Choosing the right Agentic AI framework: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n compared

🚀 Agentic AI is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 LangChain The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 AutogenAI (Microsoft) Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 CrewAI Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 Make Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 n8n Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 Bottom line: ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

🚀 Agentic AI is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 LangChain The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 AutogenAI (Microsoft) Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 CrewAI Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 Make Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 n8n Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 Bottom line: ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n

🚀 *Agentic AI* is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI (Microsoft)* Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 *Make* Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 *Bottom line:* ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

🚀 *Agentic AI* is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI (Microsoft)* Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 *Make* Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 *Bottom line:* ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

🚀 *Agentic AI* is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI (Microsoft)* Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 *Make* Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 *Bottom line:* ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

🚀 *Agentic AI* is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI (Microsoft)* Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 *Make* Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 *Bottom line:* ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make. com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

🚀 Agentic AI is exploding, but which framework should you bet on? Credits to Eduardo Ordax. Follow them for valuable insights. Original post below: ====== 🚀 Agentic AI is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 LangChain The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 AutogenAI (Microsoft) Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 CrewAI Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 Make Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 n8n Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 Bottom line: ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all! ====== 💼 Thinking about quitting your job? It’s a tough decision, right? 🤔 No worries — we’ve built a free tool to help you get clarity on one of life’s hardest choices: whether to stay or leave your job 🚪 Join the waitlist today and be the first to try it out, completely free 🚀 Click here now 👉 https://lnkd.in/gkAnbHvv

*Agentic AI is growing fast, but which framework is the best choice?* If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. *Here’s how to make sense of the chaos:* 👇🏻 🦜🔄 *𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵* Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI* (Microsoft) Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 Make Visual, no-code automation for business users. Connect AI to CRMs, reports, and SaaS tools without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. Bottom line: ▪️Want enterprise complexity? -> LangGraph ▪️Need fast AI app prototyping? -> LangChain ▪️Building collaboratively? -> Autogen or CrewAI ▪️Prefer drag-and-drop ? -> Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. That's set 🙌🏻 Agentic AI is not one-size-fits-all! ✨ If you wanna learn more, keep doing React. Share this post to help others in your networks. 💓

𝑨𝑰 𝑨𝒈𝒆𝒏𝒕𝒔 𝒂𝒓𝒆𝒏’𝒕 𝒘𝒉𝒂𝒕 𝒚𝒐𝒖 𝒕𝒉𝒊𝒏𝒌 𝒕𝒉𝒆𝒚 𝒂𝒓𝒆. They’re way bigger than just “chatbots with plugins.” Here’s the blueprint nobody’s telling you about. If I were learning AI Agent Architecture from scratch, these are the concepts I’d master first 👇 📌 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭? An AI Agent perceives, reasons, and acts often without constant human supervision. It’s a system designed to solve problems autonomously. 📌 𝐂𝐨𝐫𝐞 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐁𝐥𝐨𝐜𝐤𝐬 1️⃣ 𝘐𝘯𝘱𝘶𝘵 𝘓𝘢𝘺𝘦𝘳 - Collects data from APIs, live streams, or interaction logs. Think of it as the “senses” of your AI. 2️⃣ 𝘚𝘵𝘰𝘳𝘢𝘨𝘦 𝘓𝘢𝘺𝘦𝘳 - Vector stores, knowledge graphs, and repositories store context, memory, and reusable insights. This is where your AI remembers. 3️⃣ 𝘖𝘳𝘤𝘩𝘦𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯 𝘓𝘢𝘺𝘦𝘳 - The brain of the system. It plans, reflects, learns, and coordinates multiple models to get the job done. 4️⃣ 𝘖𝘶𝘵𝘱𝘶𝘵 𝘓𝘢𝘺𝘦𝘳 - Delivers tailored results and knowledge updates back to the user or other systems. 5️⃣ 𝘚𝘦𝘳𝘷𝘪𝘤𝘦 𝘓𝘢𝘺𝘦𝘳 - Ensures seamless delivery and intelligent recommendations across multiple platforms. 📌 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 (𝐒𝐭𝐞𝐩-𝐛𝐲-𝐒𝐭𝐞𝐩) ⮕ Input → Collected via APIs/logs/data streams ⮕ Storage → Provides context + memory ⮕ Orchestration → Plans & coordinates models ⮕ Output → Optimizes results for the task ⮕ Service → Handles delivery + recommendations 📌 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 ✅ Modular & scalable design ✅ Supports multi-agent collaboration ✅ Enables self-learning over time ✅ A step closer to true autonomous AI systems 💡 Think of it as a 𝒅𝒊𝒈𝒊𝒕𝒂𝒍 𝒏𝒆𝒓𝒗𝒐𝒖𝒔 𝒔𝒚𝒔𝒕𝒆𝒎: Input = Senses | Storage = Memory | Orchestration = Brain | Output = Actions | Service = Communication Follow PROITBRIDGE for more AI breakdowns like this!

🚀 Agentic AI is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 LangChain The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 AutogenAI (Microsoft) Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 CrewAI Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 Make Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 n8n Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 Bottom line: ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all! #ai #genai #agents #agentic #framework