Hey everyone, I’m just getting started with AI development and need some guidance. I keep hearing about Langchain when people talk about building AI agents, but I’m not sure if it’s really necessary or if there are other good options out there.
What frameworks do you actually use when creating agents? I’m curious about the pros and cons of different approaches. Right now I’m working on a chatbot project and trying to figure out the best tech stack to use.
Any advice would be really helpful. What has worked well for you in your projects? Are there any pitfalls I should watch out for when choosing between different agent frameworks?
Been there, done that. Built my first agent with Langchain and spent way more time fighting the framework than actually solving problems.
Here’s the thing - most chatbots don’t need heavy agent frameworks. You just need something that handles API calls, data transformations, basic logic, and maybe some database stuff.
I learned this on a customer service bot project. Started with Langchain, got completely buried in abstractions. Then it hit me - I just needed to chain simple operations together without writing a ton of boilerplate.
That’s why I switched to automation platforms. Instead of wrestling with framework limitations, I drag and drop my workflow. Connect the AI model to whatever services the bot needs. Add some basic logic flows.
You can prototype in minutes instead of days. No complex setup, no dependency hell, no debugging weird framework behavior.
For your chatbot, just map out what it actually does step by step. Build those steps as an automated workflow. Way easier to maintain and modify without touching code.
Latenode makes this approach dead simple. Visual workflow building with AI integration built right in: https://latenode.com
After two years building production agents, LangChain turns into a maintenance nightmare once you scale past basic prototypes. The abstraction layers break when you need custom behavior, and debugging becomes a pain.
I’ve had way better luck with a hybrid approach - direct API calls to GPT-4 or Claude plus custom state management. You get full control over conversation flow and can easily add memory persistence and context switching.
For your chatbot, start simple with the OpenAI SDK and build your own orchestration logic. You can always add fancy frameworks later, but starting lean helps you figure out what you actually need. The biggest mistake I see is over-engineering from day one when you don’t even know your real requirements yet.
Langchain gets tons of hype but it’s overkill for most projects. I’ve built plenty of AI agents without it.
The real game changer? Proper workflow automation. Everyone obsesses over AI frameworks and misses the bigger picture - how do you orchestrate tasks, API calls, and data processing?
I’ve watched teams waste weeks fighting Langchain’s complexity when they could’ve shipped something working way faster. The key is connecting AI models to automated workflows that do the heavy lifting.
For your chatbot, what does it actually need to do? Call APIs, process responses, store data, trigger actions. That’s where workflow automation shines.
Don’t get locked into one framework. Use a visual automation platform that connects AI models with your other tools seamlessly. You get more flexibility and iterate faster.
Check out Latenode - it handles workflow orchestration while you focus on making your agent useful: https://latenode.com
i totally get what u mean! sometimes less is more, right? openai’s api can do wonders without all the extra complexity. haven’t heard too much about crewai, but if it’s gaining traction, might be worth checking out. good luck with ur chatbot!
Honestly, LangChain’s overkill for simple projects. I switched to AutoGen recently - much cleaner for multi-agent work and the docs don’t suck. But if you’re just building a basic chatbot, just use OpenAI’s SDK directly and skip the hassle.
The choice depends on your use case and tech constraints. I’ve used several frameworks over three years - LangChain’s great for prototypes but becomes a pain when you need precise control over tokens and response formatting. Most devs miss this: proper prompt engineering and context management matter more than the framework. I’ve watched sophisticated chatbots crash because they can’t handle conversation state properly. For production, I build lightweight orchestration layers using provider SDKs directly. Skip the bloated frameworks. One exception: Microsoft’s Semantic Kernel if you’re in .NET. Better separation of concerns than LangChain and cleaner plugin architecture. Biggest mistake? Picking frameworks by popularity instead of project needs. Figure out your memory management, integrations, and scaling requirements first, then choose your tools.