I’m in the process of creating a robust AI application meant for production use. I’m torn between two frameworks, Langchain and Pydantic AI. I find Pydantic appealing due to its type-safe APIs, which seem more trustworthy. Meanwhile, I’m concerned that Langchain might introduce excessive complexity with its many features. I would appreciate any insights or experiences from others who have used these frameworks in production. Please share your thoughts!
Both frameworks work fine, but you’re thinking too small.
Production AI apps need way more than a good framework. You need solid data pipelines, model switching, proper error handling, monitoring, and scaling that won’t break.
I’ve hit this same wall multiple times. Started with one framework, hit production requirements, then spent weeks building infrastructure around it. Database connections, API integrations, scheduling, retries, logging - the framework’s just one piece.
Smart move? Build your AI logic into automated workflows from day one. Test both Langchain and Pydantic approaches in parallel, see what works for your use case, and switch without rebuilding everything.
We did this for a recommendation system last year. Started with one approach, A/B tested another, ended up using both for different pipeline parts. The automation platform handled orchestration, monitoring, and scaling while we focused on AI logic.
Latenode makes this straightforward. Build AI workflows with production infrastructure baked in. Test different frameworks without committing. Get monitoring, scaling, and reliability without building it yourself.
Been down this exact path. Both frameworks work, but you’re approaching this wrong.
Don’t lock into one framework - build your production AI app with proper automation from day one. I’ve watched teams struggle with framework migrations, dependency hell, and scaling nightmares because they picked one tool and built everything around it.
You need a flexible automation layer that handles different AI frameworks, switches between them when needed, and manages production stuff like monitoring, scaling, and error handling.
Built a similar system last year where we tested multiple AI approaches in production. Started with one framework, switched to another, ended up using both for different use cases. The automation layer saved us months of refactoring.
Latenode handles this perfectly. Build workflows that work with any AI framework, change them without touching your core app, get all the production features built in. Way more flexible than committing to just Langchain or Pydantic.
depends on ur team size and timeline. if u’ve got experience with complex systems, langchain is the way. but for smaller teams, pydantic ai is way less of a headache. seen too many projects get bogged down with langchain’s abstractions when they just needed something simple.
totally feel u! langchain can be a pain and just overly compicated. pydantic is much more user-friendly and solid. if ur after something simpler, pydantic is def the way to go, trust me!
I’ve shipped multiple production AI systems, and you’re absolutely right about Langchain’s complexity. It tries to solve every possible use case, creating unnecessary maintenance headaches early on. When things break in production, those abstraction layers make debugging a total nightmare. Pydantic AI’s type safety is a game-changer for production stability. You’ll catch bugs during development instead of hearing about them from angry users. The learning curve is way easier, and you’ll actually understand your own code months later. That said, think about what you actually need. If you’re building multi-agent workflows or complex chain orchestration, Langchain’s kitchen-sink approach might save you development time despite the complexity. But for most production apps, starting simple with Pydantic and building exactly what you need creates more reliable systems.
I’ve used Langchain in several production apps lately. Yeah, it’s complex, but totally manageable if you stick to what you actually need. Langchain’s great for rapid prototyping, especially when you’re dealing with complex workflows that hit multiple models and handle lots of data. The flexibility is huge when you want to swap between different LLM providers. Pydantic AI gives you better type safety and fewer runtime surprises, but you’ll end up rebuilding stuff Langchain already handles. My take: start with a basic Langchain setup that covers your core needs. Don’t try to use every feature from day one. Keep your codebase clean and add complexity only when you have to.