LLM-Rosetta

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LLM-Rosetta — A Python library for converting between different LLM provider API formats using a hub-and-spoke architecture with a central IR (Intermediate Representation).

Full Documentation

Full documentation is available at:

The Problem

When building applications that work with multiple LLM providers, you face an N² conversion problem — every provider pair requires its own conversion logic. LLM-Rosetta solves this with a hub-and-spoke approach: each provider only needs a single converter to/from the shared IR format.

Provider A ──→ IR ──→ Provider B
Provider C ──→ IR ──→ Provider D
         ... and so on

Supported Providers

Provider API Standard Request Response Streaming
OpenAI Chat Completions ✅ ✅ ✅
OpenAI Responses API ✅ ✅ ✅
Anthropic Messages API ✅ ✅ ✅
Google GenAI API ✅ ✅ ✅

Ollama & Other OpenAI-Compatible Servers

LLM-Rosetta works out of the box with any server that exposes OpenAI-compatible endpoints. Ollama (v0.13+) is a great example — it supports three of the four API formats that LLM-Rosetta converts between:

Ollama Endpoint LLM-Rosetta Converter Since
/v1/chat/completions openai_chat Early versions
/v1/responses openai_responses v0.13.3
/v1/messages anthropic v0.14.0

Other compatible servers include HuggingFace TGI, vLLM, and LM Studio.

Features

  • Unified IR format for messages, tool calls, and content parts
  • Bidirectional conversion: requests to provider format, responses from provider format
  • Streaming support with typed stream events
  • Auto-detection of provider from request/response objects
  • Support for text, images, tool calls, and tool results
  • Zero required dependencies (only typing_extensions); provider SDKs are optional

Compliance Testing

llm-comply is a companion tool that validates LLM API endpoints against official specs. Use it to verify that a gateway or proxy correctly implements the OpenAI Chat, Open Responses, Anthropic Messages, and Google GenAI formats.

pip install llm-comply

# Test your gateway endpoint
llm-comply -u https://your-gateway/v1 -k $API_KEY -m your-model --format openai-chat

A hosted version is available at llm-comply.service.oaklight.top. An on-demand Compliance workflow is also included in CI.

Installation

Basic Installation

Install the core package (requires Python >= 3.8):

pip install llm-rosetta

Installing with Provider SDKs

# Individual providers
pip install llm-rosetta[openai]
pip install llm-rosetta[anthropic]
pip install llm-rosetta[google]

# All providers
pip install llm-rosetta[openai,anthropic,google]

Optional Dependencies

Extra Packages Description
openai openai OpenAI Chat Completions & Responses API
anthropic anthropic Anthropic Messages API
google google-genai Google GenAI API

Quick Start

from llm_rosetta import OpenAIChatConverter, AnthropicConverter

# Create converters
openai_conv = OpenAIChatConverter()
anthropic_conv = AnthropicConverter()

# Convert an OpenAI response to IR, then to Anthropic format
ir_messages = openai_conv.response_from_provider(openai_response)
anthropic_request = anthropic_conv.request_to_provider(ir_messages)

Auto-Detection

from llm_rosetta import convert, detect_provider

# Automatically detect provider and convert
provider = detect_provider(some_response)
ir_messages = convert(some_response, direction="from_provider")

Cross-Provider Conversation

from llm_rosetta import OpenAIChatConverter, GoogleGenAIConverter
from llm_rosetta.types.ir import Message, ContentPart

# Shared IR message history
ir_messages = []

# Turn 1: Ask OpenAI
ir_messages.append(Message(role="user", content=[ContentPart(type="text", text="Hello!")]))
openai_request = openai_conv.request_to_provider({"messages": ir_messages})
openai_response = openai_client.chat.completions.create(**openai_request)
ir_messages.extend(openai_conv.response_from_provider(openai_response))

# Turn 2: Continue with Google — full context preserved
google_request = google_conv.request_to_provider({"messages": ir_messages})

Citation

If you use LLM-Rosetta in your research, please cite our paper:

@article{ding2026llm,
  title={LLM-Rosetta: A Hub-and-Spoke Intermediate Representation for Cross-Provider LLM API Translation},
  author={Ding, Peng},
  journal={arXiv preprint arXiv:2604.09360},
  year={2026}
}

Contributing

Contributions are welcome! Please visit the GitHub repository to get started.

License

This project is licensed under the MIT License — see the LICENSE file for details.

Project links

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Credits

Author: Oaklight

GitHub Statistics

Data verified by PyPI on Aug 21, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.10

Provides Extra

openai anthropic google gateway profiling dev all

Tags

openai anthropic gemini google-genai api-adapter llm-gateway llmir streaming tool-calling multi-provider responses-api

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