LangChain – Great for Prototypes, But Watch Out in Production

Complicated AI Subjects in Simple Terms Series

The Basics LangChain is a well-known python library in the AI space, built to help developers quickly prototype applications using large language models (LLMs). It streamlines development, allows for easy chaining of prompts, and supports various integrations for building sophisticated AI workflows. But while it’s handy in the early stages of a project, it’s not the tool I’d bet on when it’s time to go live in production.

Let’s Start with My Experience

Last article we talked about Romanzo, my expert technical Chat Bot that I had developed for Hyland Software’s Global Services department. During that development cycle, I also had a concurrent project that I was “skunkworking” with a few developers on our off time. I called this Hyland AI Connect (HAIC), but that name is rightfully being absorbed by the awesome R&D team for our platform solution.

"The software formally known as HAIC" is an agent workflow targeted at extrapolating, interpreting, and analyzing structured data for complex data trend requests.? I plugged it into our flagship OnBase product with really awesome results. Shortening the story a little bit, our Executive AI council has approved its use for internal purposes to enable our entire business intelligence cube to use for many internal use cases. (I will later write an article about that)

What does Langchain have to do with this?? Well, it was built with Langchain SQL Agents and other agents in that codebase… When I went to move it to a private hosted Azure LLM, I ran into a ton of problems. Also, some of the targeted use cases I wanted to use it for was not very compatible with the output we needed as an organization. ?I quickly realized that using an inflexible framework would require so much rework that I should just start from scratch. So, I did.

Why LangChain Can Be a Problem Here’s the deal: using LangChain for production code can lead to a few serious headaches. Let’s talk control, interoperability, and library stability.

Lack of Control LangChain’s biggest selling point is also its Achilles' heel. It abstracts away much of what happens under the hood, which is perfect for fast iterations and concept testing. But in a production setting, that abstraction can become a barrier. When I want to tweak a system prompt or fine-tune a response strategy, I don’t need the framework holding my hand. I need complete control, and LangChain isn’t built to deliver that precision.

Interoperability Challenges LangChain also shows its cracks when you try to fit it into a larger, more complex ecosystem. Let’s say you’re building an AI solution that has to seamlessly interact with other systems – maybe a mix of databases, REST APIs, and custom-built microservices. LangChain’s architecture can make that integration less flexible, sometimes forcing workarounds that can bloat your codebase or, worse, introduce weak spots. Production code should be agile and adaptable; LangChain doesn’t always play nice with others.

Library Maintenance (or Lack Thereof) Here’s a kicker not many talk about: the state of the libraries LangChain depends on. Some of these integrations move to community-maintained versions or, worse, fall by the wayside. A lack of consistent updates and oversight can turn what was a promising framework into a ticking time bomb in your tech stack. For a prototype? Fine. But for production code? That’s a hard pass in my book.

To Sum It Up LangChain is great for experimenting, prototyping, and proving concepts. It’s the AI tinkerer’s playground. It has a ton of "no thought" connectors that allows you to plug and play into various applications. But when it comes time to roll out a production-level solution, the trade-offs become clearer:

• Lack of Control: Limits the fine-tuning that production needs.
• Reduced Interoperability: Not as flexible in diverse tech environments.
• Library Support: Risks from outdated or community-only supported libraries.

If you are in the RFP process, ask your potential vendors if they are using LangChain. If they are, I would seriously consider how fully developed their solution offerings are. In my experience I quickly found that this was a non-scalable solution. Platform companies had better have discovered that themselves.

Obligatory Counterpoint Now, some folks will argue that LangChain speeds up initial builds and can still be part of a production pipeline with enough guardrails in place. It has a ton of prebuilt connectors to AI Agent tools. Sure, but to me, those guardrails and tools come with too many caveats. If you want true long-term stability, you’re better off moving beyond LangChain when it counts.

Use LangChain as a prototype playground, not as the foundation of your production masterpiece

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