102.8K
AIPeter how do I choose a framework?
#ai #aiengineer #aiagents #frameworks #langchain #langgraph #mastra #agno #autogen #crewai
@aiengineerpeter
#Langgraph
世界中の人々によるLanggraphに関する9.6K件のリール動画を視聴。
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22トレンドリール
(12)
9.7K
SOPhD is taking quite a lot from me lately😆, but finally managed some time to work on my side projects.
From this project, I learned ⬇️
Key LangGraph Concepts:
🍂State: Shared data that flows through the workflow
🍂Nodes: Functions that transform the state
🍂Edges: Connections defining the execution order
🍂Graph: The compiled workflow that orchestrates everything
It was a lot of fun working on this and really nice to do something totally different from my PhD 😆
#machinelearning #ai #aiagents #langgraph #codewithme #computerscience #coding #tech
@sopi.iscoding
12.4K
VAHello learners,
Before we dive into the LangGraph series, let’s start with the basics:
What’s the difference between LangChain and LangGraph?
Think of LangChain like following a cooking recipe. You go step by step: gather ingredients → follow instructions → get the final dish. It’s simple, predictable, and works best when the flow is fixed. In AI terms, LangChain lets you connect prompts, models, and tools in a straight line — ideal for quick chatbots, RAG (retrieval-augmented generation), and simple workflows.
Now, think of LangGraph like using Google Maps. You have multiple possible routes, and you can choose based on traffic, time, or detours. It’s flexible, dynamic, and lets you make decisions along the way. In AI, LangGraph allows agents to loop, branch, and adapt their actions — perfect for complex decision-making systems or AI assistants that need more freedom.
To put it simply:
• LangChain = Fixed, step-by-step flows (like a recipe).
• LangGraph = Dynamic, flexible paths (like a map).
Both are useful, but for building advanced agentic AI systems, LangGraph provides more power and flexibility.
#langchain #langgraph #agenticai #generativeai #learnai
[langchain, langgraph, agentic ai, generative ai, ai agents, llm, rag]
@varshavlog.vv
32.5K
AKYour LLM worked in the demo.
Then it hit real users… and started hallucinating, looping, or losing context.
That’s not an “LLM problem”.
That’s missing structure.
LangChain is how you connect an LLM to tools, memory, and data.
Prompts, retrievers, APIs, databases — all wired together.
LangGraph is how you control what happens next.
States. Transitions. Loops. Branches.
No more “hope the agent behaves”.
Chain = steps in a line.
Graph = decisions with rules.
If you’re new:
LangChain helps you build faster.
If you’re shipping to prod:
LangGraph helps you keep things sane.
Same rule as backend systems:
Structure beats prompts. Always.
LLMs don’t need more clever prompts.
They need better control flow.
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{langchain, langgraph, llm, genai, ai agents, prompt engineering, retrieval augmented generation, agent orchestration, system design, ai engineering}
#genai #llm #aiagents #langchain #langgraph
@akashcode.ai
71.6K
__Here are some LangGraph project ideas you can actually build 👇
1. Build an Intelligent SQL Query Agent 🧠
Create an AI agent that converts natural language questions into SQL queries and retrieves accurate results from your database using LangGraph.
Skills: LangGraph · SQL · LLMs · Python · Prompt Engineering
2. Build a Smart Social Media Agent 📱
Develop an AI agent that automates social media workflows — from content creation and caption writing to post scheduling and trend analysis.
Skills: LangGraph · APIs · NLP · Automation · Python
3. Build a Web Research & Article Generation Agent 🌐
Design an agent that explores the web, extracts key insights, and generates well-structured articles with proper references automatically.
Skills: LangGraph · Web Scraping · LLMs · Summarization · Python
4. Build an AI Prompt Generation Agent 💡
Develop an agent that generates optimized prompts for different AI models like ChatGPT or DALL·E based on the user’s intent.
Skills: LangGraph · NLP · Prompt Optimization · Python
5. Build a Human-in-the-Loop AI Agent 👩💻
Create a workflow where the AI seeks human feedback or approval at critical steps, blending automation with human intelligence for better accuracy.
Skills: LangGraph · Workflow Automation · Human Feedback Loops · LLMs · Python
✨ Start small, learn fast, and let your AI agents do the work!
Which one will you build first? 🚀
#datascience #langgraph #genai #aiagents #aiprojects #langchain #openai #machinelearning #fyp #trending #motivation #ai #techreels #codingreels #learnai #datascientist #learnmore #aiinsights #aitools #llm #song #trending #explore
@__nextdoor.aigirl
191.5K
DABecoming an AI Engineer in 2026 isn’t about learning everything…� It’s about learning the right thing in the right order.
Save this. This will guide your entire 2025. 💚⚡
📊 Math & Statistics
Resource: Khan Academy – Statistics & Probability
🐍 Python
Resource: freeCodeCamp – Python Full Course
🤖 Classical ML
Resource: Andrew Ng — Machine Learning (Coursera)
🚀 Advanced ML
Resource: StatQuest — Advanced ML Playlist
🧠 Deep Learning
Resource: DeepLearning.AI — Deep Learning Specialization
🗣️ NLP
Resource: Hugging Face — NLP Course
⚡ Transformers
Resource: Hugging Face — Transformers Course
🎯 Finetuning
Resource: Hugging Face — Fine-tuning Tutorials (LoRA, QLoRA)
🔗 LangChain
Resource: LangChain Official Documentation — “LCEL + Tools” section
🕸️ LangGraph
Resource: LangGraph Docs — Agents & Workflows
🛠️ MCPs (Model Context Protocol)
Resource: OpenAI MCP Docs — Getting Started
🟩 FastAPI
Resource: FastAPI Docs — The Official Tutorial
💾 Save this for your 2026 roadmap�
🔥 Share with someone starting their AI journey
📲 Follow @datasciencebrain for Daily Notes 📝, Tips ⚙️ and Interview QA🏆
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[datascienceroadmap, airoles, mlengineerpath, datasciencejobs, analyticscareer, datatechskills, mlopsengineer, dataengineerskills, aiindustrytrends, techlearningguide]
#ai #aiengineer #machinelearning #deeplearning #llm #huggingface #langchain #langgraph #fastapi #datascience #python #genai #mlroadmap #datasciencebrain
@datasciencebrain
99.6K
JGTraditional RAG is a straight pipeline:
retrieve → generate → done
If retrieval is wrong, the answer is wrong.
Its literally the way you design it than adding any rocket science.
To make RAG self-correcting, we use Agentic RAG — a feedback loop.👇
⸻
1️⃣ What Agentic RAG Actually Does
Instead of trusting the first retrieved chunks, it:
➡ retrieves
➡ checks relevance
➡ validates the answer
➡ retries if needed
It behaves like a system that knows when it’s wrong.
⸻
2️⃣ The 3 Agents You Need (Multi-Agent System)
🟦 Router Agent — “can i answer it well?”
If the router agent itself can answer → may be skip retrieval.
Else → retrieve from vector DB.
⸻
🟩 Grader Agent — “Are these chunks relevant?”
If good → go generate answer.
If bad → rewrite the query → retrieve again.
Prevents garbage-in → garbage-out.
⸻
🟥 Validator Agent — “Does the answer match the sources?”
If yes → return to user.
If not → retry using a different reasoning path.
This catches hallucinations before the user ever sees them.
⸻
3️⃣ Why This Beats Traditional RAG
Plain RAG = one-shot retrieval.
Agentic RAG = loop → grade → validate → retry → succeed.
More reliable.
More deterministic.
Handles long + messy documents better.
⸻
4️⃣ How YOU Implement It (Simple Stack)
Use:
• LangGraph → multi-agent control flow (routing, loops, validation)
• Milvus (or any production grade vector DB) → scalable chunk retrieval
• Any LLM → query rewriting + reasoning + validation
This converts RAG into a self-correcting system, not a one-shot guess.
⸻
⭐ TL;DR
Traditional RAG retrieves once.
Agentic RAG retrieves, checks, validates, and retries until correct.
This approach definitely improves answer quality compared to plain RAG.
⸻
🔖 Tags
#AgenticRAG #RAG #LLM #GenAI #LangGraph #Milvus #MachineLearning #aiengineering #TechReels #ai #datascience #ml #trend #engineering #basics #llm #prompt #rag
@jganesh.ai
392
FRTOP AGENTIC AI FRAMEWORK
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#AgenticAI
#AIFrameworks
#LLMAgents
#AutonomousAI
#MultiAgentSystems
#LangChain
#CrewAI
#AutoGen
#LangGraph
#SemanticKernel
#AIAutomation
#TechTrends
#AIEngineering
#FutureofWork
#GenerativeAI
@freshgrad.com.official
28.1K
PRDay 15 of llms
Human in Loop - Langgraph
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #aiagents #ai #langgraph
@priyal.py
22.2K
DI⚡ Stop wasting time (and $$$) picking the wrong AI agent framework.
Each one is built for a different job:
✅ LangGraph → full control & reliability
✅ Google AI / Azure Foundry → enterprise multimodel power
✅ CrewAI / AG2 → teamwork between agents
✅ Agno → build fast
✅ n8n → no-code drag & drop
The right framework = agents that actually deliver 🚀
👉 Share this with a friend & follow for more AI builder guides.
#AIagents #LangGraph #CrewAI #Agno #aitools
@diptii.ai
70.3K
ENLangChain vs LangGraph — what’s the difference?
Both help you build LLM applications, but they solve different problems.
LangChain
Designed for building linear AI workflows.
Example flow:
User query → retrieve documents → LLM generates answer.
It’s great for:
• RAG pipelines
• Tool calling
• Prompt chains
• Simple AI applications
LangGraph
Built for stateful and multi-step AI agents.
Instead of a simple chain, it creates a graph of nodes and decisions where the AI can loop, retry, or choose different paths.
It’s great for:
• Agent workflows
• Multi-agent systems
• Complex reasoning loops
• Long-running AI processes
Simple way to think about it:
LangChain → linear pipelines
LangGraph → agent decision graphs
Follow @engineerbeatsai for more AI insights.
#AI #LangChain #LangGraph #LLM #AgenticAI GenAI AIEngineering PromptEngineering
@engineerbeatsai
5.7K
K2🤯 Think AI agents are just like chatbots? Not even close.
Agentic AI = AI that thinks, plans & acts — not just responds.
Here’s the breakdown 👇
🔹 Step 1: Input Sources → Collects from queries, APIs, web, knowledge bases.
🔹 Step 2: AI Processing → Understands context, retrieves memory, plans next steps.
🔹 Step 3: Action Layer → Executes tasks, makes decisions, collaborates, handles errors.
🔹 Step 4: Output → Delivers intelligent, context-aware results.
✨ That’s the magic: AI that doesn’t just answer… it works for you.
Comment ‘AI’ for a complete roadmap! 🗺️
📌 Save this post to finally understand how Agentic AI really functions.
.
#AgenticAI #GenerativeAI #AIExplained #ArtificialIntelligence #LangChain #LangGraph #MachineLearning #AIJobs #AutonomousAI
@k21academy
✨ #Langgraph発見ガイド
Instagramには#Langgraphの下に10K件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。
ログインせずに最新の#Langgraphコンテンツを発見しましょう。このタグの下で最も印象的なリール、特に@datasciencebrain, @aiengineerpeter and @jganesh.aiからのものは、大きな注目を集めています。
#Langgraphで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。
人気カテゴリー
📹 ビデオトレンド: 最新のReelsとバイラル動画を発見
📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索
🌟 注目のクリエイター: @datasciencebrain, @aiengineerpeter, @jganesh.aiなどがコミュニティをリード
#Langgraphについてのよくある質問
Pictameを使用すれば、Instagramにログインせずに#Langgraphのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。
パフォーマンス分析
12リールの分析
💡 トップ投稿は平均116.4K回の再生（平均の2.2倍）
週3-5回、活動時間に定期的に投稿
コンテンツ作成のヒントと戦略
💡 トップコンテンツは10K以上再生回数を獲得 - 最初の3秒に集中
✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長860文字
📹 #Langgraphには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用
✨ 多くの認証済みクリエイターが活動中（25%） - コンテンツスタイルを研究
#Langgraph に関連する人気検索
🎬動画愛好家向け
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Langgraphを探索#langgraph agent orchestration framework#langgraph aws#langgraph state machine langchain#langgraph edge programming basics#langgraph edge tutorials#n8n vs langgraph#workday langgraph integration#langgraph vs langchain