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LLM-Rosetta

PyPI version GitHub release CI License: MIT Ask DeepWiki

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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

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})

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.

About

Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome!

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