harness-model-protocol

Map internal message objects to provider-specific wire formats by identifying translation points and applying explicit mapping rules. This ensures internal messages become provider-native requests correctly across different LLMs.

Parse streaming tool calls by applying streaming parsing logic to evaluate multi-provider abstraction strategies. This guides robust integration by decoding how streaming tool call encoding works across different LLM providers.

Message roles in LLM wire protocols work through clear role handling across system, user, assistant, and tool messages. Translating these roles correctly ensures proper protocol semantics and reliable tool usage in agent harnesses.

Generate a compatibility assessment for a new LLM provider by evaluating wire-format variants across OpenAI, Anthropic, and Gemini. This produces a translation blueprint for internal message formats to ensure interoperability.

Yes, this approach supports mapping wire-format variants across OpenAI, Anthropic, and Gemini providers. It identifies translation points to ensure correct parsing and execution of tool calls and streaming interactions.