#Langgraph Agents

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#Langgraph Agents Reel by @debugging_dost - If I could restart my AI/ML journey, I'd learn AI Agents first. LangChain + LangGraph = real-world AI systems 🤯 Not just models. Not just prompts.
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@debugging_dost
If I could restart my AI/ML journey, I’d learn AI Agents first. LangChain + LangGraph = real-world AI systems 🤯 Not just models. Not just prompts. Agentic AI is the future. Follow us for no-noise AI learning. #AI #AIML #AIAgents #LangChain #LangGraph
#Langgraph Agents Reel by @python.trainer.helper - Ever tried to build a production-ready LLM application and felt like you were drowning in a sea of disconnected APIs, memory issues, and limited funct
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@python.trainer.helper
Ever tried to build a production-ready LLM application and felt like you were drowning in a sea of disconnected APIs, memory issues, and limited functionality? You’re not alone. As shown in the video, building Without LangChain is often a chaotic mess. To build robust, scalable, and intelligent apps, you need an Orchestrator. As an IIT Roorkee Alumnus with 21 years of IT experience and 11 years dedicated specifically to Gen AI training, I’ve seen this transition first-hand. LangChain is the bridge that turns a basic chatbot into a powerful, production-ready tool by managing: Memory Management: Giving your AI a "conversation history". Retrieval (RAG): Connecting models to your specific data. Tool Use: Allowing AI to interact with external APIs and functions. Stop struggling with complex integrations. Join my upcoming Generative AI batch and learn how to build the "Organized & Powerful" way. 👇 Subscribe & Follow for more AI Insights! YouTube/Instagram/LinkedIn: python.trainer.helper #LangChain #LLM #GenerativeAI #AIArchitecture #RAG #LargeLanguageModels #Orchestration
#Langgraph Agents Reel by @the.datascience.gal (verified account) - Comment "AI" to get access to LangChain & LangGraph hands-on workshop If you're an aspiring AI engineer, this is one of the toolkits you absolutely n
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@the.datascience.gal
Comment "AI" to get access to LangChain & LangGraph hands-on workshop If you’re an aspiring AI engineer, this is one of the toolkits you absolutely need to learn. I started building with LangChain back in end of 2022 while at Google, when it was literally being built- so every other day there was a new update that I had to read their docs for. And LangChain today has been the quiet backbone of so many real LLM applications. Once you move beyond simple prompts and start building workflows, retrieval systems, or anything agentic, you start to see why this framework matters. LangChain gives you a way to structure the entire lifecycle of an LLM driven system so you’re not juggling scattered scripts and brittle glue code. ✦ It standardizes how you work with chat models, messages, memory, and tools ✦ It gives you prompt templates and chains so your system behaves consistently ✦ It brings retrievers, vector stores, and document loaders into a clean workflow ✦ It sets the foundation for agentic execution, where models can reason and take actions ✦ And LangGraph is built on top of LangChain to help you design stateful, multi step, and multi agent workflows with much more control.
#Langgraph Agents Reel by @techtalkbyravindra - When to Use LangChain? If your GenAI app is still small and simple… LangGraph might be overkill. Here's where LangChain is perfect. LangChain is bui
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@techtalkbyravindra
When to Use LangChain? If your GenAI app is still small and simple… LangGraph might be overkill. Here’s where LangChain is perfect. LangChain is built to create LLM‑powered applications: chatbots, assistants, Q&A apps, RAG systems. Think of it as: Retrieve relevant data Summarize/process with LLM Return the final answer All of this usually happens in a sequential flow. Here is an example: You’re building a chatbot that answers questions from PDFs and websites. LangChain gives you document loaders, text splitters, vector embeddings, and chaining to go from “user question” → “fetch context” → “LLM answer” in a straight line. If you’re starting with classic RAG chatbots, follow and save this so you don’t over‑complicate your stack. #LangChainBasics #RAG #AIChatbots
#Langgraph Agents Reel by @falconreality - AI workflows are getting complex… That's where LangGraph comes in. An open-source framework for building AI agents with memory, tools, and multi-age
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@falconreality
AI workflows are getting complex… That’s where LangGraph comes in. An open-source framework for building AI agents with memory, tools, and multi-agent collaboration. Follow Falcon Reality for simple AI explainers. #AI #LangGraph #AIagents #Automation #FutureOfAI
#Langgraph Agents Reel by @learning_intelligence_tamil - Build AI Agents in LangGraph 🦜🔗 Meet LangChain - the framework that's changing how developers build AI applications. Here's what makes it powerful
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@learning_intelligence_tamil
Build AI Agents in LangGraph 🦜🔗 Meet LangChain — the framework that’s changing how developers build AI applications. Here’s what makes it powerful: → Connect to OpenAI, Anthropic, Google & more → Build smart agents that use tools & APIs → Add memory so your AI remembers context → Create multi-agent workflows with LangGraph → Debug everything with LangSmith Whether you’re building chatbots, automating tasks, or creating RAG systems — LangChain makes it simple. The future of AI development is modular, flexible, and open-source. 🚀 ⚠️ Note: SmolAgents (not “Small Agents”) is also mentioned — it’s an experimental Framework great for quick prototyping Save this for later ✅ Tag someone learning AI 👇 📝 Note: Animations provided are for easy understanding and educational purposes only. . . . 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: LangChain, AI Agents, LLM Framework, LangGraph, LangSmith, AI Development, Machine Learning, OpenAI, Anthropic, RAG, Retrieval Augmented Generation, Python AI, AI Automation, Multi Agent Systems, AI Tools #LangChain #AIAgents #MachineLearning #ArtificialIntelligence #LearnAI
#Langgraph Agents Reel by @askdatadawn (verified account) - Build your first AI agent in Langchain! In this walk-through, we are building a simple currency conversion agent using Langchain. I explain → how
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@askdatadawn
Build your first AI agent in Langchain! In this walk-through, we are building a simple currency conversion agent using Langchain. I explain → how the @tool decorator works → how to use create_tool_calling_agent function → how to add simple memory to the agent And of course, we look through the agent results together. Comment MONEY and I’ll send you a link to my GitHub repo (for free!) #langchain #datascience #aiengineering
#Langgraph Agents Reel by @datastoryteller.ai - Langgraph is how you control AI flow when projects get complex! #langgraph #genai #agenticai #ai #datascience
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@datastoryteller.ai
Langgraph is how you control AI flow when projects get complex! #langgraph #genai #agenticai #ai #datascience
#Langgraph Agents Reel by @ducateducation (verified account) - 🚀 Want to build LLM apps? Remember these 2 frameworks: LangChain vs LangGraph 👇 LangChain = simple, linear flow ✅ Think: step-by-step, predictable
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@ducateducation
🚀 Want to build LLM apps? Remember these 2 frameworks: LangChain vs LangGraph 👇 LangChain = simple, linear flow ✅ Think: step-by-step, predictable pipelines using chains + tools + memory. LangGraph = complex, agentic flow ⚡ Think: decisions + loops + state tracking (multi-step agents that behave like real workflows). 🧠 Easiest way to remember: LangChain = straight road 🛣️ LangGraph = city roads 🏙️ (turns, loops, signals) So tell us👇 Your next AI project is Simple or Advanced? Comment “CHAIN” or “GRAPH” 🔥 📌 Save this for later + share with a friend building AI. Follow @ducateducation for more AI learning. #LangChain #LangGraph #LLM GenerativeAI, AIEngineering, AIAgents, PromptEngineering, MachineLearning #Python #DucatSchoolOfAI AIStudents TechCareers
#Langgraph Agents Reel by @idigitialrita - LangChain is what allows AI to interact with the real world. It connects language models with tools, data sources, and actions - turning AI into a wo
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@idigitialrita
LangChain is what allows AI to interact with the real world. It connects language models with tools, data sources, and actions — turning AI into a working system. This is a core building block of AI agents. Day 16 / 90 of Agentic AI. Comment “TOOLS” if you’re starting to see the bigger picture 🔗🚀
#Langgraph Agents Reel by @ai.with.shrey - How AI Agents Actually Work 🤖 (LangChain Explained) Want to build an AI that does more than just chat? You need LangChain Agents. 🤖 In this 60-sec
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@ai.with.shrey
How AI Agents Actually Work 🤖 (LangChain Explained) Want to build an AI that does more than just chat? You need LangChain Agents. 🤖 In this 60-second breakdown, I explain the exact architecture behind intelligent agents that can Think, Plan, and Act. The 6-Step Workflow: 1️⃣ User Request: Triggering the agent. 2️⃣ Planning: The LLM reasons and creates a plan. 3️⃣ Search: Retrieving info from PDFs or Vectors. 4️⃣ Action: Calling APIs for real-time data. 5️⃣ Memory: Storing context for later. 6️⃣ Response: Delivering the final smart answer. 🔥 UPCOMING SERIES: I am creating a full 7-Part Video Series teaching you how to build this exact AI Agent from scratch using Python. Subscribe now so you don't miss the first episode! 🚀 #langchain #aiagents #python #llm #openai #coding #artificialintelligence #automation #techeducation
#Langgraph Agents Reel by @chaostoai - Day 11/15 of langchain series 🚀 LLMs don't remember anything by default. Memory in LangChain allows ai agents to: • remember past conversations •
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@chaostoai
Day 11/15 of langchain series 🚀 LLMs don’t remember anything by default. Memory in LangChain allows ai agents to: • remember past conversations • maintain context • improve response quality • enable conversational intelligence Types of memory include: buffer memory summary memory vector store memory Memory is what makes ai agents feel intelligent and context-aware. Save this for your langchain journey. #langchain #aiagents #generativeai #rag #aiengineering

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