Esperanto 🌐

Esperanto is a powerful Python library that provides a unified interface for interacting with various Large Language Model (LLM) providers. It simplifies the process of working with different AI models (LLMs, Embedders, Transcribers, and TTS) APIs by offering a consistent interface while maintaining provider-specific optimizations.

Why Esperanto? 🚀

🪶 Ultra-Lightweight Architecture

Direct HTTP Communication: All providers communicate directly via HTTP APIs using httpx - no bulky vendor SDKs required
Minimal Dependencies: Unlike LangChain and similar frameworks, Esperanto has a tiny footprint with zero overhead layers
Production-Ready Performance: Direct API calls mean faster response times and lower memory usage

🔄 True Provider Flexibility

Standardized Responses: Switch between any provider (OpenAI ↔ Anthropic ↔ Google ↔ etc.) without changing a single line of code
Consistent Interface: Same methods, same response objects, same patterns across all 15+ providers
Future-Proof: Add new providers or change existing ones without refactoring your application

⚡ Perfect for Production

Prototyping to Production: Start experimenting and deploy the same code to production
No Vendor Lock-in: Test different providers, optimize costs, and maintain flexibility
Enterprise-Ready: Direct HTTP calls, standardized error handling, and comprehensive async support

Whether you're building a quick prototype or a production application serving millions of requests, Esperanto gives you the performance of direct API calls with the convenience of a unified interface.

Features ✨

Unified Interface: Work with multiple LLM providers using a consistent API
Provider Support:
- OpenAI (GPT-4o, o1, o3, o4, Whisper, TTS)
- OpenAI-Compatible (LM Studio, Ollama, vLLM, custom endpoints)
- Anthropic (Claude models)
- OpenRouter (Access to multiple models)
- xAI (Grok)
- Perplexity (Sonar models)
- Groq (Mixtral, Llama, Whisper)
- Google GenAI (Gemini LLM, Speech-to-Text, Text-to-Speech, Embedding with native task optimization)
- Vertex AI (Google Cloud, LLM, Embedding, TTS)
- Ollama (Local deployment multiple models)
- Transformers (Universal local models - Qwen, CrossEncoder, BAAI, Jina, Mixedbread)
- ElevenLabs (Text-to-Speech, Speech-to-Text)
- Azure OpenAI (Chat, Embedding, Whisper, TTS)
- Mistral (Mistral Large, Small, Embedding, etc.)
- DeepSeek (deepseek-chat)
- DashScope / Qwen (qwen-turbo, qwen-plus, qwen-max)
- MiniMax (MiniMax-M2.5)
- Voyage (Embeddings, Reranking)
- Jina (Advanced embedding models with task optimization, Reranking)
Embedding Support: Multiple embedding providers for vector representations
Reranking Support: Universal reranking interface for improving search relevance
Speech-to-Text Support: Transcribe audio using multiple providers
Text-to-Speech Support: Generate speech using multiple providers
Async Support: Both synchronous and asynchronous API calls
Streaming: Support for streaming responses
Structured Output: JSON output formatting (where supported)
LangChain Integration: Easy conversion to LangChain chat models

📚 Documentation

Quick Start Guide - Get started in 5 minutes
Documentation Index - Complete documentation hub
Provider Comparison - Choose the right provider
Configuration Guide - Environment setup

By Capability

Language Models (LLM) - Text generation and chat
Embeddings - Vector representations
Reranking - Search relevance
Speech-to-Text - Audio transcription
Text-to-Speech - Voice generation

By Provider

Provider Setup Guides - Complete setup for all 17 providers

Advanced Topics

CHANGELOG - Version history and migration guides

Installation 🚀

Install Esperanto using pip:

pip install esperanto

Optional Dependencies

Transformers Provider

If you plan to use the transformers provider, install with the transformers extra:

pip install "esperanto[transformers]"

This installs:

transformers - Core Hugging Face library
torch - PyTorch framework
tokenizers - Fast tokenization
sentence-transformers - CrossEncoder support
scikit-learn - Advanced embedding features
numpy - Numerical computations

LangChain Integration

If you plan to use any of the .to_langchain() methods, you need to install the correct LangChain SDKs manually:

# Core LangChain dependencies (required)
pip install "langchain>=0.3.8,<0.4.0" "langchain-core>=0.3.29,<0.4.0"

# Provider-specific LangChain packages (install only what you need)
pip install "langchain-openai>=0.2.9"
pip install "langchain-anthropic>=0.3.0"
pip install "langchain-google-genai>=2.1.2"
pip install "langchain-ollama>=0.2.0"
pip install "langchain-groq>=0.2.1"
pip install "langchain_mistralai>=0.2.1"
pip install "langchain_deepseek>=0.1.3"

Provider Support Matrix

| Provider |--------------|---- | OpenAI | ✅ | OpenAI-Compatible | ✅ | Anthropic | ✅ | Groq | ✅ | Google (GenAI) | ✅ | Vertex AI | ✅ | Ollama | ✅ | Perplexity | ✅ | Transformers | ❌ | ElevenLabs | ❌ | Azure OpenAI | ✅ | Mistral | ✅ | DeepSeek | ✅ | Voyage | ❌ | Jina | ❌ | xAI | ✅ | DashScope | ✅ | MiniMax | ✅ | OpenRouter | ✅ | LLM Support | Embedding Support | Reranking Support | Speech-to-Text | Text-to-Speech | JSON Mode | ---------|------------------|-------------------|----------------|----------------|-----------| | ✅ | ❌ | ✅ | ✅ | ✅ | | ✅ | ❌ | ✅ | ✅ | ⚠️* | | ❌ | ❌ | ❌ | ❌ | ✅ | | ❌ | ❌ | ✅ | ❌ | ✅ | | ✅ | ❌ | ✅ | ✅ | ✅ | | ✅ | ❌ | ❌ | ✅ | ❌ | | ✅ | ❌ | ❌ | ❌ | ❌ | | ❌ | ❌ | ❌ | ❌ | ✅ | | ✅ | ✅ | ❌ | ❌ | ❌ | | ❌ | ❌ | ✅ | ✅ | ❌ | | ✅ | ❌ | ✅ | ✅ | ✅ | | ✅ | ❌ | ❌ | ❌ | ✅ | | ❌ | ❌ | ❌ | ❌ | ✅ | | ✅ | ✅ | ❌ | ❌ | ❌ | | ✅ | ✅ | ❌ | ❌ | ❌ | | ❌ | ❌ | ❌ | ❌ | ❌ | | ❌ | ❌ | ❌ | ❌ | ✅ | | ❌ | ❌ | ❌ | ❌ | ✅ | | ❌ | ❌ | ❌ | ❌ | ✅ |

*⚠️ OpenAI-Compatible: JSON mode support depends on the specific endpoint implementation

Quick Start 🏃‍♂️

You can use Esperanto in two ways: directly with provider-specific classes or through the AI Factory.

Using AI Factory

The AI Factory provides a convenient way to create model instances and discover available providers:

from esperanto.factory import AIFactory

# Get available providers for each model type
providers = AIFactory.get_available_providers()
print(providers)
# Output:
# {
#     'language': ['anthropic', 'azure', 'dashscope', 'deepseek', 'google', 'groq', 'minimax', 'mistral', 'ollama', 'openai', 'open

Esperanto

Install / Use

README