claude-task-master/docs/configuration.md

Configuration

Taskmaster uses two primary methods for configuration:

1. .taskmasterconfig File (Project Root - Recommended for most settings)

   • This JSON file stores most configuration settings, including AI model selections, parameters, logging levels, and project defaults.
   • Location: This file is created in the root directory of your project when you run the task-master models --setup interactive setup. You typically do this during the initialization sequence. Do not manually edit this file beyond adjusting Temperature and Max Tokens depending on your model.
   • Management: Use the task-master models --setup command (or models MCP tool) to interactively create and manage this file. You can also set specific models directly using task-master models --set-<role>=<model_id>, adding --ollama or --openrouter flags for custom models. Manual editing is possible but not recommended unless you understand the structure.
   • Example Structure:

     {
     	"models": {
     		"main": {
     			"provider": "anthropic",
     			"modelId": "claude-3-7-sonnet-20250219",
     			"maxTokens": 64000,
     			"temperature": 0.2,
     			"baseURL": "https://api.anthropic.com/v1"
     		},
     		"research": {
     			"provider": "perplexity",
     			"modelId": "sonar-pro",
     			"maxTokens": 8700,
     			"temperature": 0.1,
     			"baseURL": "https://api.perplexity.ai/v1"
     		},
     		"fallback": {
     			"provider": "anthropic",
     			"modelId": "claude-3-5-sonnet",
     			"maxTokens": 64000,
     			"temperature": 0.2
     		}
     	},
     	"global": {
     		"logLevel": "info",
     		"debug": false,
     		"defaultSubtasks": 5,
     		"defaultPriority": "medium",
     		"projectName": "Your Project Name",
     		"ollamaBaseURL": "http://localhost:11434/api",
     		"azureBaseURL": "https://your-endpoint.azure.com/",
     		"vertexProjectId": "your-gcp-project-id",
     		"vertexLocation": "us-central1"
     	}
     }
     

2. Environment Variables (.env file or MCP env block - For API Keys Only)

   • Used exclusively for sensitive API keys and specific endpoint URLs.
   • Location:
     • For CLI usage: Create a .env file in your project root.
     • For MCP/Cursor usage: Configure keys in the env section of your .cursor/mcp.json file.
   • Required API Keys (Depending on configured providers):
     • ANTHROPIC_API_KEY: Your Anthropic API key.
     • PERPLEXITY_API_KEY: Your Perplexity API key.
     • OPENAI_API_KEY: Your OpenAI API key.
     • GOOGLE_API_KEY: Your Google API key (also used for Vertex AI provider).
     • MISTRAL_API_KEY: Your Mistral API key.
     • AZURE_OPENAI_API_KEY: Your Azure OpenAI API key (also requires AZURE_OPENAI_ENDPOINT).
     • OPENROUTER_API_KEY: Your OpenRouter API key.
     • XAI_API_KEY: Your X-AI API key.
   • Optional Endpoint Overrides:
     • Per-role baseURL in .taskmasterconfig: You can add a baseURL property to any model role (main, research, fallback) to override the default API endpoint for that provider. If omitted, the provider's standard endpoint is used.
     • AZURE_OPENAI_ENDPOINT: Required if using Azure OpenAI key (can also be set as baseURL for the Azure model role).
     • OLLAMA_BASE_URL: Override the default Ollama API URL (Default: http://localhost:11434/api).
     • VERTEX_PROJECT_ID: Your Google Cloud project ID for Vertex AI. Required when using the 'vertex' provider.
     • VERTEX_LOCATION: Google Cloud region for Vertex AI (e.g., 'us-central1'). Default is 'us-central1'.
     • GOOGLE_APPLICATION_CREDENTIALS: Path to service account credentials JSON file for Google Cloud auth (alternative to API key for Vertex AI).

Important: Settings like model ID selections (main, research, fallback), maxTokens, temperature, logLevel, defaultSubtasks, defaultPriority, and projectName are managed in .taskmasterconfig, not environment variables.

Example .env File (for API Keys)

# Required API keys for providers configured in .taskmasterconfig
ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
PERPLEXITY_API_KEY=pplx-your-key-here
# OPENAI_API_KEY=sk-your-key-here
# GOOGLE_API_KEY=AIzaSy...
# etc.

# Optional Endpoint Overrides
# AZURE_OPENAI_ENDPOINT=https://your-azure-endpoint.openai.azure.com/
# OLLAMA_BASE_URL=http://custom-ollama-host:11434/api

# Google Vertex AI Configuration (Required if using 'vertex' provider)
# VERTEX_PROJECT_ID=your-gcp-project-id
# VERTEX_LOCATION=us-central1
# GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-credentials.json

Troubleshooting

Configuration Errors

• If Task Master reports errors about missing configuration or cannot find .taskmasterconfig, run task-master models --setup in your project root to create or repair the file.
• Ensure API keys are correctly placed in your .env file (for CLI) or .cursor/mcp.json (for MCP) and are valid for the providers selected in .taskmasterconfig.

If task-master init doesn't respond:

Try running it with Node directly:

node node_modules/claude-task-master/scripts/init.js

Or clone the repository and run:

git clone https://github.com/eyaltoledano/claude-task-master.git
cd claude-task-master
node scripts/init.js

Provider-Specific Configuration

Google Vertex AI Configuration

Google Vertex AI is Google Cloud's enterprise AI platform and requires specific configuration:

1. Prerequisites:

   • A Google Cloud account with Vertex AI API enabled
   • Either a Google API key with Vertex AI permissions OR a service account with appropriate roles
   • A Google Cloud project ID

2. Authentication Options:

   • API Key: Set the GOOGLE_API_KEY environment variable
   • Service Account: Set GOOGLE_APPLICATION_CREDENTIALS to point to your service account JSON file

3. Required Configuration:

   • Set VERTEX_PROJECT_ID to your Google Cloud project ID
   • Set VERTEX_LOCATION to your preferred Google Cloud region (default: us-central1)

4. Example Setup:

   # In .env file
   GOOGLE_API_KEY=AIzaSyXXXXXXXXXXXXXXXXXXXXXXXXX
   VERTEX_PROJECT_ID=my-gcp-project-123
   VERTEX_LOCATION=us-central1
   

   Or using service account:

   # In .env file
   GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
   VERTEX_PROJECT_ID=my-gcp-project-123
   VERTEX_LOCATION=us-central1
   

5. In .taskmasterconfig:

   "global": {
     "vertexProjectId": "my-gcp-project-123",
     "vertexLocation": "us-central1"
   }