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 itself as the "scientific computing" answer to agent orchestration—prioritizing deterministic execution and debugging clarity over the "magic" of async-heavy alternatives.

The 'anti-framework' architecture

The core philosophy behind Orchestral is an intentional rejection of ...