Choosing the right 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 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. 💓

🚀 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

DigitalOcean’s GenAI Platform: Easy AI Agents for Everyone 🚀🤖 DigitalOcean just dropped its new GenAI Platform, a cool way for businesses and creators to build AI helpers without needing to be AI wizards 🧙♂️. Launched in August 2025, this tool lets you connect powerful AI models with your own data, so the AI actually knows your stuff—like a chatbot that’s read all your documents 📄💡. What’s hot? 🔥 You can plug in your data and use built-in AI models to create smart agents that help with customer support, data insights, or automations. It supports multi-agent setups, meaning bots can team up and tackle complex jobs together. Plus, it has safety guardrails to keep answers on track ✅. Some quirks ⚠️ The interface is straightforward but not fancy, so it feels a bit like building with blocks rather than a polished app. Some advanced features need a bit of learning — not quite plug-and-play for absolute novices. Should you try it? If you want to bring AI into your projects without coding headaches and love experimenting, this platform is a smart playground. But if you crave a flashy, super simple AI, you might want to hold off until it’s more polished 🎯. Basically, DigitalOcean’s GenAI Platform is a solid gateway to AI, mixing power and simplicity for curious users ready to roll their sleeves up! ✨🛠️

MIT, in the report The GenAI Divide: State of AI in Business 2025, reports that 95% of generative AI projects deliver no ROI – despite companies spending tens of billions of dollars each year. They fail because: ❌ no clear business objective, ❌ no integration with company workflows, ❌ no framework for knowledge codification, ❌ pilots never scale into production. 💡 At DevStage we take a different approach. We build Internal Developer Portals (IDPs) that: ✔️ structure organizational knowledge – APIs, services, ownership, deployments, monitoring – turning tribal knowledge into codified, reusable, discoverable assets, ✔️ offer ready-to-use actions – deployments, scaffolding, access requests exposed via the portal as AI tools, ✔️ enable reliable AI agents – combining context and tools so they act on real company data instead of hallucinations. 🖐️Developer portals are not just documentation hubs. 👉 They are the missing link that turns AI hype into practical impact - providing both knowledge and operational handles for agentic software development.

The Instructions.md Revolution: AI Building AI The future of software isn’t just AI as a coding assistant—it’s AI architecting, designing, and deploying entire applications. 1. From Code-First to Instructions-First Instead of writing code line by line, developers now define requirements, architecture, and quality rules in an instructions.md file. AI then generates the full app stack. 2. What Goes Into Instructions.md • System architecture guidelines • Code generation rules & naming conventions • Behavioral specs for edge cases • Preferred tech stack • Quality & security protocols 3. The Workflow 📝 Create instructions.md 🤖 AI processes instructions (Copilot, GPT-4, Claude) ⚡ AI generates full application 🚀 Deploy & scale with best practices built in 4. Why It Matters • 10x faster development • Consistent quality across teams • Knowledge transfer through shared instruction sets • Recursive innovation: AI building AI 5. Real-World Impact Industries are already leveraging this: • Finance: compliant trading & risk tools • Healthcare: secure diagnostic apps • E-commerce: recommendation engines in days, not months The Future Instruction-driven development creates a feedback loop: better instructions → smarter AI → better apps. The question isn’t if this will reshape software—it’s whether you’ll shape it, or watch it happen.