Orchestral replaces LangChain’s complexity with reproducible, provider-agnostic LLM orchestration

A new framework from researchers Alexander and Jacob Roman rejects the complexity of current AI tools, offering a synchronous, type-safe alternative designed for reproducibility and cost-conscious science.In the rush to build autonomous AI agents, developers have largely been forced into a binary choice: surrender control to massive, complex ecosystems like LangChain, or lock themselves into single-vendor SDKs from providers like Anthropic or OpenAI. For software engineers, this is an annoyance. For scientists trying to use AI for reproducible research, it is a dealbreaker.Enter Orchestral AI, a new Python framework released on Github this week that attempts to chart a third path. Developed by theoretical physicist Alexander Roman and software engineer Jacob Roman, Orchestral positions its