Multi-LLM Prompt Converter: OpenAI ↔ Claude ↔ Gemini ↔ Llama

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Cross-LLM Schema Migration: Adapting Prompts for OpenAI, Claude & Gemini

1. Architectural Differences Between Leading Model Formats

OpenAI Messages API

Structure: Flat array of role objects
Roles: system, user, assistant, tool
System Position: First object in the messages list
Tool Format: tools[].function.parameters

Anthropic Claude API

Structure: Top-level system string + messages array
Roles: Alternating user and assistant only
Prompt Preference: Hierarchical XML tags (<rules>)
Tool Format: tools[].input_schema

Google Gemini API

Structure: contents[].parts[].text
Roles: user and model (not assistant)
System Parameter: system_instruction.parts[].text
Function Calls: functionDeclarations

Meta Llama 3 ChatML

Structure: Plain string with special delimiter tokens
Header Token: <|start_header_id|>role<|end_header_id|>
Turn End: <|eot_id|>
Usage: Open-source inference (vLLM, Ollama)

2. How to Migrate Function Calling / Tool Definitions

When porting code from OpenAI to Anthropic Claude, tool schemas can fail if passed unchanged. Anthropic requires removing the outer function container and renaming the parameters key to input_schema:

// OpenAI Format:
{ "type": "function", "function": { "name": "get_weather", "parameters": { ... } } }

// Converted Anthropic Claude Format:
{ "name": "get_weather", "input_schema": { ... } }

Frequently asked questions

Why do AI prompts need syntactic conversion between providers?

Each LLM provider enforces different API schemas. OpenAI utilizes a messages array with role objects ('system', 'user', 'assistant'). Anthropic Claude historically preferred XML tags () and 'Human:'/'Assistant:' turns, while its modern API expects a dedicated system parameter and messages array. Google Gemini uses 'contents' with 'parts', and open-weights models like Llama 3 use special ChatML header tokens (<|start_header_id|>).

How does tool calling and function schema translation work?

OpenAI defines tools using { type: 'function', function: { name, description, parameters: { ... } } }. Anthropic Claude uses { name, description, input_schema: { ... } }. This converter automatically normalizes the outer container while preserving the underlying JSON Schema properties intact.

Can I convert multi-turn conversation logs?

Yes. The converter parses multi-turn dialogs marked with role labels (User:, Assistant:, Human:, System:) or raw JSON message arrays and translates every conversational turn into the target model's syntax.

Does format conversion affect model reasoning quality?

Using the native formatting preferred by a model family measurably improves adherence. For example, Claude models follow instructions significantly better when rules are placed in XML tags rather than generic text blocks, and Llama 3 requires strict ChatML header tags when fine-tuned.

Is my proprietary prompt transmitted to any external server?

No. 100% of the format auto-detection, AST regex mapping, and JSON re-structuring runs entirely in your local browser sandbox. Zero prompt data is transmitted.

Multi-LLM Prompt & Schema Converter