# Provider Configuration Guide

This guide provides detailed information on how to configure each provider in Kilo Code CLI. Each provider has specific configuration requirements and optional settings that can be customized to suit your needs.

## Table of Contents

- [Introduction](#introduction)
- [Configuration Methods](#configuration-methods)
- [Provider Details](#provider-details)
    - [Kilo Code](#kilocode)
    - [Anthropic](#anthropic)
    - [OpenAI Native](#openai-native)
    - [OpenRouter](#openrouter)
    - [AWS Bedrock](#bedrock)
    - [Google Gemini](#gemini)
    - [Google Vertex AI](#vertex)
    - [Claude Code](#claude-code)
    - [Mistral](#mistral)
    - [Groq](#groq)
    - [DeepSeek](#deepseek)
    - [xAI](#xai)
    - [Cerebras](#cerebras)
    - [Ollama](#ollama)
    - [LM Studio](#lmstudio)
    - [VSCode Language Model](#vscode-lm)
    - [OpenAI](#openai)
    - [Glama](#glama)
    - [HuggingFace](#huggingface)
    - [LiteLLM](#litellm)
    - [Moonshot](#moonshot)
    - [Doubao](#doubao)
    - [Chutes](#chutes)
    - [SambaNova](#sambanova)
    - [Fireworks](#fireworks)
    - [Featherless](#featherless)
    - [DeepInfra](#deepinfra)
    - [IO Intelligence](#io-intelligence)
    - [Qwen Code](#qwen-code)
    - [Gemini CLI](#gemini-cli)
    - [ZAI](#zai)
    - [Minimax](#minimax)
    - [Unbound](#unbound)
    - [Requesty](#requesty)
    - [Roo](#roo)
    - [Vercel AI Gateway](#vercel-ai-gateway)
    - [Virtual Quota Fallback](#virtual-quota-fallback)
    - [Human Relay](#human-relay)
    - [Fake AI](#fake-ai)
    - [OVHcloud AI Endpoints](#ovhcloud-ai-endpoints)
    - [Inception](#inception)
    - [Synthetic](#synthetic)

## Introduction

Kilo Code CLI supports multiple AI providers, each with their own configuration requirements. This document details the configuration fields for each provider, including required and optional parameters.

## Configuration Methods

You can configure providers using:

1. **Interactive CLI**: Run `kilocode config` to configure providers interactively
2. **Configuration File**: Edit your configuration file directly (typically located in your user config directory)
3. **Environment Variables**: Some providers support environment variable configuration

---

## Provider Details

### kilocode

The official Kilo Code provider for accessing Kilo Code's managed AI services.

**Description**: Access Kilo Code's managed AI infrastructure with support for multiple models and organizations.

**Required Fields**:

- `kilocodeToken` (password): Your Kilo Code authentication token
- `kilocodeModel` (text): The model to use (default: `anthropic/claude-sonnet-4.5`)

**Optional Fields**:

- `kilocodeOrganizationId` (text): Organization ID for team accounts (leave empty for personal use)
- `openRouterSpecificProvider` (text): Specific OpenRouter provider to use when routing through OpenRouter
- `openRouterProviderDataCollection` (text): OpenRouter provider data collection preference
    - `allow`: Allow data collection by the provider
    - `deny`: Deny data collection by the provider
- `openRouterProviderSort` (text): OpenRouter provider sorting preference for model selection
    - `price`: Sort by price (lowest first)
    - `throughput`: Sort by throughput (highest first)
    - `latency`: Sort by latency (lowest first)
- `openRouterZdr` (boolean): Enable OpenRouter Zero Data Retention for enhanced privacy

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "kilocode",
	"kilocodeToken": "your-token-here",
	"kilocodeModel": "anthropic/claude-sonnet-4",
	"kilocodeOrganizationId": "org-123456"
}
```

**Default Model**: `anthropic/claude-sonnet-4.5`

**Notes**:

- OpenRouter-related fields are used when Kilocode routes requests through OpenRouter
- Zero Data Retention (ZDR) ensures that no request data is stored by OpenRouter

---

### anthropic

Direct integration with Anthropic's Claude API.

**Description**: Use Claude models directly from Anthropic with your own API key.

**Required Fields**:

- `apiKey` (password): Your Anthropic API key
- `apiModelId` (text): The Claude model to use (default: `claude-sonnet-4.5`)

**Optional Fields**:

- `anthropicBaseUrl` (text): Custom base URL for API requests (leave empty for default)
- `anthropicUseAuthToken` (boolean): Use authentication token instead of API key for requests. When enabled, the system will use token-based authentication instead of the standard API key authentication method.
- `anthropicBeta1MContext` (boolean): Enable beta 1M context window support. This allows access to extended context windows for supported models, enabling processing of larger documents and conversations.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "anthropic",
	"apiKey": "sk-ant-...",
	"apiModelId": "claude-sonnet-4.5",
	"anthropicBaseUrl": "",
	"anthropicUseAuthToken": false,
	"anthropicBeta1MContext": false
}
```

**Default Model**: `claude-sonnet-4.5`

**Notes**:

- Requires an Anthropic API key from https://console.anthropic.com/
- Supports all Claude 3 and Claude 3.5 models
- The `anthropicUseAuthToken` option is useful for enterprise deployments with custom authentication systems
- The `anthropicBeta1MContext` feature requires beta access and may incur additional costs

---

### openai-native

Native OpenAI API integration.

**Description**: Use OpenAI's models with native API support.

**Required Fields**:

- `openAiNativeApiKey` (password): Your OpenAI API key
- `apiModelId` (text): The OpenAI model to use (default: `gpt-5-chat-latest`)

**Optional Fields**:

- `openAiNativeBaseUrl` (text): Custom base URL for API requests (leave empty for default)
- `openAiNativeServiceTier` (text): Service tier for request prioritization and latency optimization
    - `auto` (default): Let OpenAI automatically select the best tier based on current system load and model availability
    - `default`: Use standard processing with balanced performance and cost
    - `flex`: Cost-optimized processing with variable latency, ideal for non-time-sensitive workloads
    - `priority`: Fastest processing with higher priority in the queue, recommended for latency-sensitive applications

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "openai-native",
	"openAiNativeApiKey": "sk-...",
	"apiModelId": "gpt-5-chat-latest",
	"openAiNativeBaseUrl": "",
	"openAiNativeServiceTier": "auto"
}
```

**Default Model**: `gpt-5-chat-latest`

**Notes**:

- Requires an OpenAI API key from https://platform.openai.com/
- Supports GPT-4, GPT-4 Turbo, and GPT-3.5 models
- Service tiers allow you to optimize for either cost (flex) or latency (priority) based on your use case
- The `auto` tier is recommended for most users as it balances performance and cost automatically

---

### openrouter

Access multiple AI models through OpenRouter's unified API.

**Description**: Use OpenRouter to access various AI models from different providers through a single API.

**Required Fields**:

- `openRouterApiKey` (password): Your OpenRouter API key
- `openRouterModelId` (text): The model identifier (default: `anthropic/claude-3-5-sonnet`)

**Optional Fields**:

- `openRouterBaseUrl` (text): Custom base URL (leave empty for default)
- `openRouterSpecificProvider` (text): Specific OpenRouter provider to use for routing requests. When specified, OpenRouter will route your request to this specific provider instead of automatically selecting one. Useful when you want to ensure requests go to a particular infrastructure provider.
- `openRouterUseMiddleOutTransform` (boolean): Enable middle-out transform for optimized request routing. This feature can improve routing efficiency and reduce latency by using advanced request transformation techniques.
- `openRouterProviderDataCollection` (text): OpenRouter provider data collection preference
    - `allow`: Allow data collection by the provider for model improvement and analytics
    - `deny`: Deny data collection by the provider to maintain stricter privacy controls
- `openRouterProviderSort` (text): OpenRouter provider sorting preference for model selection
    - `price`: Sort by price (lowest first) - optimizes for cost efficiency
    - `throughput`: Sort by throughput (highest first) - optimizes for maximum tokens per second
    - `latency`: Sort by latency (lowest first) - optimizes for fastest response time
- `openRouterZdr` (boolean): Enable OpenRouter Zero Data Retention (ZDR) for enhanced privacy. When enabled, OpenRouter will not store any request or response data, ensuring maximum privacy and compliance with data protection requirements.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "openrouter",
	"openRouterApiKey": "sk-or-...",
	"openRouterModelId": "anthropic/claude-3-5-sonnet",
	"openRouterBaseUrl": "",
	"openRouterSpecificProvider": "anthropic",
	"openRouterUseMiddleOutTransform": false,
	"openRouterProviderDataCollection": "deny",
	"openRouterProviderSort": "latency",
	"openRouterZdr": true
}
```

**Default Model**: `anthropic/claude-3-5-sonnet`

**Notes**:

- Get your API key from https://openrouter.ai/
- Supports models from Anthropic, OpenAI, Google, Meta, and more
- Zero Data Retention (ZDR) ensures that no request data is stored by OpenRouter, providing maximum privacy
- Provider sorting allows you to optimize for your specific use case (cost, speed, or throughput)
- Specific provider routing is useful when you need consistent infrastructure or have provider-specific requirements

---

### bedrock

AWS Bedrock for accessing foundation models on AWS infrastructure.

**Description**: Use AWS Bedrock to access various foundation models with AWS security and compliance.

**Required Fields**:

- `awsAccessKey` (password): Your AWS access key ID
- `awsSecretKey` (password): Your AWS secret access key
- `awsRegion` (text): AWS region (default: `us-east-1`)
- `apiModelId` (text): The model to use (default: `anthropic.claude-sonnet-4.5-20250929-v1:0`)

**Optional Fields**:

- `awsSessionToken` (password): AWS session token for temporary credentials
- `awsUseCrossRegionInference` (boolean): Enable cross-region inference to access models in different AWS regions
- `awsUsePromptCache` (boolean): Enable prompt caching to reduce costs and latency for repeated prompts. When enabled, Bedrock caches portions of your prompts that are reused across requests, significantly reducing both API costs and response times for subsequent requests with similar context.
- `awsProfile` (string): AWS profile name from your credentials file (typically `~/.aws/credentials`). Use this to specify which AWS profile to use for authentication instead of providing access keys directly.
- `awsUseProfile` (boolean): Use AWS profile from credentials file instead of access keys. When enabled, the system will authenticate using the profile specified in `awsProfile` rather than `awsAccessKey` and `awsSecretKey`.
- `awsApiKey` (string): AWS API key for alternative authentication methods. This is used for specific authentication scenarios that require API key-based access.
- `awsUseApiKey` (boolean): Use API key authentication instead of access keys. Enable this when you want to authenticate using `awsApiKey` rather than the standard AWS access key/secret key pair.
- `awsCustomArn` (string): Custom Amazon Resource Name (ARN) for cross-account access or custom model access. Use this when you need to access models in a different AWS account or when using custom fine-tuned models.
- `awsModelContextWindow` (number): Override the model's default context window size. Specify a custom token limit for the context window. Must be a positive integer. Use this when you need to limit or extend the context size beyond the model's default.
- `awsBedrockEndpointEnabled` (boolean): Enable custom Bedrock endpoint. Set to true when you want to use a custom endpoint URL instead of the standard AWS Bedrock endpoint.
- `awsBedrockEndpoint` (string): Custom Bedrock endpoint URL. Specify a custom endpoint when using VPC endpoints, private endpoints, or region-specific endpoints. Only used when `awsBedrockEndpointEnabled` is true.
- `awsBedrock1MContext` (boolean): Enable 1M token context window support for compatible models. When enabled, allows access to extended context windows (up to 1 million tokens) for models that support this feature, enabling processing of extremely large documents and conversations.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "bedrock",
	"awsAccessKey": "AKIA...",
	"awsSecretKey": "...",
	"awsRegion": "us-east-1",
	"apiModelId": "anthropic.claude-sonnet-4.5-20250929-v1:0",
	"awsSessionToken": "",
	"awsUseCrossRegionInference": false,
	"awsUsePromptCache": true,
	"awsBedrock1MContext": false
}
```

**Example Configuration with AWS Profile**:

```json
{
	"id": "bedrock-profile",
	"provider": "bedrock",
	"awsProfile": "my-aws-profile",
	"awsUseProfile": true,
	"awsRegion": "us-east-1",
	"apiModelId": "anthropic.claude-sonnet-4.5-20250929-v1:0",
	"awsUsePromptCache": true
}
```

**Example Configuration with Custom Endpoint**:

```json
{
	"id": "bedrock-custom",
	"provider": "bedrock",
	"awsAccessKey": "AKIA...",
	"awsSecretKey": "...",
	"awsRegion": "us-east-1",
	"apiModelId": "anthropic.claude-sonnet-4.5-20250929-v1:0",
	"awsBedrockEndpointEnabled": true,
	"awsBedrockEndpoint": "https://bedrock-runtime.us-east-1.amazonaws.com",
	"awsModelContextWindow": 200000
}
```

**Default Model**: `anthropic.claude-sonnet-4.5-20250929-v1:0`

**Notes**:

- Requires AWS account with Bedrock access
- Supports Claude, Llama, Mistral, and other foundation models
- Cross-region inference allows access to models in different regions
- **Authentication Options**: You can authenticate using either:
    - Direct credentials (`awsAccessKey` and `awsSecretKey`)
    - AWS profile (`awsProfile` with `awsUseProfile` enabled)
    - API key (`awsApiKey` with `awsUseApiKey` enabled)
- **Prompt Caching**: Significantly reduces costs for applications with repeated context. The first request pays full price, but subsequent requests with cached content can be up to 90% cheaper.
- **Custom ARN**: Useful for accessing models in different AWS accounts or using custom fine-tuned models deployed in your organization.
- **Custom Endpoints**: Use VPC endpoints for enhanced security or region-specific endpoints for compliance requirements.
- **Extended Context**: The 1M context window feature requires model support and may incur additional costs. Check AWS Bedrock documentation for compatible models.

---

### gemini

Google's Gemini AI models via direct API access.

**Description**: Access Google's Gemini models directly with your API key.

**Required Fields**:

- `geminiApiKey` (password): Your Google AI API key
- `apiModelId` (text): The model to use (default: `gemini-2.5-flash-preview-04-17`)

**Optional Fields**:

- `googleGeminiBaseUrl` (text): Custom base URL (leave empty for default)
- `enableUrlContext` (boolean): Allows the model to access and process URLs in prompts. When enabled, the model can fetch and analyze content from URLs provided in the conversation, enabling web-based research and content analysis capabilities.
- `enableGrounding` (boolean): Enables Google Search grounding for factual responses. When enabled, the model can use Google Search to ground its responses in real-time information, improving accuracy for factual queries and reducing hallucinations.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "gemini",
	"geminiApiKey": "AIza...",
	"apiModelId": "gemini-2.5-flash-preview-04-17",
	"googleGeminiBaseUrl": "",
	"enableUrlContext": false,
	"enableGrounding": false
}
```

**Example Configuration with URL Context and Grounding**:

```json
{
	"id": "gemini-enhanced",
	"provider": "gemini",
	"geminiApiKey": "AIza...",
	"apiModelId": "gemini-2.5-flash-preview-04-17",
	"enableUrlContext": true,
	"enableGrounding": true
}
```

**Default Model**: `gemini-2.5-flash-preview-04-17`

**Notes**:

- Get your API key from https://makersuite.google.com/app/apikey
- Supports Gemini Pro and Gemini Ultra models
- **URL Context**: Enables the model to fetch and process web content directly from URLs in prompts. Useful for analyzing web pages, documentation, or online resources.
- **Grounding**: Connects the model to Google Search for real-time information retrieval. This feature helps ensure responses are based on current, factual information and can significantly reduce hallucinations for knowledge-based queries.
- Both URL Context and Grounding features may incur additional costs and require appropriate API permissions.

---

### vertex

Google Cloud Vertex AI for enterprise-grade AI deployment.

**Description**: Use Google Cloud's Vertex AI platform for accessing AI models with enterprise features.

**Required Fields**:

- `vertexProjectId` (text): Your Google Cloud project ID
- `vertexRegion` (text): Google Cloud region (default: `us-central1`)
- `apiModelId` (text): The model to use (default: `claude-4.5-sonnet`)

**Authentication** (choose one):

- `vertexJsonCredentials` (password): JSON service account credentials
- `vertexKeyFile` (text): Path to service account key file

**Optional Fields**:

- `enableUrlContext` (boolean): Allows the model to access and process URLs in prompts. When enabled, the model can fetch and analyze content from URLs provided in the conversation, enabling web-based research and content analysis capabilities.
- `enableGrounding` (boolean): Enables Google Search grounding for factual responses. When enabled, the model can use Google Search to ground its responses in real-time information, improving accuracy for factual queries and reducing hallucinations.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "vertex",
	"vertexProjectId": "my-project-123",
	"vertexRegion": "us-central1",
	"apiModelId": "claude-4.5-sonnet",
	"vertexJsonCredentials": "{...}",
	"vertexKeyFile": "",
	"enableUrlContext": false,
	"enableGrounding": false
}
```

**Example Configuration with URL Context and Grounding**:

```json
{
	"id": "vertex-enhanced",
	"provider": "vertex",
	"vertexProjectId": "my-project-123",
	"vertexRegion": "us-central1",
	"apiModelId": "claude-4.5-sonnet",
	"vertexJsonCredentials": "{...}",
	"enableUrlContext": true,
	"enableGrounding": true
}
```

**Default Model**: `claude-4.5-sonnet`

**Notes**:

- Requires Google Cloud project with Vertex AI enabled
- Supports Claude, Gemini, and other models through Vertex AI
- Use either JSON credentials or key file path, not both
- **URL Context**: Enables the model to fetch and process web content directly from URLs in prompts. Useful for analyzing web pages, documentation, or online resources.
- **Grounding**: Connects the model to Google Search for real-time information retrieval. This feature helps ensure responses are based on current, factual information and can significantly reduce hallucinations for knowledge-based queries.
- Both URL Context and Grounding features may incur additional costs and require appropriate API permissions in your Google Cloud project.

---

### claude-code

Local Claude Code CLI integration.

**Description**: Use the Claude Code CLI tool for local AI interactions.

**Required Fields**:

- `claudeCodePath` (text): Path to the Claude Code executable
- `apiModelId` (text): The model to use (default: `claude-sonnet-4-5`)
- `claudeCodeMaxOutputTokens` (text): Maximum output tokens (default: `8000`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "claude-code",
	"claudeCodePath": "/usr/local/bin/claude-code",
	"apiModelId": "claude-sonnet-4-5",
	"claudeCodeMaxOutputTokens": "8000"
}
```

**Default Model**: `claude-sonnet-4-5`

**Notes**:

- Requires Claude Code CLI to be installed locally
- Useful for offline or local-first workflows

---

### mistral

Mistral AI's language models.

**Description**: Access Mistral's powerful language models including Codestral for code generation.

**Required Fields**:

- `mistralApiKey` (password): Your Mistral API key
- `apiModelId` (text): The model to use (default: `magistral-medium-latest`)

**Optional Fields**:

- `mistralCodestralUrl` (text): Custom Codestral base URL (leave empty for default)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "mistral",
	"mistralApiKey": "...",
	"apiModelId": "magistral-medium-latest",
	"mistralCodestralUrl": ""
}
```

**Default Model**: `magistral-medium-latest`

**Notes**:

- Get your API key from https://console.mistral.ai/
- Supports Mistral Large, Mistral Medium, and Codestral models

---

### groq

Groq's ultra-fast LPU inference.

**Description**: Use Groq's Language Processing Unit (LPU) for extremely fast inference.

**Required Fields**:

- `groqApiKey` (password): Your Groq API key
- `apiModelId` (text): The model to use (default: `llama-3.3-70b-versatile`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "groq",
	"groqApiKey": "gsk_...",
	"apiModelId": "llama-3.3-70b-versatile"
}
```

**Default Model**: `llama-3.3-70b-versatile`

**Notes**:

- Get your API key from https://console.groq.com/
- Known for extremely fast inference speeds
- Supports Llama, Mixtral, and Gemma models

---

### deepseek

DeepSeek's AI models.

**Description**: Access DeepSeek's language models optimized for coding and reasoning.

**Required Fields**:

- `deepSeekApiKey` (password): Your DeepSeek API key
- `apiModelId` (text): The model to use (default: `deepseek-chat`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "deepseek",
	"deepSeekApiKey": "...",
	"apiModelId": "deepseek-chat"
}
```

**Default Model**: `deepseek-chat`

**Notes**:

- Get your API key from https://platform.deepseek.com/
- Optimized for code generation and technical tasks

---

### xai

xAI's Grok models.

**Description**: Access xAI's Grok language models.

**Required Fields**:

- `xaiApiKey` (password): Your xAI API key
- `apiModelId` (text): The model to use (default: `grok-code-fast-1`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "xai",
	"xaiApiKey": "...",
	"apiModelId": "grok-code-fast-1"
}
```

**Default Model**: `grok-code-fast-1`

**Notes**:

- Get your API key from https://x.ai/
- Access to Grok models

---

### cerebras

Cerebras AI inference platform.

**Description**: Use Cerebras' wafer-scale AI inference platform.

**Required Fields**:

- `cerebrasApiKey` (password): Your Cerebras API key
- `apiModelId` (text): The model to use (default: `qwen-3-coder-480b-free`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "cerebras",
	"cerebrasApiKey": "...",
	"apiModelId": "qwen-3-coder-480b-free"
}
```

**Default Model**: `qwen-3-coder-480b-free`

**Notes**:

- Get your API key from https://cerebras.ai/
- Optimized for high-throughput inference

---

### ollama

Local Ollama instance for running models locally.

**Description**: Run AI models locally using Ollama.

**Required Fields**:

- `ollamaBaseUrl` (text): Ollama server URL (default: `http://localhost:11434`)
- `ollamaModelId` (text): Model identifier (default: `llama3.2`)

**Optional Fields**:

- `ollamaApiKey` (password): API key if authentication is enabled
- `ollamaNumCtx` (number): Context window size for the model. Controls the maximum number of tokens the model can process at once. Common values include:
    - `2048`: Small context, lower memory usage
    - `4096`: Standard context for most tasks
    - `8192`: Extended context for longer conversations
    - `16384`: Large context for complex tasks
    - `32768`: Very large context (requires significant memory)
      Higher values allow processing longer conversations and larger documents but require more memory. If not specified, Ollama uses the model's default context size.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "ollama",
	"ollamaBaseUrl": "http://localhost:11434",
	"ollamaModelId": "llama3.2",
	"ollamaApiKey": "",
	"ollamaNumCtx": 8192
}
```

**Default Model**: `llama3.2`

**Notes**:

- Requires Ollama to be installed and running locally
- Download from https://ollama.ai/
- Supports many open-source models (Llama, Mistral, CodeLlama, etc.)
- No API key required for local usage
- The `ollamaNumCtx` parameter directly affects memory usage - ensure your system has sufficient RAM for larger context windows

---

### lmstudio

LM Studio for local model inference.

**Description**: Use LM Studio to run models locally with a user-friendly interface.

**Required Fields**:

- `lmStudioBaseUrl` (text): LM Studio server URL (default: `http://localhost:1234/v1`)
- `lmStudioModelId` (text): Model identifier (default: `local-model`)

**Optional Fields**:

- `lmStudioDraftModelId` (string): Draft model ID for speculative decoding. Specifies a smaller, faster model that generates initial token predictions which are then verified by the main model. This can significantly improve inference speed while maintaining output quality. The draft model should be compatible with the main model's vocabulary and typically be a smaller version of the same model family.
- `lmStudioSpeculativeDecodingEnabled` (boolean): Enable speculative decoding for faster inference. When enabled along with a draft model, uses a two-stage generation process: the draft model proposes multiple tokens ahead, and the main model verifies them in parallel. This technique can reduce latency by 2-3x for compatible model pairs without sacrificing quality.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "lmstudio",
	"lmStudioBaseUrl": "http://localhost:1234/v1",
	"lmStudioModelId": "local-model",
	"lmStudioDraftModelId": "local-model-draft",
	"lmStudioSpeculativeDecodingEnabled": true
}
```

**Example Configuration without Speculative Decoding**:

```json
{
	"id": "default",
	"provider": "lmstudio",
	"lmStudioBaseUrl": "http://localhost:1234/v1",
	"lmStudioModelId": "local-model",
	"lmStudioSpeculativeDecodingEnabled": false
}
```

**Default Model**: `local-model`

**Notes**:

- Requires LM Studio to be installed and running
- Download from https://lmstudio.ai/
- Supports various quantized models
- **Speculative Decoding**: This advanced technique can dramatically improve inference speed (2-3x faster) by using a smaller "draft" model to predict tokens ahead of time, which are then verified by the main model in parallel. This works best when:
    - The draft model is from the same model family as the main model
    - The draft model is significantly smaller/faster (e.g., 7B draft for 70B main model)
    - Both models share the same tokenizer and vocabulary
- To use speculative decoding, you must:
    1. Set `lmStudioSpeculativeDecodingEnabled` to `true`
    2. Specify a compatible draft model in `lmStudioDraftModelId`
    3. Ensure both models are loaded in LM Studio

---

### vscode-lm

VSCode's built-in language model API.

**Description**: Use VSCode's native language model capabilities (e.g., GitHub Copilot).

**Required Fields**:

- `vsCodeLmModelSelector` (text): Model selector in format `vendor/family`

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "vscode-lm",
	"vsCodeLmModelSelector": {
		"vendor": "copilot",
		"family": "gpt-4o"
	}
}
```

**Default Model**: `copilot-gpt-4o`

**Notes**:

- Requires VSCode with language model support
- Typically used with GitHub Copilot subscription
- No separate API key needed

---

### openai

OpenAI API integration (alternative configuration).

**Description**: Alternative OpenAI integration with simplified configuration and support for Azure OpenAI Service.

**Required Fields**:

- `openAiApiKey` (password): Your OpenAI API key
- `openAiModelId` (text): The model to use (default: `gpt-4o`)

**Optional Fields**:

- `openAiBaseUrl` (text): Custom base URL for OpenAI API requests (leave empty for default). When using Azure OpenAI, set this to your Azure endpoint URL (e.g., `https://your-resource.openai.azure.com`)
- `openAiLegacyFormat` (boolean): Use legacy API format for compatibility with older OpenAI API versions. Enable this if you're using an older API version or a proxy that expects the legacy format.
- `openAiR1FormatEnabled` (boolean): Enable R1 format for reasoning models that support extended thinking capabilities. This format is optimized for models like o1 and o1-mini that perform chain-of-thought reasoning.
- `openAiUseAzure` (boolean): Use Azure OpenAI Service instead of standard OpenAI API. When enabled, ensure you set `openAiBaseUrl` to your Azure endpoint and `azureApiVersion` to a valid API version.
- `azureApiVersion` (string): Azure OpenAI API version (e.g., `2024-02-15-preview`, `2023-05-15`). Required when `openAiUseAzure` is true. See [Azure OpenAI API versions](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference) for available versions.
- `openAiStreamingEnabled` (boolean): Enable streaming responses for real-time token generation. When enabled, responses are streamed as they're generated rather than waiting for the complete response.
- `openAiHeaders` (object): Custom HTTP headers to include in OpenAI API requests. Useful for adding authentication headers, tracking headers, or other custom metadata. Example: `{"X-Custom-Header": "value", "X-Request-ID": "123"}`

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "openai",
	"openAiApiKey": "sk-...",
	"openAiModelId": "gpt-4o",
	"openAiBaseUrl": "",
	"openAiLegacyFormat": false,
	"openAiR1FormatEnabled": false,
	"openAiUseAzure": false,
	"azureApiVersion": "",
	"openAiStreamingEnabled": true,
	"openAiHeaders": {}
}
```

**Example Azure OpenAI Configuration**:

```json
{
	"id": "azure-openai",
	"provider": "openai",
	"openAiApiKey": "your-azure-api-key",
	"openAiModelId": "gpt-4",
	"openAiBaseUrl": "https://your-resource.openai.azure.com",
	"openAiUseAzure": true,
	"azureApiVersion": "2024-02-15-preview",
	"openAiStreamingEnabled": true
}
```

**Example Configuration with Custom Headers**:

```json
{
	"id": "openai-with-headers",
	"provider": "openai",
	"openAiApiKey": "sk-...",
	"openAiModelId": "gpt-4o",
	"openAiHeaders": {
		"X-Organization-ID": "org-123456",
		"X-Request-Source": "kilocode-cli"
	}
}
```

**Default Model**: `gpt-4o`

**Notes**:

- Similar to openai-native but with different configuration structure
- Supports all OpenAI models including GPT-4, GPT-4 Turbo, and GPT-3.5
- Azure OpenAI Service provides enterprise-grade security and compliance
- R1 format is specifically designed for reasoning models and may not work with standard chat models
- Streaming is recommended for better user experience with long responses
- Custom headers can be used for request tracking, authentication, or integration with proxy services

---

### glama

Glama AI platform.

**Description**: Access AI models through the Glama platform.

**Required Fields**:

- `glamaApiKey` (password): Your Glama API key
- `glamaModelId` (text): Model identifier (default: `llama-3.1-70b-versatile`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "glama",
	"glamaApiKey": "...",
	"glamaModelId": "llama-3.1-70b-versatile"
}
```

**Default Model**: `llama-3.1-70b-versatile`

---

### huggingface

HuggingFace Inference API.

**Description**: Access models hosted on HuggingFace's inference infrastructure.

**Required Fields**:

- `huggingFaceApiKey` (password): Your HuggingFace API token
- `huggingFaceModelId` (text): Model identifier (default: `meta-llama/Llama-2-70b-chat-hf`)
- `huggingFaceInferenceProvider` (text): Inference provider (default: `auto`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "huggingface",
	"huggingFaceApiKey": "hf_...",
	"huggingFaceModelId": "meta-llama/Llama-2-70b-chat-hf",
	"huggingFaceInferenceProvider": "auto"
}
```

**Default Model**: `meta-llama/Llama-2-70b-chat-hf`

**Notes**:

- Get your token from https://huggingface.co/settings/tokens
- Supports thousands of models from the HuggingFace Hub
- Inference provider can be `auto`, `hf-inference`, or specific endpoints

---

### litellm

LiteLLM proxy for unified model access.

**Description**: Use LiteLLM as a proxy to access multiple AI providers through a unified interface.

**Required Fields**:

- `litellmBaseUrl` (text): LiteLLM proxy URL
- `litellmApiKey` (password): API key for the proxy
- `litellmModelId` (text): Model identifier (default: `gpt-4o`)

**Optional Fields**:

- `litellmUsePromptCache` (boolean): Enable prompt caching to reduce costs and improve performance for repeated prompts. When enabled, LiteLLM caches portions of your prompts that are reused across requests, significantly reducing both API costs and response times for subsequent requests with similar context. This is particularly beneficial for applications with repeated system prompts, documentation, or other static context.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "litellm",
	"litellmBaseUrl": "http://localhost:8000",
	"litellmApiKey": "...",
	"litellmModelId": "gpt-4o",
	"litellmUsePromptCache": true
}
```

**Example Configuration without Prompt Caching**:

```json
{
	"id": "litellm-no-cache",
	"provider": "litellm",
	"litellmBaseUrl": "http://localhost:8000",
	"litellmApiKey": "...",
	"litellmModelId": "gpt-4o",
	"litellmUsePromptCache": false
}
```

**Default Model**: `gpt-4o`

**Notes**:

- Requires LiteLLM proxy to be running
- See https://docs.litellm.ai/ for setup
- Supports 100+ LLM providers through a single interface
- **Prompt Caching**: Significantly reduces costs for applications with repeated context. The first request pays full price, but subsequent requests with cached content can be up to 90% cheaper and respond faster. This feature is especially useful when:
    - Using the same system prompts across multiple requests
    - Processing documents or code with similar context
    - Building applications with consistent instruction sets
    - Working with large context windows that remain mostly unchanged
- Prompt caching availability and pricing may vary depending on the underlying provider being accessed through LiteLLM

---

### moonshot

Moonshot AI platform.

**Description**: Access Moonshot AI's language models.

**Required Fields**:

- `moonshotBaseUrl` (text): Moonshot API base URL (default: `https://api.moonshot.ai/v1`)
- `moonshotApiKey` (password): Your Moonshot API key
- `apiModelId` (text): The model to use (default: `kimi-k2-0711-preview`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "moonshot",
	"moonshotBaseUrl": "https://api.moonshot.ai/v1",
	"moonshotApiKey": "...",
	"apiModelId": "kimi-k2-0711-preview"
}
```

**Default Model**: `kimi-k2-0711-preview`

---

### doubao

Doubao AI platform.

**Description**: Access Doubao's AI models.

**Required Fields**:

- `doubaoApiKey` (password): Your Doubao API key
- `apiModelId` (text): The model to use (default: `doubao-seed-1-6-250615`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "doubao",
	"doubaoApiKey": "...",
	"apiModelId": "doubao-seed-1-6-250615"
}
```

**Default Model**: `doubao-seed-1-6-250615`

---

### chutes

Chutes AI platform.

**Description**: Access AI models through the Chutes platform.

**Required Fields**:

- `chutesApiKey` (password): Your Chutes API key
- `apiModelId` (text): The model to use (default: `deepseek-ai/DeepSeek-R1-0528`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "chutes",
	"chutesApiKey": "...",
	"apiModelId": "deepseek-ai/DeepSeek-R1-0528"
}
```

**Default Model**: `deepseek-ai/DeepSeek-R1-0528`

---

### sambanova

SambaNova AI inference platform.

**Description**: Use SambaNova's AI inference platform for fast model execution.

**Required Fields**:

- `sambaNovaApiKey` (password): Your SambaNova API key
- `apiModelId` (text): The model to use (default: `Meta-Llama-3.1-70B-Instruct`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "sambanova",
	"sambaNovaApiKey": "...",
	"apiModelId": "Meta-Llama-3.1-70B-Instruct"
}
```

**Default Model**: `Meta-Llama-3.1-70B-Instruct`

---

### fireworks

Fireworks AI platform.

**Description**: Access models through Fireworks AI's fast inference platform.

**Required Fields**:

- `fireworksApiKey` (password): Your Fireworks API key
- `apiModelId` (text): The model to use (default: `accounts/fireworks/models/kimi-k2-instruct-0905`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "fireworks",
	"fireworksApiKey": "...",
	"apiModelId": "accounts/fireworks/models/kimi-k2-instruct-0905"
}
```

**Default Model**: `accounts/fireworks/models/kimi-k2-instruct-0905`

**Notes**:

- Get your API key from https://fireworks.ai/
- Known for fast inference speeds

---

### featherless

Featherless AI platform.

**Description**: Access AI models through the Featherless platform.

**Required Fields**:

- `featherlessApiKey` (password): Your Featherless API key
- `apiModelId` (text): The model to use (default: `deepseek-ai/DeepSeek-V3-0324`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "featherless",
	"featherlessApiKey": "...",
	"apiModelId": "deepseek-ai/DeepSeek-V3-0324"
}
```

**Default Model**: `deepseek-ai/DeepSeek-V3-0324`

---

### deepinfra

DeepInfra's serverless AI inference.

**Description**: Use DeepInfra for serverless access to various AI models.

**Required Fields**:

- `deepInfraApiKey` (password): Your DeepInfra API key
- `deepInfraModelId` (text): Model identifier (default: `meta-llama/Meta-Llama-3.1-70B-Instruct`)

**Optional Fields**:

- `deepInfraBaseUrl` (text): Custom base URL for DeepInfra API requests. Use this when you need to connect to a different DeepInfra endpoint or a custom proxy. Leave empty to use the default DeepInfra API URL (`https://api.deepinfra.com/v1/openai`).

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "deepinfra",
	"deepInfraApiKey": "...",
	"deepInfraModelId": "meta-llama/Meta-Llama-3.1-70B-Instruct"
}
```

**Example Configuration with Custom Base URL**:

```json
{
	"id": "deepinfra-custom",
	"provider": "deepinfra",
	"deepInfraApiKey": "...",
	"deepInfraModelId": "meta-llama/Meta-Llama-3.1-70B-Instruct",
	"deepInfraBaseUrl": "https://custom-endpoint.deepinfra.com/v1/openai"
}
```

**Default Model**: `meta-llama/Meta-Llama-3.1-70B-Instruct`

**Notes**:

- Get your API key from https://deepinfra.com/
- Supports many open-source models
- The custom base URL is useful when:
    - Using a proxy or gateway for DeepInfra requests
    - Connecting to a self-hosted DeepInfra-compatible endpoint
    - Testing against a staging or development environment

---

### io-intelligence

IO Intelligence platform.

**Description**: Access AI models through the IO Intelligence platform.

**Required Fields**:

- `ioIntelligenceApiKey` (password): Your IO Intelligence API key
- `ioIntelligenceModelId` (text): Model identifier (default: `gpt-4o`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "io-intelligence",
	"ioIntelligenceApiKey": "...",
	"ioIntelligenceModelId": "gpt-4o"
}
```

**Default Model**: `gpt-4o`

---

### qwen-code

Qwen Code AI models.

**Description**: Access Qwen's code-specialized models using OAuth authentication.

**Required Fields**:

- `qwenCodeOauthPath` (text): Path to OAuth credentials file (default: `~/.qwen/oauth_creds.json`)
- `apiModelId` (text): The model to use (default: `qwen3-coder-plus`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "qwen-code",
	"qwenCodeOauthPath": "~/.qwen/oauth_creds.json",
	"apiModelId": "qwen3-coder-plus"
}
```

**Default Model**: `qwen3-coder-plus`

**Notes**:

- Requires OAuth credentials file
- Optimized for code generation tasks

---

### gemini-cli

Gemini CLI integration.

**Description**: Use Google's Gemini models through CLI with OAuth authentication.

**Required Fields**:

- `geminiCliOAuthPath` (text): Path to OAuth credentials file (default: `~/.gemini/oauth_creds.json`)
- `geminiCliProjectId` (text): Google Cloud project ID
- `apiModelId` (text): The model to use (default: `gemini-2.5-flash-preview-04-17`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "gemini-cli",
	"geminiCliOAuthPath": "~/.gemini/oauth_creds.json",
	"geminiCliProjectId": "my-project-123",
	"apiModelId": "gemini-2.5-flash-preview-04-17"
}
```

**Default Model**: `gemini-2.5-flash-preview-04-17`

**Notes**:

- Requires OAuth credentials file
- Requires Google Cloud project

---

### zai

ZAI AI platform.

**Description**: Access AI models through the ZAI platform with support for both international and China-based API endpoints.

**Required Fields**:

- `zaiApiKey` (password): Your ZAI API key
- `zaiApiLine` (text): API line identifier (default: `international_coding`)
- `apiModelId` (text): The model to use (default: `glm-4.6`)

**Available API Lines**:

The `zaiApiLine` parameter determines which API endpoint and region to use:

- `international_coding` (default): International Coding Plan

    - Base URL: `https://api.z.ai/api/coding/paas/v4`
    - Region: International
    - Optimized for coding tasks

- `international`: International Standard

    - Base URL: `https://api.z.ai/api/paas/v4`
    - Region: International
    - General-purpose API

- `china_coding`: China Coding Plan

    - Base URL: `https://open.bigmodel.cn/api/coding/paas/v4`
    - Region: China
    - Optimized for coding tasks

- `china`: China Standard
    - Base URL: `https://open.bigmodel.cn/api/paas/v4`
    - Region: China
    - General-purpose API

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "zai",
	"zaiApiKey": "...",
	"zaiApiLine": "international_coding",
	"apiModelId": "glm-4.6"
}
```

**Default Model**: `glm-4.6`

**Notes**:

- Choose the API line based on your geographic location and use case
- Coding-optimized lines provide better performance for code generation tasks
- China-based lines may offer better latency for users in mainland China

---

### unbound

Unbound AI platform.

**Description**: Access AI models through the Unbound platform.

**Required Fields**:

- `unboundApiKey` (password): Your Unbound API key
- `unboundModelId` (text): Model identifier (default: `gpt-4o`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "unbound",
	"unboundApiKey": "...",
	"unboundModelId": "gpt-4o"
}
```

**Default Model**: `gpt-4o`

---

### requesty

Requesty AI platform.

**Description**: Access AI models through the Requesty platform.

**Required Fields**:

- `requestyApiKey` (password): Your Requesty API key
- `requestyModelId` (text): Model identifier (default: `gpt-4o`)

**Optional Fields**:

- `requestyBaseUrl` (text): Custom base URL (leave empty for default)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "requesty",
	"requestyApiKey": "...",
	"requestyBaseUrl": "",
	"requestyModelId": "gpt-4o"
}
```

**Default Model**: `gpt-4o`

---

### roo

Roo AI platform.

**Description**: Access AI models through the Roo platform.

**Required Fields**:

- `apiModelId` (text): Model identifier (default: `deepseek-ai/DeepSeek-R1-0528`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "roo",
	"apiModelId": "deepseek-ai/DeepSeek-R1-0528"
}
```

**Default Model**: `deepseek-ai/DeepSeek-R1-0528`

**Notes**:

- No API key required
- Configuration may vary based on platform setup

---

### vercel-ai-gateway

Vercel AI Gateway for unified model access.

**Description**: Use Vercel's AI Gateway to access multiple AI providers.

**Required Fields**:

- `vercelAiGatewayApiKey` (password): Your Vercel AI Gateway API key
- `vercelAiGatewayModelId` (text): Model identifier (default: `gpt-4o`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "vercel-ai-gateway",
	"vercelAiGatewayApiKey": "...",
	"vercelAiGatewayModelId": "gpt-4o"
}
```

**Default Model**: `gpt-4o`

**Notes**:

- Requires Vercel account with AI Gateway enabled
- Provides unified access to multiple AI providers

---

### virtual-quota-fallback

Virtual quota management with automatic fallback.

**Description**: Manage multiple provider profiles with automatic fallback when quotas are exceeded.

**Required Fields**:

- `profiles` (text): Array of provider profiles with quota configurations

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "virtual-quota-fallback",
	"profiles": [
		{
			"provider": "anthropic",
			"quota": 1000000,
			"config": {
				"apiKey": "...",
				"apiModelId": "claude-3-5-sonnet-20241022"
			}
		},
		{
			"provider": "openai",
			"quota": 500000,
			"config": {
				"openAiApiKey": "...",
				"apiModelId": "gpt-4o"
			}
		}
	]
}
```

**Default Model**: `gpt-4o`

**Notes**:

- Automatically switches to fallback providers when quota is exceeded
- Useful for managing costs and ensuring availability
- Each profile can have its own quota and configuration

---

### human-relay

Human-in-the-loop relay for manual responses.

**Description**: Route requests to a human operator for manual responses.

**Required Fields**:

- `apiModelId` (text): Model identifier (fixed value: `human`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "human-relay",
	"apiModelId": "human"
}
```

**Default Model**: `human`

**Notes**:

- Used for testing or scenarios requiring human intervention
- No AI model is actually called

---

### fake-ai

Fake AI provider for testing and development.

**Description**: Mock AI provider for testing purposes without making actual API calls.

**Required Fields**:

- `apiModelId` (text): Model identifier (fixed value: `fake-model`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "fake-ai",
	"apiModelId": "fake-model"
}
```

**Default Model**: `fake-model`

**Notes**:

- Used for testing and development
- Returns mock responses without calling any actual AI service
- Useful for integration testing

---

### OVHcloud AI Endpoints

OVHcloud AI Endpoints inference provider.

**Description**: Use OVHcloud leading cloud computing for accessing various open-source models, with GDPR compliance and data sovreignty.

**Required Field**:

- `ovhCloudAiEndpointsModelId` (text): Model identifier (default: `gpt-oss-120b`)

**Optional Fields**:

- `ovhCloudAiEndpointsApiKey` (password): Your OVHcloud AI Endpoints API key
  If you do not provide the API key, you can use our service for free with a rate limit.
- `ovhCloudAiEndpointsBaseUrl` (text): Custom base URL for OVHcloud AI Endpoints API requests. Use this when you need to connect to a different OVHcloud region or a custom endpoint. Leave empty to use the default OVHcloud AI Endpoints URL.

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "ovhcloud",
	"ovhCloudAiEndpointsApiKey": "your-api-key",
	"ovhCloudAiEndpointsModelId": "gpt-oss-120b"
}
```

**Example Configuration with Custom Base URL**:

```json
{
	"id": "ovhcloud-custom",
	"provider": "ovhcloud",
	"ovhCloudAiEndpointsApiKey": "your-api-key",
	"ovhCloudAiEndpointsModelId": "gpt-oss-120b",
	"ovhCloudAiEndpointsBaseUrl": "https://custom-endpoint.ovhcloud.com/v1"
}
```

**Default Model**: `gpt-oss-120b`

**Notes**:

- Get your API key from https://ovh.com/manager in `Public Cloud > AI & Machine Learning` section, then in `AI Endpoints`.
- You can browse our [catalog](https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/) to discover all of our models.
- The custom base URL is useful when:
    - Connecting to a specific OVHcloud region for lower latency
    - Using a private or VPC endpoint for enhanced security
    - Testing against a staging or development environment

---

### inception

Inception Labs AI platform.

**Description**: Access AI models through the Inception Labs platform.

**Required Fields**:

- `inceptionLabsApiKey` (password): Your Inception Labs API key
- `inceptionLabsModelId` (text): Model identifier (default: `gpt-4o`)

**Optional Fields**:

- `inceptionLabsBaseUrl` (text): Custom base URL (leave empty for default)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "inception",
	"inceptionLabsApiKey": "...",
	"inceptionLabsModelId": "gpt-4o",
	"inceptionLabsBaseUrl": ""
}
```

**Default Model**: `gpt-4o`

**Notes**:

- Get your API key from Inception Labs platform
- Supports various AI models

---

### synthetic

Synthetic AI provider.

**Description**: Access AI models through the Synthetic platform.

**Required Fields**:

- `syntheticApiKey` (password): Your Synthetic API key
- `apiModelId` (text): Model identifier (default: `synthetic-model`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "synthetic",
	"syntheticApiKey": "...",
	"apiModelId": "synthetic-model"
}
```

**Default Model**: `synthetic-model`

---

### minimax

MiniMax AI platform.

**Description**: Access MiniMax's AI models.

**Required Fields**:

- `minimaxApiKey` (password): Your MiniMax API key
- `minimaxBaseUrl` (text): MiniMax API base URL (default: `https://api.minimax.io/anthropic`)
- `apiModelId` (text): The model to use (default: `MiniMax-M2`)

**Example Configuration**:

```json
{
	"id": "default",
	"provider": "minimax",
	"minimaxBaseUrl": "https://api.minimax.io/anthropic",
	"minimaxApiKey": "...",
	"apiModelId": "MiniMax-M2"
}
```

**Default Model**: `MiniMax-M2`

**Notes**:

- Supports both `.io` and `.com` domains
- Uses Anthropic-compatible API format

---

## Additional Resources

- [Kilo Code Documentation](https://docs.kilocode.com/)
- [Configuration Schema](../src/config/schema.json)

## Support

For issues or questions about provider configuration:

- Open an issue on [GitHub](https://github.com/kilo-org/kilocode)
- Join our [Discord community](https://discord.gg/kilocode)
- Check the [FAQ](https://docs.kilocode.com/faq)
