#Langgraph Ai Agent Framework

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#Langgraph Ai Agent Framework Reel by @akashcodeofficial (verified account) - LangGraph in 55 seconds - here's the full picture ๐Ÿ‘‡ You saw the food delivery example. Let's go one level deeper. LangGraph didn't replace LangChai
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@akashcodeofficial
LangGraph in 55 seconds โ€” hereโ€™s the full picture ๐Ÿ‘‡ You saw the food delivery example. Letโ€™s go one level deeper. LangGraph didn't replace LangChain. LangChain agents are actually built on top of LangGraph now. โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” ๐—ฆ๐˜๐—ฎ๐˜๐—ฒ โ€” your agentโ€™s shared notepad ๐Ÿง  Every node reads from it. Every node writes back to it. Thatโ€™s how everything stays connected. โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” ๐—ก๐—ผ๐—ฑ๐—ฒ๐˜€ โ€” just functions โš™๏ธ They can: โ†’ Call LLMs (OpenAI, Claude, Gemini) โ†’ Hit APIs (backend, Stripe, Razorpay) โ†’ Query DB โ†’ Use MCP tools (Gmail, Notion, GitHub) โ†’ Run Python logic One node = one job. graph.add_node(โ€œcheck_orderโ€, check_order) โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” ๐—˜๐—ฑ๐—ด๐—ฒ๐˜€ โ€” the flow ๐Ÿ”€ Normal โ†’ always next graph.add_edge(โ€œcheck_orderโ€, โ€œwhy_lateโ€) Conditional โ†’ decides via state graph.add_conditional_edges( โ€œwhy_lateโ€, decide_path ) Routing: Restaurant โ†’ offer_refund Rider โ†’ ping_rider Retries โ†’ escalate Just an if-condition deciding the path. โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” ๐—ค๐˜‚๐—ถ๐—ฐ๐—ธ ๐—ณ๐—น๐—ผ๐˜„: from langgraph.graph import StateGraph graph = StateGraph(State) graph.add_node(โ€œcheck_orderโ€, check_order) graph.add_node(โ€œoffer_refundโ€, offer_refund) graph.add_node(โ€œping_riderโ€, ping_rider) graph.add_node(โ€œrespondโ€, respond) graph.add_edge(โ€œcheck_orderโ€, โ€œwhy_lateโ€) graph.add_conditional_edges(โ€œwhy_lateโ€, decide_path) graph.add_edge(โ€œoffer_refundโ€, โ€œfixed_checkโ€) graph.add_edge(โ€œping_riderโ€, โ€œfixed_checkโ€) graph.add_conditional_edges(โ€œfixed_checkโ€, retry_or_respond) graph.set_entry_point(โ€œcheck_orderโ€) agent = graph.compile() State + Nodes + Edges โ†’ Agent ready. โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” Next: LangChain vs LangGraph When to use what ๐Ÿ‘€ Follow for part 2 โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” #langgraph #aiagents #systemdesign #llmengineering #backenddev langgraph tutorial, langgraph python, ai agent framework, langgraph nodes edges, what is langgraph, mcp tools langgraph, langchain vs langgraph, agentic ai, openai langgraph, build ai agents python, stateful ai agents, conditional edges langgraph, langgraph stategraph, ai agent for beginners, langgraph explained simply
#Langgraph Ai Agent Framework Reel by @akashcode.ai (verified account) - LangGraph in 55 seconds - here's the full picture ๐Ÿ‘‡ You saw the food delivery example. Now let's go one level deeper. LangGraph didn't replace Lang
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@akashcode.ai
LangGraph in 55 seconds โ€” hereโ€™s the full picture ๐Ÿ‘‡ You saw the food delivery example. Now letโ€™s go one level deeper. LangGraph didnโ€™t replace LangChain. LangChain agents are now built on top of LangGraph. โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” State โ†’ your agentโ€™s shared memory ๐Ÿง  Every step reads from it. Every step writes back to it. Thatโ€™s how the agent remembers what already happened. โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” Nodes โ†’ just functions โš™๏ธ Each node does one job call an LLM hit an API query a database use tools like Gmail or Notion run your own logic one node = one task โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” Edges โ†’ the flow ๐Ÿ”€ normal โ†’ always go next conditional โ†’ decide based on state restaurant issue โ†’ offer refund rider issue โ†’ ping rider retry โ†’ try again or escalate itโ€™s just logic deciding the path โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” how it all connects state + nodes + edges = a working AI agent โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” this is how modern AI agents are built if you understand this you understand agent systems save this follow for part 2 โ†’ langchain vs langgraph โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” #aiagents #langgraph #llm #genai #machinelearning systemdesign
#Langgraph Ai Agent Framework Reel by @ayushukla.techno - Save this for later!โ–พ {agentic ai, LLM, agent, Ai, ai Rag, Ai agents, langGraph, LangChain, Ai, machine learning, Artificial Intelligence} #ai #llm
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@ayushukla.techno
Save this for later!โ–พ {agentic ai, LLM, agent, Ai, ai Rag, Ai agents, langGraph, LangChain, Ai, machine learning, Artificial Intelligence} #ai #llm #agenticai #machinelearning #ayushukla
#Langgraph Ai Agent Framework Reel by @genai.revolution (verified account) - LangGraph vs LangChain vs Bee AI ๐Ÿ‘‡ The framework you choose = how powerful your AI system becomes. LangGraph โ†’ Visual workflows โ†’ Strong memory + t
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@genai.revolution
LangGraph vs LangChain vs Bee AI ๐Ÿ‘‡ The framework you choose = how powerful your AI system becomes. LangGraph โ†’ Visual workflows โ†’ Strong memory + tracking LangChain โ†’ Chain-based setup โ†’ Great for integrations Bee AI โ†’ Agent-first โ†’ Built for scale + real-time tasks ๐Ÿ‘‰ What matters most: โ€ข Control & traceability โ†’ LangGraph โ€ข Structured flows โ†’ LangChain โ€ข Scalable agents โ†’ Bee AI Soโ€ฆ if youโ€™re starting today: graphs, chains, or agents? ๐Ÿ‘€ โ€” โ™ป๏ธ Save this โž• Follow for more AI breakdowns #AI #GenAI #AIagents #LLM #TechExplained
#Langgraph Ai Agent Framework Reel by @codewithupasana - LangChain v/s LangGraph โ‰๏ธ Most people think LangChain is enough to build AI agents. That's only true for simple use cases. LangChain helps you conn
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@codewithupasana
LangChain v/s LangGraph โ‰๏ธ Most people think LangChain is enough to build AI agents. Thatโ€™s only true for simple use cases. LangChain helps you connect LLMs, tools, and workflows. But as your logic becomes complex, things start breaking down. That is where LangGraph comes in. LangChain works well for linear or loosely controlled flows. LangGraph gives you full control with state, loops, and branching. If you want to build production-level AI systems, understanding this difference is important. #hashtags #genai #langchain #langgraph #aiagents machinelearning developers softwareengineer coding artificialintelligence
#Langgraph Ai Agent Framework Reel by @parasmadan.in (verified account) - How to train AI Agents ๐Ÿ‘จโ€๐Ÿ’ป Microsoft just launched Agent Lightning and it helps us train AI Agents. #aiagents #microsoft #developer
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@parasmadan.in
How to train AI Agents ๐Ÿ‘จโ€๐Ÿ’ป Microsoft just launched Agent Lightning and it helps us train AI Agents. #aiagents #microsoft #developer
#Langgraph Ai Agent Framework Reel by @average.yash - Day 31 of my 50 days of GenAI challenge. create_agent builds agents in one line. But what happens when the LLM decides to call a tool? Where does the
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@average.yash
Day 31 of my 50 days of GenAI challenge. create_agent builds agents in one line. But what happens when the LLM decides to call a tool? Where does the loop happen? How does it know when to stop? Today we built the ReAct agent as an explicit LangGraph graph. Two nodes. One routing function. That's the whole agent. - llm_node โ€” calls Gemini with `bind_tools` to decide: use a tool or just answer - tool_node โ€” executes the tool call and wraps results in `ToolMessage` - should_continue โ€” the conditional edge that routes to tools or END We tested with a weather query โ€” the agent picked get_weather, called the API, and responded with live data. Then we added memory in one line: compile(checkpointer=InMemorySaver()). Same graph, now it remembers across turns. [langgraph tool agent, langgraph react agent, langgraph conditional edges, add_conditional_edges, bind_tools langgraph, langgraph should_continue, tool calling agent graph, langgraph toolmessage, react loop langgraph, langgraph gemini tools, agent routing function, langgraph memory checkpointer, langgraph tutorial python, ai agent graph, langgraph tool node] #GenAI #LangGraph #AIAgents #ReActAgent #LLMEngineering
#Langgraph Ai Agent Framework Reel by @jaiinfowayofficial - ๐Ÿ” Exploring the Top AI Agent Frameworks in 2025! Whether you're building custom LLM apps, orchestrating multi-agent workflows, or enhancing CRM syst
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@jaiinfowayofficial
๐Ÿ” Exploring the Top AI Agent Frameworks in 2025! Whether you're building custom LLM apps, orchestrating multi-agent workflows, or enhancing CRM systems โ€” choosing the right AI framework matters. From LangChain and LangGraph to CrewAI and Agentforce, here's a quick guide to help you match the right tool with your specific use case. ๐Ÿง  Get modular flexibility, deep CRM integration, secure enterprise automation, and more โ€” all in one visual! ๐Ÿ“Š Download the full visual breakdown and explore your AI strategy with confidence. ๐Ÿš€ Powered by Jaiinfoway ๐Ÿ”— www.jaiinfoway.com #AI #GenAI #AIagents #LangChain #LangGraph #CrewAI #PyDanticAI #Agentforce
#Langgraph Ai Agent Framework Reel by @thatcodergirlie (verified account) - Comment "blog" & I'll share the blog link & my notes with you in your DM ๐Ÿค๐Ÿป (Make sure to follow else automation won't work) Topic: LangChain v.s.
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@thatcodergirlie
Comment โ€œblogโ€ & Iโ€™ll share the blog link & my notes with you in your DM ๐Ÿค๐Ÿป (Make sure to follow else automation wonโ€™t work) Topic: LangChain v.s. LangGraph Save for your future interviews ๐Ÿ“ฉ #dsa #systemdesign #tech #coding #codinglife [dsa, system design, LangChain, LangGraph, tech]
#Langgraph Ai Agent Framework Reel by @nick_saraev (verified account) - Comment "AGENT" to get this Free AI Agent that automates all yours Tasks. Picture this. An AI agent takes over your browser. It acts just like a huma
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@nick_saraev
Comment "AGENT" to get this Free AI Agent that automates all yours Tasks. Picture this. An AI agent takes over your browser. It acts just like a human. You tell it what to do. And it gets busy. It opens tabs. Clicks buttons with care. Fills forms. Moves between pages. All to finish real tasks. Think simple jobs. Like filling job apps on LinkedIn. Or pulling data from stores online. It handles those. Now think bigger. Booking trips. Watching prices. Gathering market facts. It jumps from site to site. No sweat. I tried it last week. Told one agent to research AI tools. It scanned 20 pages in 10 minutes. Saved me hours. Scale it up. Run 50+ agents at once. Each in its own browser. Like having a team of researchers. They work fast. Turn hours into minutes. Perfect for: 1. SaaS folks grabbing leads 2. No-code builders testing flows 3. Agencies digging for content ideas The best part. It's free. No fees. No cards. It even sets itself up. Say, "Get a free API key from Google AI Studio." It opens the page. Grabs the key. Configures everything. Done. Like stealing candy from a baby. Powered by free tools like Gemini. Works for devs anywhere. Tie it to Python scripts. Build vibe-code apps. Or launch SaaS agents. No hassle. Endless scale. Zero cost. Retriever AI. Your digital workers. Ready for any task. Today. #aiagent #rtrvrai #aiautomation #googleaistudio #nicksaraev
#Langgraph Ai Agent Framework Reel by @filatov.design (verified account) - This AI Agent can literally do everything ๐Ÿค– It can build full websites and apps, write research reports, analyze data, or even book flights and mana
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@filatov.design
This AI Agent can literally do everything ๐Ÿค– It can build full websites and apps, write research reports, analyze data, or even book flights and manage your emails. All in one place. I asked it to build a web app with login and database, it planned, coded, tested, and hosted it in minutes. Then I switched tasks and asked for a research report, it browsed real websites, cited sources, and even turned it into a full presentation. And it doesnโ€™t stop there. DeepAgent connects with tools like Slack, Gmail, and Jira, so you can automate your entire workflow. โšก๏ธ Comment Agent, and Iโ€™ll DM you the link to try DeepAgent yourself.
#Langgraph Ai Agent Framework Reel by @theneildave.ai (verified account) - Stop building AI Agents until you understand this framework split. Building Agentic workflows but confused between LangGraph, CrewAI, and AutoGen? Cho
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@theneildave.ai
Stop building AI Agents until you understand this framework split. Building Agentic workflows but confused between LangGraph, CrewAI, and AutoGen? Choosing the wrong framework means wasting weeks of engineering time rebuilding from scratch. Here is the ultimate framework breakdown so you do not make the wrong choice: 1. CrewAI (The Fast Prototyper) Philosophy: Role-based agents (Writer, Analyst, Researcher). Best for: Fast content workflows and prototyping. The Catch: Less under-the-hood control and struggles with edge cases. 2. AutoGen by Microsoft (The Simulator) Philosophy: Conversation-first agents that talk like humans. Best for: Multi-agent chats and complex tool integrations. The Catch: Output can get unpredictable, making debugging a nightmare. 3. LangGraph (The Production Standard) Philosophy: Low-level control freak mode. Best for: Production-grade workflows requiring state machines, loops, and strict memory control. The Catch: A very steep learning curve and heavy engineering effort. Simple rule: Need Control? Choose LangGraph. Need Speed? Choose CrewAI. Need Conversations? Choose AutoGen. Do not follow the hype. Pick the framework that fits your architecture. Comment the word โ€œAGENTโ€ below and I will DM you my real-world architecture examples for these frameworks. [ NVIDIA, GTC2026, Jensen Huang, Artificial Intelligence, Machine Learning, MLOps, LLMs, Software Developer, Tech Careers, AI Agents, Generative AI, Coding India, AI Engineering]

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