. Whether you're working on a simple task automation or a complex AI workflow, understanding these frameworks' strengths and limitations is crucial. LangChain excels at linear task pipelines with minimal setup and a rich ecosystem of integrations, making it perfect for quick prototyping and straightforward use cases. On the other hand, LangGraph shines in managing complex, scalable workflows with its graph-based approach, offering unparalleled flexibility, explicit state management, and support for branching and cyclic dependencies. We explore practical examples to highlight when you might choose one framework over the other. For instance, if you're creating a chatbot with simple sequential steps, LangChain's simplicity and ease of use are unmatched. However, if your project involves complex workflows with multiple branching paths, reusable nodes, or requires tracking intermediate results, LangGraph's graph-based architecture and customizable state management make it the better choice. We also discuss community support, learning curves, and how each framework aligns with specific project goals. By the end of this video, you'll have a clear understanding of which framework is best for your specific use case. Whether you're a developer prototyping AI applications or building scalable, production-ready systems, this comparison will help you choose the right tool for the job. Don't forget to like, subscribe, and comment below to share your experience with LangChain and LangGraph! 🧑‍💻GITHUB URL 🧑‍💻 No code samples for this video 📽OTHER NEW MACHINA VIDEOS REFERENCED IN THIS VIDEO 📽 What is the Perceptron? -    • What is the Perceptron ?   What is the MP Neuron? -    • What is the MP Neuron ?   What is Physical AI ? -    • What is Physical AI ?   What is the Turing Test ? -    • What is the Turing Test?   What is LLM Alignment ? -    • What is LLM Alignment ?   What are Agentic Workflows? -    • What are Agentic Workflows ?   Why is AI going Nuclear? -    • Why is AI going Nuclear?   What is Synthetic Data? -    • What is Synthetic Data?   What is NLP? -    • What is NLP ?   What is Open Router? -    • What is Open Router ?   What is Sentiment Analysis? -    • What is Sentiment Analysis ?   What is Mojo ? -    • What is Mojo ?   SDK(s) in Pinecone Vector DB -    • SDK(s) in Pinecone Vector DB   Pinecone Vector DB POD(s) vs Serverless -    • Pinecone Vector Database PODS vs Serverless   Meta Data Filters in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   Namespaces in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   Fetches & Queries in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   Upserts & Deletes in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   What is a Pineconde Index -    • What is a Pinecone Index ?   What is the Pinecone Vector DB -    • What is a Pinecone Index ?   What is LLM LangGraph ? -    • What is LLM LangGraph?   AWS Lambda + Anthropic Claude -    • AWS Lambda + Anthropic Claude   What is Llama Index ? -    • What is LLM Llama Index ?   LangChain HelloWorld with Open GPT 3.5 -    • LangChain HelloWorld with Open GPT 3.5   Forget about LLMs What About SLMs -    • Forget about LLMs What About SLMs ?   What are LLM Presence and Frequency Penalties? -    • What are LLM Presence and Frequency Penalt...   What are LLM Hallucinations ? -    • What are LLM Hallucinations ?   Can LLMs Reason over Large Inputs ? -    • Can LLMs Effectively Reason over Large Inp...   What is the LLM’s Context Window? -    • What is the LLM's Context Window ?   What is LLM Chain of Thought Prompting? -    • What is LLM Chain of Thought Prompting?   Algorithms for Search Similarity -    • Algorithms for Search Similarity   How LLMs use Vector Databases -    • How LLMs use Vector Databases   What are LLM Embeddings ? -    • What are LLM Embeddings ?   How LLM’s are Driven by Vectors -    • How LLM’s are Driven by Vectors   What is 0, 1, and Few Shot LLM Prompting ? -    • What is 0, 1, and Few Shot LLM Prompting ?   What are the LLM’s Top-P and TopK ? -    • What are the LLM’s Top-P + Top-K ?   What is the LLM’s Temperature ? -    • What is the LLM’s Temperature ?   What is LLM Prompt Engineering ? -    • What is LLM Prompt Engineering ?   What is LLM Tokenization? -    • What is LLM Tokenization ?   What is the LangChain Framework? -    • What is the LangChain Framework?   CoPilots vs AI Agents -    • AI CoPilots versus AI Agents   What is an AI PC ? -    • What is an AI PC ?   What are AI HyperScalers? -    • What are AI HyperScalers?   What is LLM Fine-Tuning ? -    • What is LLM Fine-Tuning ?   What is LLM Pre-Training? -    • What is LLM Pre-Training?   AI ML Training versus Inference -    • AI ML Training versus Inference   What is meant by AI ML Model Training Corpus? -    • What is meant by AI ML Model Training Corpus?   What is AI LLM Multi-Modality? -    • What is AI LLM Multi-Modality?   What is an LLM ? -    • What is an LLM ?   Predictive versus Generative AI ? -    • Predictive versus Generative AI ?   🔠KEYWORDS 🔠 #langchain #langgraph #DAG #LLM