The Langchain Dilemma: An AI Engineer’s Perspective on Production Readiness

For years, I’ve been in a love-hate relationship with Langchain. As an AI engineer, I was immediately drawn to its promise: a neat toolbox to connect LLMs, memory, and tools in minutes. For whipping up prototypes and getting MVPs off the ground, it felt like a superpower. But when it came time to move to production, that feeling faded, replaced by a nagging sense of frustration. My applications felt sluggish, debugging was a nightmare, and scaling felt like hitting a wall.

For the longest time, I thought the problem was on my side. Was my architecture flawed? Was I missing some key configuration? I spent countless hours trying to optimize, only to feel like I was running in circles. It wasn’t until I started talking with multiple AI engineers that I realized I wasn’t alone. We were all sharing the same war stories. Then, I stumbled upon a video by Swarnendu De, “Never Use Langchain in Production , Agentic AI Best Practices”, and everything clicked. The video articulated the exact issues I and so many of my peers had been quietly battling. It was the validation I needed, and it made me realize this was an issue that needed to be raised.

The Silent Struggles of a Production Langchain App

If you’ve taken a Langchain app to production, these pain points might sound painfully familiar. They’re the issues we often blame on ourselves, but as I’ve learned, they are common side effects of a framework built for prototyping, not for the rigors of production.

🐢 The Mysterious Case of Latency

In any real-world application, speed is everything. I was pulling my hair out trying to understand why my app was so slow. It turns out, Langchain’s abstractions, especially its memory components and agent executors, can add over a second of latency per API call. One fellow engineer I spoke with mentioned they cut their API latency by some more then 1 seconds just by removing Langchain’s memory wrapper, without changing a single thing about their model or infrastructure. That’s the difference between a happy user and a churned one.

🐛 Debugging in the Dark

When something breaks in a production Langchain app, good luck finding the source quickly, is it the prompt? The chain? A callback? Or something buried deep in the framework’s internals? Without clear observability, you’re essentially reverse-engineering your own stack every time there’s an issue. It’s a massive time sink and completely impractical when you’re under pressure. Frameworks like LangGraph, with their transparent execution flows, are now my go-to for anything serious.

⛓️ The Architectural Handcuffs

Langchain encourages you to build around its abstractions. While this is great for getting started, it creates a powerful architectural lock-in. Its heavy dependency graph inflates your container size, slows down deployments, and makes it incredibly painful to swap out components later. I’ve heard stories of teams spending months on rewrites just to untangle their product from Langchain. Even its own founder has called it a “toolbox,” but it’s easy to accidentally lock yourself into the whole box.

💸 The Hidden Costs of “Magic”

Context is king in AI, but excess context is a budget killer. I learned this the hard way when our monthly OpenAI bill started creeping up. Langchain’s default memory setups often store far more conversation history than necessary, leading to wasted tokens and extra API calls. After hearing about another team’s success, we built a custom, trimmed-down memory solution and saw our costs drop by nearly 30% in the first month, with the added bonus of faster responses.

The Path Forward: A Consensus is Forming

The great news is that you don’t have to choose between orchestration and performance. The consensus I’ve seen forming among experienced AI engineers is a move towards leaner, more controllable tools:

• Direct API calls for full control over prompts and responses.
• LangGraph or AutoGen for building flexible, observable workflows.
• Custom orchestration stacks using tools like FastAPI, Celery, and Redis for ultimate scalability.

How to Escape the Dilemma

Moving away from Langchain doesn’t have to be a painful, all-or-nothing rewrite. A smarter, phased approach is the way to go, a strategy that has worked for my team and others:

1. Abstract it away: First, wrap your existing Langchain code in your own functions. This makes it much easier to swap out later.
2. Replace memory and tools first: This is where you’ll find the biggest and most immediate gains in performance and cost savings.
3. Add observability: Use tools like AgentOps, LangSmith, or OpenTelemetry to finally see what’s going on under the hood and pinpoint bottlenecks.
4. Test direct LLM calls in parallel: This allows you to measure the improvements in cost and speed and validate your migration.

This lets you keep delivering new features while steadily regaining control of your architecture.

It’s a relief to know that the struggles I faced weren’t unique to me but are a shared experience in the AI engineering community. Langchain is a fantastic tool for learning and prototyping, but for production, our priorities have to change. We need stability, speed, and cost control. By having these open conversations, we can all learn to build better, more robust AI systems that are truly ready for scale.