LLM-Rosetta: A Hub-and-Spoke Intermediate Representation for Cross-Provider LLM API Translation

LLM-Rosetta is an open-source translation framework that solves API fragmentation across major Large Language Model providers by establishing a standardized intermediate representation. The hub-and-spoke architecture enables bidirectional conversion between OpenAI, Anthropic, and Google APIs with minimal overhead, addressing the O(N²) adapter problem that currently locks applications into specific vendors.

The proliferation of proprietary LLM APIs has created significant vendor lock-in, forcing developers to maintain separate integrations for each provider. LLM-Rosetta addresses this architectural pain point by introducing a provider-neutral intermediate representation (IR) that captures the semantic commonalities across APIs—messages, content types, tool calls, and streaming behaviors—rather than attempting direct bilateral translations. This approach reduces integration complexity from quadratic to linear scaling as new providers emerge.

The framework's design recognizes a critical insight: while LLM APIs appear syntactically diverse, their underlying semantics are remarkably consistent. By abstracting this shared core into a 9-type content model and 10-type stream event schema, LLM-Rosetta enables modular converter development. The implementation covers four major API standards representing the commercial majority while maintaining bidirectional fidelity with sub-100 microsecond overhead, matching the performance of existing single-provider solutions like LiteLLM.

For the developer ecosystem, this release significantly reduces the cost of multi-provider strategies. Applications can now switch between OpenAI, Anthropic, and Google without extensive refactoring, enabling more flexible vendor negotiations and reducing single-provider risk. The framework's deployment at Argonne National Laboratory and compliance with Open Responses standards validates production readiness. As LLM provider competition intensifies and pricing dynamics shift, the ability to rapidly switch providers becomes a strategic advantage for cost optimization and service continuity.

• →LLM-Rosetta reduces provider integration from O(N²) complexity to linear scaling through hub-and-spoke intermediate representation architecture.
• →Bidirectional API conversion for OpenAI, Anthropic, and Google covers the vast majority of commercial LLM providers with lossless round-trip fidelity.
• →Sub-100 microsecond conversion overhead enables real-time provider switching without performance degradation in production environments.
• →Open-source framework enables developers to reduce vendor lock-in and implement flexible multi-provider LLM strategies.
• →Production deployment at Argonne National Laboratory demonstrates enterprise-grade reliability and compliance with industry standards.