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c99df64f65
* feat: Support custom response language * fix: Add default values for response language in config-manager.js * chore: Update configuration file and add default response language settings * feat: Support MCP/CLI custom response language * chore: Update test comments to English for consistency * docs: Auto-update and format models.md * chore: fix format --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Ralph Khreish <35776126+Crunchyman-ralph@users.noreply.github.com>
316 lines
13 KiB
Markdown
316 lines
13 KiB
Markdown
# Configuration
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Taskmaster uses two primary methods for configuration:
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1. **`.taskmaster/config.json` File (Recommended - New Structure)**
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- This JSON file stores most configuration settings, including AI model selections, parameters, logging levels, and project defaults.
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- **Location:** This file is created in the `.taskmaster/` directory when you run the `task-master models --setup` interactive setup or initialize a new project with `task-master init`.
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- **Migration:** Existing projects with `.taskmasterconfig` in the root will continue to work, but should be migrated to the new structure using `task-master migrate`.
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- **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.
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- **Example Structure:**
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```json
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{
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"models": {
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"main": {
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"provider": "anthropic",
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"modelId": "claude-3-7-sonnet-20250219",
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"maxTokens": 64000,
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"temperature": 0.2,
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"baseURL": "https://api.anthropic.com/v1"
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},
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"research": {
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"provider": "perplexity",
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"modelId": "sonar-pro",
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"maxTokens": 8700,
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"temperature": 0.1,
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"baseURL": "https://api.perplexity.ai/v1"
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},
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"fallback": {
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"provider": "anthropic",
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"modelId": "claude-3-5-sonnet",
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"maxTokens": 64000,
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"temperature": 0.2
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}
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},
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"global": {
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"logLevel": "info",
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"debug": false,
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"defaultNumTasks": 10,
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"defaultSubtasks": 5,
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"defaultPriority": "medium",
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"defaultTag": "master",
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"projectName": "Your Project Name",
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"ollamaBaseURL": "http://localhost:11434/api",
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"azureBaseURL": "https://your-endpoint.azure.com/openai/deployments",
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"vertexProjectId": "your-gcp-project-id",
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"vertexLocation": "us-central1",
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"responseLanguage": "English"
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}
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}
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```
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2. **Legacy `.taskmasterconfig` File (Backward Compatibility)**
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- For projects that haven't migrated to the new structure yet.
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- **Location:** Project root directory.
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- **Migration:** Use `task-master migrate` to move this to `.taskmaster/config.json`.
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- **Deprecation:** While still supported, you'll see warnings encouraging migration to the new structure.
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## Environment Variables (`.env` file or MCP `env` block - For API Keys Only)
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- Used **exclusively** for sensitive API keys and specific endpoint URLs.
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- **Location:**
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- For CLI usage: Create a `.env` file in your project root.
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- For MCP/Cursor usage: Configure keys in the `env` section of your `.cursor/mcp.json` file.
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- **Required API Keys (Depending on configured providers):**
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- `ANTHROPIC_API_KEY`: Your Anthropic API key.
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- `PERPLEXITY_API_KEY`: Your Perplexity API key.
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- `OPENAI_API_KEY`: Your OpenAI API key.
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- `GOOGLE_API_KEY`: Your Google API key (also used for Vertex AI provider).
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- `MISTRAL_API_KEY`: Your Mistral API key.
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- `AZURE_OPENAI_API_KEY`: Your Azure OpenAI API key (also requires `AZURE_OPENAI_ENDPOINT`).
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- `OPENROUTER_API_KEY`: Your OpenRouter API key.
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- `XAI_API_KEY`: Your X-AI API key.
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- **Optional Endpoint Overrides:**
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- **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.
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- **Environment Variable Overrides (`<PROVIDER>_BASE_URL`):** For greater flexibility, especially with third-party services, you can set an environment variable like `OPENAI_BASE_URL` or `MISTRAL_BASE_URL`. This will override any `baseURL` set in the configuration file for that provider. This is the recommended way to connect to OpenAI-compatible APIs.
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- `AZURE_OPENAI_ENDPOINT`: Required if using Azure OpenAI key (can also be set as `baseURL` for the Azure model role).
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- `OLLAMA_BASE_URL`: Override the default Ollama API URL (Default: `http://localhost:11434/api`).
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- `VERTEX_PROJECT_ID`: Your Google Cloud project ID for Vertex AI. Required when using the 'vertex' provider.
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- `VERTEX_LOCATION`: Google Cloud region for Vertex AI (e.g., 'us-central1'). Default is 'us-central1'.
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- `GOOGLE_APPLICATION_CREDENTIALS`: Path to service account credentials JSON file for Google Cloud auth (alternative to API key for Vertex AI).
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**Important:** Settings like model ID selections (`main`, `research`, `fallback`), `maxTokens`, `temperature`, `logLevel`, `defaultSubtasks`, `defaultPriority`, and `projectName` are **managed in `.taskmaster/config.json`** (or `.taskmasterconfig` for unmigrated projects), not environment variables.
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## Tagged Task Lists Configuration (v0.17+)
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Taskmaster includes a tagged task lists system for multi-context task management.
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### Global Tag Settings
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```json
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"global": {
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"defaultTag": "master"
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}
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```
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- **`defaultTag`** (string): Default tag context for new operations (default: "master")
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### Git Integration
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Task Master provides manual git integration through the `--from-branch` option:
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- **Manual Tag Creation**: Use `task-master add-tag --from-branch` to create a tag based on your current git branch name
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- **User Control**: No automatic tag switching - you control when and how tags are created
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- **Flexible Workflow**: Supports any git workflow without imposing rigid branch-tag mappings
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## State Management File
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Taskmaster uses `.taskmaster/state.json` to track tagged system runtime information:
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```json
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{
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"currentTag": "master",
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"lastSwitched": "2025-06-11T20:26:12.598Z",
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"migrationNoticeShown": true
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}
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```
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- **`currentTag`**: Currently active tag context
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- **`lastSwitched`**: Timestamp of last tag switch
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- **`migrationNoticeShown`**: Whether migration notice has been displayed
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This file is automatically created during tagged system migration and should not be manually edited.
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## Example `.env` File (for API Keys)
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```
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# Required API keys for providers configured in .taskmaster/config.json
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ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
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PERPLEXITY_API_KEY=pplx-your-key-here
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# OPENAI_API_KEY=sk-your-key-here
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# GOOGLE_API_KEY=AIzaSy...
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# AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here
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# etc.
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# Optional Endpoint Overrides
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# Use a specific provider's base URL, e.g., for an OpenAI-compatible API
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# OPENAI_BASE_URL=https://api.third-party.com/v1
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#
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# Azure OpenAI Configuration
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# AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/ or https://your-endpoint-name.cognitiveservices.azure.com/openai/deployments
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# OLLAMA_BASE_URL=http://custom-ollama-host:11434/api
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# Google Vertex AI Configuration (Required if using 'vertex' provider)
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# VERTEX_PROJECT_ID=your-gcp-project-id
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```
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## Troubleshooting
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### Configuration Errors
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- If Task Master reports errors about missing configuration or cannot find the config file, run `task-master models --setup` in your project root to create or repair the file.
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- For new projects, config will be created at `.taskmaster/config.json`. For legacy projects, you may want to use `task-master migrate` to move to the new structure.
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- 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 your config file.
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### If `task-master init` doesn't respond:
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Try running it with Node directly:
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```bash
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node node_modules/claude-task-master/scripts/init.js
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```
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Or clone the repository and run:
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```bash
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git clone https://github.com/eyaltoledano/claude-task-master.git
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cd claude-task-master
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node scripts/init.js
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```
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## Provider-Specific Configuration
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### Google Vertex AI Configuration
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Google Vertex AI is Google Cloud's enterprise AI platform and requires specific configuration:
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1. **Prerequisites**:
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- A Google Cloud account with Vertex AI API enabled
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- Either a Google API key with Vertex AI permissions OR a service account with appropriate roles
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- A Google Cloud project ID
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2. **Authentication Options**:
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- **API Key**: Set the `GOOGLE_API_KEY` environment variable
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- **Service Account**: Set `GOOGLE_APPLICATION_CREDENTIALS` to point to your service account JSON file
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3. **Required Configuration**:
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- Set `VERTEX_PROJECT_ID` to your Google Cloud project ID
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- Set `VERTEX_LOCATION` to your preferred Google Cloud region (default: us-central1)
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4. **Example Setup**:
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```bash
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# In .env file
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GOOGLE_API_KEY=AIzaSyXXXXXXXXXXXXXXXXXXXXXXXXX
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VERTEX_PROJECT_ID=my-gcp-project-123
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VERTEX_LOCATION=us-central1
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```
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Or using service account:
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```bash
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# In .env file
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GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
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VERTEX_PROJECT_ID=my-gcp-project-123
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VERTEX_LOCATION=us-central1
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```
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5. **In .taskmaster/config.json**:
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```json
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"global": {
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"vertexProjectId": "my-gcp-project-123",
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"vertexLocation": "us-central1"
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}
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```
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### Azure OpenAI Configuration
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Azure OpenAI provides enterprise-grade OpenAI models through Microsoft's Azure cloud platform and requires specific configuration:
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1. **Prerequisites**:
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- An Azure account with an active subscription
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- Azure OpenAI service resource created in the Azure portal
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- Azure OpenAI API key and endpoint URL
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- Deployed models (e.g., gpt-4o, gpt-4o-mini, gpt-4.1, etc) in your Azure OpenAI resource
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2. **Authentication**:
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- Set the `AZURE_OPENAI_API_KEY` environment variable with your Azure OpenAI API key
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- Configure the endpoint URL using one of the methods below
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3. **Configuration Options**:
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**Option 1: Using Global Azure Base URL (affects all Azure models)**
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```json
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// In .taskmaster/config.json
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{
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"models": {
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"main": {
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"provider": "azure",
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"modelId": "gpt-4o",
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"maxTokens": 16000,
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"temperature": 0.7
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},
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"fallback": {
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"provider": "azure",
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"modelId": "gpt-4o-mini",
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"maxTokens": 10000,
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"temperature": 0.7
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}
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},
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"global": {
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"azureBaseURL": "https://your-resource-name.azure.com/openai/deployments"
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}
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}
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```
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**Option 2: Using Per-Model Base URLs (recommended for flexibility)**
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```json
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// In .taskmaster/config.json
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{
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"models": {
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"main": {
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"provider": "azure",
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"modelId": "gpt-4o",
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"maxTokens": 16000,
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"temperature": 0.7,
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"baseURL": "https://your-resource-name.azure.com/openai/deployments"
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},
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"research": {
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"provider": "perplexity",
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"modelId": "sonar-pro",
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"maxTokens": 8700,
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"temperature": 0.1
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},
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"fallback": {
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"provider": "azure",
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"modelId": "gpt-4o-mini",
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"maxTokens": 10000,
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"temperature": 0.7,
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"baseURL": "https://your-resource-name.azure.com/openai/deployments"
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}
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}
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}
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```
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4. **Environment Variables**:
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```bash
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# In .env file
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AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here
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# Optional: Override endpoint for all Azure models
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AZURE_OPENAI_ENDPOINT=https://your-resource-name.azure.com/openai/deployments
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```
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5. **Important Notes**:
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- **Model Deployment Names**: The `modelId` in your configuration should match the **deployment name** you created in Azure OpenAI Studio, not the underlying model name
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- **Base URL Priority**: Per-model `baseURL` settings override the global `azureBaseURL` setting
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- **Endpoint Format**: When using per-model `baseURL`, use the full path including `/openai/deployments`
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6. **Troubleshooting**:
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**"Resource not found" errors:**
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- Ensure your `baseURL` includes the full path: `https://your-resource-name.openai.azure.com/openai/deployments`
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- Verify that your deployment name in `modelId` exactly matches what's configured in Azure OpenAI Studio
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- Check that your Azure OpenAI resource is in the correct region and properly deployed
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**Authentication errors:**
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- Verify your `AZURE_OPENAI_API_KEY` is correct and has not expired
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- Ensure your Azure OpenAI resource has the necessary permissions
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- Check that your subscription has not been suspended or reached quota limits
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**Model availability errors:**
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- Confirm the model is deployed in your Azure OpenAI resource
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- Verify the deployment name matches your configuration exactly (case-sensitive)
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- Ensure the model deployment is in a "Succeeded" state in Azure OpenAI Studio
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- Ensure youre not getting rate limited by `maxTokens` maintain appropriate Tokens per Minute Rate Limit (TPM) in your deployment.
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