litellm/docs/my-website/docs/contributing/adding_openai_compatible_providers.md

Adding OpenAI-Compatible Providers

For simple OpenAI-compatible providers (like Hyperbolic, Nscale, etc.), you can add support by editing a single JSON file.

Quick Start

1. Edit litellm/llms/openai_like/providers.json
2. Add your provider configuration
3. Test with: litellm.completion(model="your_provider/model-name", ...)

Basic Configuration

For a fully OpenAI-compatible provider:

{
  "your_provider": {
    "base_url": "https://api.yourprovider.com/v1",
    "api_key_env": "YOUR_PROVIDER_API_KEY"
  }
}

That's it! The provider is now available.

Configuration Options

Required Fields

• base_url - API endpoint (e.g., https://api.provider.com/v1)
• api_key_env - Environment variable name for API key (e.g., PROVIDER_API_KEY)

Optional Fields

• api_base_env - Environment variable to override base_url
• base_class - Use "openai_gpt" (default) or "openai_like"
• param_mappings - Map OpenAI parameter names to provider-specific names
• constraints - Parameter value constraints (min/max)
• special_handling - Special behaviors like content format conversion

Examples

Simple Provider (Fully Compatible)

{
  "hyperbolic": {
    "base_url": "https://api.hyperbolic.xyz/v1",
    "api_key_env": "HYPERBOLIC_API_KEY"
  }
}

Provider with Parameter Mapping

{
  "publicai": {
    "base_url": "https://api.publicai.co/v1",
    "api_key_env": "PUBLICAI_API_KEY",
    "param_mappings": {
      "max_completion_tokens": "max_tokens"
    }
  }
}

Provider with Constraints

{
  "custom_provider": {
    "base_url": "https://api.custom.com/v1",
    "api_key_env": "CUSTOM_API_KEY",
    "constraints": {
      "temperature_max": 1.0,
      "temperature_min": 0.0
    }
  }
}

Responses API Support

If your provider also supports the OpenAI Responses API (/v1/responses), add supported_endpoints:

{
  "your_provider": {
    "base_url": "https://api.yourprovider.com/v1",
    "api_key_env": "YOUR_PROVIDER_API_KEY",
    "supported_endpoints": ["/v1/chat/completions", "/v1/responses"]
  }
}

This enables litellm.responses() with zero additional code:

import litellm

response = litellm.responses(
    model="your_provider/model-name",
    input="Hello, what can you do?",
)
print(response.output)

If supported_endpoints is omitted, it defaults to []. Chat completions is always enabled for JSON providers regardless of this field.

The provider inherits all request/response handling from OpenAI's Responses API — streaming, tools, and all standard parameters work out of the box.

Usage

import litellm
import os

# Set your API key
os.environ["YOUR_PROVIDER_API_KEY"] = "your-key-here"

# Chat completions
response = litellm.completion(
    model="your_provider/model-name",
    messages=[{"role": "user", "content": "Hello"}],
)

# Responses API (if supported_endpoints includes "/v1/responses")
response = litellm.responses(
    model="your_provider/model-name",
    input="Hello",
)

When to Use Python Instead

Use a Python config class if you need:

• Custom authentication flows (OAuth, JWT, etc.)
• Complex request/response transformations
• Provider-specific streaming logic
• Advanced tool calling modifications

For chat completions, create a config class in litellm/llms/your_provider/chat/transformation.py that inherits from OpenAIGPTConfig or OpenAILikeChatConfig.

For responses API with small overrides, inherit from OpenAIResponsesAPIConfig and override only what's needed. See litellm/llms/perplexity/responses/transformation.py for a minimal example (~40 lines vs 400+).

Testing

Test your provider:

# Quick test
python -c "
import litellm
import os
os.environ['PROVIDER_API_KEY'] = 'your-key'
response = litellm.completion(
    model='provider/model-name',
    messages=[{'role': 'user', 'content': 'test'}]
)
print(response.choices[0].message.content)
"

Reference

See existing providers in litellm/llms/openai_like/providers.json for examples.