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Providers Configuration

AiderDesk supports multiple Large Language Model (LLM) providers to power your AI coding assistant. You can configure these providers in the Model Library (accessible via the top bar icon). Each provider has specific configuration requirements, and most support environment variables for secure credential management.

Table of Contents​

Anthropic​

Anthropic provides powerful AI models like Claude that excel at coding and reasoning tasks.

Configuration Parameters​

  • API Key: Your Anthropic API key for authentication

Setup​

  1. Go to Anthropic Console
  2. Create a new API key
  3. Enter the API key in the Model Library Anthropic configuration
  4. Or set the ANTHROPIC_API_KEY environment variable

Azure​

Azure OpenAI provides enterprise-grade AI models with enhanced security, compliance, and regional deployment options.

Configuration Parameters​

  • API Key: Your Azure OpenAI API key for authentication
    • Environment variable: AZURE_API_KEY
    • Get your API key from Azure Portal
  • Resource Name: Your Azure OpenAI resource name
    • Environment variable: AZURE_RESOURCE_NAME
    • Found in your Azure OpenAI resource overview page
  • API Version: The API version to use
    • Environment variable: AZURE_API_VERSION
    • Default: 2025-01-01-preview

Setup​

  1. Go to Azure Portal and create an Azure OpenAI resource
  2. Navigate to your resource and find the Keys and Endpoint section
  3. Copy your API key and resource name
  4. Enter the API key, resource name, and optionally API version in the Model Library Azure configuration
  5. Or set the appropriate environment variables

Important Notes​

  • Custom Models Required: Azure models are not automatically discovered. You need to add custom models manually through the Model Library
  • Resource Name Format: Use only the resource name (e.g., my-openai-resource), not the full endpoint URL
  • Regional Deployment: Models are deployed to specific Azure regions, ensure your resource is in the desired region

OpenAI​

OpenAI provides advanced language models including GPT-4 series with enhanced reasoning capabilities.

Configuration Parameters​

  • API Key: Your OpenAI API key for authentication
    • Environment variable: OPENAI_API_KEY
    • Get your API key from OpenAI API Keys
  • Reasoning Effort: Control the level of reasoning for supported models
    • Low: Minimal reasoning, faster responses
    • Medium: Balanced reasoning and speed (default)
    • High: Maximum reasoning, more thorough but slower

Setup​

  1. Go to OpenAI API Keys
  2. Create a new API key
  3. Enter the API key in the Model Library OpenAI configuration
  4. Configure the Reasoning Effort based on your needs
  5. Or set the OPENAI_API_KEY environment variable

Gemini​

Google's Gemini models offer versatile AI capabilities with advanced features like thinking budgets and search grounding.

Configuration Parameters​

  • API Key: Your Gemini API key for authentication
  • Custom Base URL: Optional custom endpoint URL
    • Environment variable: GEMINI_API_BASE_URL
  • Thinking Budget: Maximum tokens for internal reasoning (0-24576)
  • Include Thoughts: Enable to see the model's internal reasoning process
  • Use Search Grounding: Enable to allow the model to use Google Search for factual grounding

Setup​

  1. Go to Google AI Studio
  2. Create a new API key
  3. Enter the API key in the Model Library Gemini configuration
  4. Configure optional parameters based on your needs
  5. Or set appropriate environment variables

Vertex AI​

Google Cloud's Vertex AI provides enterprise-grade AI models with advanced configuration options.

Configuration Parameters​

  • Project: Your Google Cloud project ID
  • Location: The region/zone where your Vertex AI resources are located
  • Google Cloud Credentials JSON: Service account credentials in JSON format
  • Thinking Budget: Maximum tokens for internal reasoning (0-24576)
  • Include Thoughts: Enable to see the model's internal reasoning process

Setup​

  1. Create a Google Cloud project if you don't have one
  2. Enable the Vertex AI API
  3. Create a service account with Vertex AI permissions
  4. Download the service account credentials JSON
  5. Enter the project ID, location, and credentials in the Model Library Vertex AI configuration
  6. Configure thinking budget and thoughts inclusion as needed

Deepseek​

Deepseek provides powerful AI models optimized for coding and technical tasks.

Configuration Parameters​

  • API Key: Your Deepseek API key for authentication

Setup​

  1. Go to Deepseek Platform
  2. Create a new API key
  3. Enter the API key in the Model Library Deepseek configuration
  4. Or set the DEEPSEEK_API_KEY environment variable

Groq​

Groq offers ultra-fast inference with specialized hardware acceleration.

Configuration Parameters​

  • API Key: Your Groq API key for authentication
    • Environment variable: GROQ_API_KEY
    • Get your API key from Groq Console
  • Models: List of available models to use (comma-separated)

Setup​

  1. Go to Groq Console
  2. Create a new API key
  3. Enter the API key in the Model Library Groq configuration
  4. Add the models you want to use (e.g., llama3-70b-8192, mixtral-8x7b-32768)
  5. Or set the GROQ_API_KEY environment variable

Bedrock​

Amazon Bedrock provides access to foundation models from leading AI companies through AWS.

Configuration Parameters​

  • Region: AWS region where Bedrock is available
    • Environment variable: AWS_REGION
    • Default: us-east-1
  • Access Key ID: Your AWS access key ID
    • Environment variable: AWS_ACCESS_KEY_ID
  • Secret Access Key: Your AWS secret access key
    • Environment variable: AWS_SECRET_ACCESS_KEY
  • Session Token: Optional temporary session token
    • Environment variable: AWS_SESSION_TOKEN

Setup​

  1. Ensure you have an AWS account with appropriate permissions
  2. Enable Bedrock in your desired AWS region
  3. Create an IAM user with Bedrock access permissions
  4. Enter the AWS credentials in the Model Library Bedrock configuration
  5. Or set the appropriate AWS environment variables

OpenAI Compatible​

Configure any OpenAI-compatible API endpoint to use custom models or self-hosted solutions.

Configuration Parameters​

  • Base URL: The API endpoint URL
    • Environment variable: OPENAI_API_BASE
  • API Key: Your API key for the compatible service
    • Environment variable: OPENAI_API_KEY
  • Models: List of available models (comma-separated)

Setup​

  1. Obtain the base URL and API key from your OpenAI-compatible service provider
  2. Enter the base URL and API key in the Model Library OpenAI Compatible configuration
  3. Or set the OPENAI_API_BASE and OPENAI_API_KEY environment variables
  4. Use openai-compatible/ prefix in the model selector

Important Notes​

  • Unified Prefix: Both Agent and Aider modes use the same openai-compatible/ prefix
  • Model Library: Use the Model Library for advanced configuration and custom model management
  • API Compatibility: Configure all settings in the Model Library for unified experience across all modes

Ollama​

Ollama allows you to run open-source models locally on your machine.

Configuration Parameters​

  • Base URL: Your Ollama server endpoint
    • Environment variable: OLLAMA_API_BASE
    • Default: http://localhost:11434

Setup​

  1. Install and run Ollama on your local machine
  2. Ensure Ollama is running and accessible
  3. Enter the base URL in the Model Library Ollama configuration
  4. Or set the OLLAMA_API_BASE environment variable

LM Studio​

LM Studio provides a user-friendly interface for running local language models.

Configuration Parameters​

  • Base URL: Your LM Studio server endpoint
    • Environment variable: LMSTUDIO_API_BASE
    • Default: http://localhost:1234

Setup​

  1. Install and run LM Studio on your local machine
  2. Start a local server in LM Studio
  3. Enter the base URL in the Model Library LM Studio configuration
  4. Or set the LMSTUDIO_API_BASE environment variable

OpenRouter​

OpenRouter provides access to multiple models from various providers through a single API.

Configuration Parameters​

  • API Key: Your OpenRouter API key for authentication
    • Environment variable: OPENROUTER_API_KEY
    • Get your API key from OpenRouter Keys
  • Models: List of models to use (auto-populated when API key is provided)
  • Advanced Settings: Additional configuration options:
    • Require Parameters: Enforce parameter requirements
    • Order: Model preference order
    • Only: Restrict to specific models
    • Ignore: Exclude specific models
    • Allow Fallbacks: Enable model fallback
    • Data Collection: Allow or deny data collection
    • Quantizations: Preferred quantization levels
    • Sort: Sort models by price or throughput

Setup​

  1. Go to OpenRouter Keys
  2. Create a new API key
  3. Enter the API key in the Model Library OpenRouter configuration
  4. Select your preferred models from the auto-populated list
  5. Configure advanced settings as needed
  6. Or set the OPENROUTER_API_KEY environment variable

Requesty​

Requesty provides optimized model routing and caching for improved performance and cost efficiency.

Configuration Parameters​

  • API Key: Your Requesty API key for authentication
  • Models: List of available models (auto-populated when API key is provided)
  • Auto Cache: Enable automatic response caching for improved performance
  • Reasoning Effort: Control the level of reasoning for supported models
    • None: No reasoning
    • Low: Minimal reasoning
    • Medium: Balanced reasoning
    • High: Enhanced reasoning
    • Max: Maximum reasoning

Setup​

  1. Go to Requesty API Keys
  2. Create a new API key
  3. Enter the API key in the Model Library Requesty configuration
  4. Select your preferred models from the auto-populated list
  5. Configure auto cache and reasoning effort as needed
  6. Or set the REQUESTY_API_KEY environment variable
  7. Use requesty/ prefix in the model selector

Important Notes​

  • Unified Prefix: Both Agent and Aider modes use the same requesty/ prefix
  • Model Library: Use the Model Library for advanced configuration and custom model management
  • API Compatibility: Configure all settings in the Providers section for unified experience across all modes

Model Library Integration​

The Model Library provides advanced provider and model management capabilities beyond basic provider configuration:

  • Multiple Profiles: Create multiple profiles for the same provider (e.g., work and personal OpenAI accounts)
  • Custom Models: Add custom models that aren't automatically discovered (e.g., Azure models)
  • Cost Configuration: Set custom pricing and token limits for models
  • Model Management: Hide irrelevant models, organize by provider profiles
  • Advanced Configuration: Configure multiple OpenAI-compatible providers with different prefixes

For comprehensive provider and model management, see Model Library.

Unified Model Prefix System​

AiderDesk now uses a unified model prefix system across all modes (Agent, Code, Ask, Architect, Context):

ProviderModel Prefix
Anthropicanthropic/
OpenAIopenai/
Azureazure/
Geminigemini/
Vertex AIvertex_ai/
Deepseekdeepseek/
Groqgroq/
Bedrockbedrock/
OpenAI Compatibleopenai-compatible/
Ollamaollama/
LM Studiolmstudio/
OpenRouteropenrouter/
Requestyrequesty/

Important Notes​

  • Unified Configuration: Configure all providers in the Model Library for consistent behavior across all modes
  • Model Selection: Use the same model prefix regardless of the mode you're using
  • Environment Variables: Environment variables are supported as fallbacks but primary configuration is through the Model Library
  • Model Library: For advanced management of multiple profiles and custom models, use the Model Library