llm-rosetta

LLM-Rosetta

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:

English: https://llm-rosetta.readthedocs.io/en/latest/
中文: https://llm-rosetta.readthedocs.io/zh-cn/latest/

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.

Community

Feel free to discuss the project via GitHub Issues or the LINUX DO community.

License

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

Release files for llm-rosetta 0.13.0

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Source distribution (sdist)

Source distribution for llm-rosetta 0.13.0
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Built distribution (wheel)

Table of built distributions (wheels) for llm-rosetta 0.13.0
File	Interpreter	ABI	Platform	Reset
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Total release size: 1.2 MB

Release files / llm_rosetta-0.13.0.tar.gz

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Release files / llm_rosetta-0.13.0-py3-none-any.whl

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Size	648.0 kB
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Upload date	Sep 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/Oaklight/llm-rosetta
Publishing commit: github.com/Oaklight/llm-rosetta/tree/812f86a88d59bcbc9e72ace6053c8e05d7f37606

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Publishing configuration: github.com/Oaklight/llm-rosetta/blob/812f86a88d59bcbc9e72ace6053c8e05d7f37606/.github/workflows/release.yml
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