LangChain marketed itself as the framework to “build LLM apps”.
And for a while, everyone used it.

Then experienced engineers started to… not.

Here’s why.

✨ The Good Part (Conceptually)

LangChain introduced: - chains
- agents
- tools
- retrieval
- memory
- document loaders
- vector DB integrations

It made prototyping easier.

🧨 The Problem

LangChain became: - bloated
- slow
- inconsistent
- breaking changes everywhere
- spaghetti abstraction
- magical boxes hiding simple logic
- too many layers between you and your own code

The dev community basically said:

“Just let me use the API and my own functions like a normal human.”

🧠 When LangChain STILL makes sense

• one-off prototypes
• rapid demos
• hackathon apps
• teaching AI fundamentals
• simple document Q&A bots

🚫 When NOT to use it

• production apps
• agentic systems
• performance-critical code
• anything needing control
• anything needing reliability
• your own GhostFrog-style pipeline

If you need precision, LangChain gets in your way.

🔧 Better Alternatives

• LlamaIndex (clean RAG workflows)
• FastAPI / Flask (your own routing)
• Custom agent loops (what Bob + Chad use)
• OpenAI function calling
• Local tools-based execution layers

LangChain taught the world the patterns.
But for building?
Code is cleaner.