Moving an LLM application from a Jupyter Notebook to a scalable production environment requires more than just wrapping code in Docker. This guide covers architecture, observability, caching, and evaluation strategies for robust LangChain deployments.

AI AgentsLangChain vs. Custom Code: When to Build Your Own LLM Orchestration Layer

Avnish Yadav – February 22, 2026 · 9 MIN READ

LangChain vs. Custom Code: When to Build Your Own LLM Orchestration Layer

Every developer building with LLMs faces the 'build vs. framework' dilemma. I wasted weeks on custom orchestration before switching. Here's my honest breakdown of when LangChain saves you time and when it's overkill.

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Unmasking the Black Box: Mastering LangChain Callbacks for Debugging

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LangChain vs. Custom Code: When to Build Your Own LLM Orchestration Layer

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