---
title: "Adding OpenAI-Compatible Providers"
description: "For simple OpenAI-compatible providers (like Hyperbolic, Nscale, etc.), you can add support by editing a single JSON file."
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/adding-openai-compatible-providers
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T10:59:16.832Z
license: CC-BY-4.0
attribution: "Adding OpenAI-Compatible Providers — Claudary (https://claudary.paisolsolutions.com/skills/adding-openai-compatible-providers)"
---

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

## Overview

# 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:

```json
{
  "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)

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

### Provider with Parameter Mapping

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

### Provider with Constraints

```json
{
  "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`:

```json
{
  "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:

```python
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

```python
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:

```bash
# 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.

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/adding-openai-compatible-providers) · https://claudary.paisolsolutions.com