A practical playground for experimenting with LangChain, LLM orchestration, and structured AI systems.
LLM Workbench is a collection of private, focused repositories exploring how to design, orchestrate, and reason with Large Language Models using modern tooling such as LangChain, Runnable pipelines, and structured outputs. The goal is not to build production products, but to:
- Understand how LLM systems actually behave
- Explore composition patterns (chains, branches, tools, routers)
- Learn through minimal, readable examples
- Improve code clarity and architectural discipline Each repository is intentionally small and scoped to one idea at a time.
- TypeScript
- LangChain (JS)
- React + Next.js (App Router / Server Actions) where relevant
- Zod / JSON Schema for validation
- OpenAI-compatible models
This is an active, evolving workspace. Patterns may change as understanding improves. Stability is less important than learning and correctness.
