wecode-ai · qdaxb · Nov 28, 2025 · Nov 28, 2025
diff --git a/AGENTS.md b/AGENTS.md
@@ -456,6 +456,11 @@ alembic upgrade head --sql
 - Add component: Use/extend `src/components/ui/`
 - Add type: Define in `src/types/`
 
+**Key feature modules:**
+- `src/features/settings/` - Settings page components including Models, Teams, Bots
+- `src/features/tasks/` - Task management and chat interface
+- `src/apis/models.ts` - Model API client (unified models, test connection)
+
 **Environment:** `NEXT_PUBLIC_API_URL` for client-side API calls
 
 ### Executor
@@ -476,6 +481,52 @@ alembic upgrade head --sql
 
 ---
 
+## 🔧 Model Management
+
+### Model Types
+
+Wegent supports two types of AI models:
+
+| Type | Description | Storage |
+|------|-------------|---------|
+| **Public** | System-provided models, shared across all users | `public_models` table |
+| **User** | User-defined private models | `kinds` table (kind='Model') |
+
+### Model Resolution Order
+
+When a Bot executes a task, models are resolved in this order:
+1. Task-level model override (if `force_override_bot_model` is true)
+2. Bot's `bind_model` from `agent_config`
+3. Bot's `modelRef` (legacy)
+4. Default model
+
+### Key APIs
+
+- `GET /api/models/unified` - List all available models (public + user)
+- `GET /api/models/unified/{name}` - Get specific model by name
+- `POST /api/models/test-connection` - Test model API connection
+- `GET /api/models/compatible?agent_name=X` - Get models compatible with agent type
+
+### Bot Model Binding
+
+Two ways to bind models to Bots:
+
+```yaml
+# Method 1: Using modelRef (legacy)
+spec:
+  modelRef:
+    name: model-name
+    namespace: default
+
+# Method 2: Using bind_model (recommended)
+spec:
+  agent_config:
+    bind_model: "my-model"
+    bind_model_type: "user"  # Optional: 'public' or 'user'
+```
+
+---
+
 ## 🔒 Security
 
 - Never commit credentials - use `.env` files (excluded from git)
@@ -549,6 +600,6 @@ docker-compose up -d --build [service]
 
 ---
 
-**Last Updated**: 2025-01
-**Wegent Version**: 1.0.7
+**Last Updated**: 2025-07
+**Wegent Version**: 1.0.8
 **Maintained by**: WeCode-AI Team
diff --git a/docs/en/guides/user/configuring-models.md b/docs/en/guides/user/configuring-models.md
@@ -45,8 +45,21 @@ Model determines "how strong the thinking ability is"
 ### Relationship with Database
 
 Model resources are stored in the following database tables:
-- `public_models`: Stores Model configuration information
-- `kinds`: Defines the resource type as `Model`
+- `public_models`: Stores public Model configurations shared across all users
+- `kinds`: Stores user-defined Model configurations (type='user')
+
+### Model Types
+
+Wegent supports two types of models:
+
+| Type | Description | Storage |
+|------|-------------|---------|
+| **Public** | System-provided models shared across all users | `public_models` table |
+| **User** | User-defined private models | `kinds` table |
+
+When binding models to Bots, the system resolves models in this order:
+1. User's private models (type='user')
+2. Public models (type='public')
 
 ---
 
@@ -432,45 +445,52 @@ When using OpenAI GPT models, you need to configure the following environment va
 
 ### Method 1: Configure via Web Interface (Recommended for Beginners)
 
-#### Step 1: Go to Model Configuration Page
+#### Step 1: Go to Model Management Page
 
 1. Log in to Wegent Web interface (http://localhost:3000)
-2. Go to **Resource Management** → **Model Configuration**
-3. Click **Create New Model** button
+2. Go to **Settings** → **Models** tab
+3. You will see a unified model list displaying both public and user-defined models
+4. Click **Create New Model** button to add a new model
 
-<!-- TODO: Add screenshot - Model configuration page -->
+<!-- TODO: Add screenshot - Model management page -->
 
-#### Step 2: Use Preset Template (Recommended)
+#### Step 2: Configure Model Details
 
-Above the JSON configuration input box, you will see a "Quick Configuration" area:
+In the model creation/edit dialog, configure the following:
 
-📋 **Use Preset Templates for Quick Configuration**
+**Basic Information**:
+- **Name**: Give the Model a descriptive name (e.g., `my-claude-sonnet`)
+- **Display Name**: Optional human-readable name shown in the UI
 
-- Click **[Claude Sonnet 4 Template]** button (primary recommendation)
-- Or click **[OpenAI GPT-4 Template]** button (alternative)
+**Provider Configuration**:
+- **Provider Type**: Select `OpenAI` or `Anthropic`
+- **Model ID**: Choose from preset models or enter a custom model ID
+  - OpenAI presets: `gpt-4`, `gpt-4-turbo`, `gpt-3.5-turbo`, `gpt-4o`, `gpt-4o-mini`
+  - Anthropic presets: `claude-sonnet-4-20250514`, `claude-3-7-sonnet-20250219`, `claude-3-5-haiku-20241022`
 
-Clicking will automatically fill the complete JSON configuration into the input box.
+**Authentication**:
+- **API Key**: Enter your API key from the provider
+  - Use the visibility toggle (👁️) to show/hide the key
+- **Base URL**: Optional custom API endpoint (for proxies or self-hosted services)
 
-#### Step 3: Modify API Key
+#### Step 3: Test Connection
 
-⚠️ **Important**: Please replace the API Key in the configuration with your actual key
+Before saving, use the **Test Connection** feature to verify your configuration:
 
-The API Key in the template is a placeholder, you need to:
-1. Find the `ANTHROPIC_AUTH_TOKEN` or `OPENAI_API_KEY` field in the configuration
-2. Replace the value with your real API Key obtained from the official site
-3. If it's an Anthropic model, it's recommended to also modify `ANTHROPIC_API_KEY`
+1. Click the **Test Connection** button
+2. The system will send a minimal test request to verify:
+   - API Key validity
+   - Model availability
+   - Network connectivity
+3. Results:
+   - ✅ "Successfully connected to {model}" - Configuration is valid
+   - ❌ Error message - Check your API key or network settings
 
-#### Step 4: Fill in Other Fields
+#### Step 4: Save Configuration
 
-- **Name**: Give the Model a descriptive name (e.g., `claude-sonnet-4-prod`)
-- **Namespace**: Usually use `default`
-- **JSON Configuration**: Already filled via template, just need to modify API Key
+Click **Save** to create or update the Model.
 
-#### Step 5: Submit Configuration
-
-Click the **Submit** button to create the Model.
-
-The system will validate the configuration format and will prompt if there are errors.
+The model will appear in your model list and can be used in Bot configurations.
 
 ---
 
@@ -490,6 +510,32 @@ Import the YAML configuration via the Web interface or API.
 
 ---
 
+## 🔄 Model Selection in Tasks
+
+### Per-Task Model Override
+
+When creating or sending a task, you can override the Bot's default model:
+
+1. In the chat interface, look for the **Model Selector** dropdown
+2. Select a different model from your available models
+3. Optionally enable **Force Override** to ensure this model is used even if the Bot has a configured model
+
+**Use Cases**:
+- Testing with different models without modifying Bot configuration
+- Using a more powerful model for complex tasks
+- Using a faster/cheaper model for simple tasks
+
+### Model Resolution Priority
+
+When a task runs, the model is resolved in this order:
+
+1. **Task-level override** (if force_override_bot_model is true)
+2. **Bot's bind_model** (from agent_config)
+3. **Bot's modelRef** (legacy)
+4. **Default model**
+
+---
+
 ## ✅ Configuration Validation
 
 After configuring a Model, **validation is essential** to ensure the configuration is correct and avoid errors during subsequent use.

diff --git a/docs/en/guides/user/creating-bots.md b/docs/en/guides/user/creating-bots.md
@@ -158,7 +158,32 @@ status:
 |-------|------|----------|-------------|
 | `ghostRef` | object | Yes | Ghost resource reference |
 | `shellRef` | object | Yes | Shell resource reference |
-| `modelRef` | object | Yes | Model resource reference |
+| `modelRef` | object | No | Model resource reference (optional, can use bind_model instead) |
+
+#### Model Binding Methods
+
+There are two ways to bind a model to a Bot:
+
+**Method 1: Using modelRef (Legacy)**
+```yaml
+spec:
+  modelRef:
+    name: <model-name>
+    namespace: default
+```
+
+**Method 2: Using bind_model in agent_config (Recommended)**
+```yaml
+spec:
+  agent_config:
+    bind_model: "my-custom-model"
+    bind_model_type: "user"  # Optional: 'public' or 'user'
+```
+
+The `bind_model` approach offers more flexibility:
+- Reference models by name without full YAML structure
+- Optionally specify model type to avoid naming conflicts
+- System auto-detects model type if not specified (user models first, then public)
 
 #### Reference Object Format
 

diff --git a/docs/en/guides/user/managing-tasks.md b/docs/en/guides/user/managing-tasks.md
@@ -187,6 +187,20 @@ status:
 | `prompt` | string | Yes | Detailed task description and requirements |
 | `teamRef` | object | Yes | Team reference executing the task |
 | `workspaceRef` | object | Yes | Workspace reference |
+| `model_id` | string | No | Model name to override Bot's default model |
+| `force_override_bot_model` | boolean | No | Force use of specified model even if Bot has configured model |
+
+### Per-Task Model Selection
+
+When creating a task through the Web interface, you can select a different model:
+
+1. **Model Selector**: In the chat input area, use the model dropdown to select from available models
+2. **Force Override**: Enable this option to ensure your selected model is used regardless of Bot configuration
+
+**Use cases**:
+- Test different models without modifying Bot configuration
+- Use a more powerful model for complex one-off tasks
+- Use a cheaper/faster model for simple queries
 
 #### status Section
 

diff --git a/docs/zh/guides/user/configuring-models.md b/docs/zh/guides/user/configuring-models.md
@@ -45,8 +45,21 @@ Model 决定"思考能力有多强"
 ### 与数据库的关系
 
 Model 资源存储在数据库的以下表中:
-- `public_models`: 存储 Model 配置信息
-- `kinds`: 定义资源类型为 `Model`
+- `public_models`: 存储所有用户共享的公共 Model 配置
+- `kinds`: 存储用户自定义的私有 Model 配置 (type='user')
+
+### 模型类型
+
+Wegent 支持两种类型的模型:
+
+| 类型 | 说明 | 存储位置 |
+|------|------|----------|
+| **公共模型** | 系统提供的所有用户共享的模型 | `public_models` 表 |
+| **用户模型** | 用户自定义的私有模型 | `kinds` 表 |
+
+当将模型绑定到 Bot 时，系统按以下顺序解析模型:
+1. 用户的私有模型 (type='user')
+2. 公共模型 (type='public')
 
 ---
 
@@ -432,45 +445,52 @@ sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
 
 ### 方式 1: 通过 Web 界面配置 (推荐新手)
 
-#### 步骤 1: 进入 Model 配置页面
+#### 步骤 1: 进入 Model 管理页面
 
 1. 登录 Wegent Web 界面 (http://localhost:3000)
-2. 进入 **资源管理** → **Model 配置**
-3. 点击 **创建新 Model** 按钮
+2. 进入 **设置** → **Models** 标签页
+3. 您将看到统一的模型列表，显示公共模型和用户自定义模型
+4. 点击 **创建新模型** 按钮添加新模型
 
-<!-- TODO: 添加截图 - Model 配置页面 -->
+<!-- TODO: 添加截图 - Model 管理页面 -->
 
-#### 步骤 2: 使用预设模板 (推荐)
+#### 步骤 2: 配置模型详情
 
-在 JSON 配置输入框上方,您会看到 "快速配置" 区域:
+在模型创建/编辑对话框中，配置以下内容:
 
-📋 **使用预设模板快速配置**
+**基本信息**:
+- **名称**: 给 Model 起一个描述性的名称 (如 `my-claude-sonnet`)
+- **显示名称**: 可选的在界面上显示的人类可读名称
 
-- 点击 **[Claude Sonnet 4 模板]** 按钮 (主要推荐)
-- 或点击 **[OpenAI GPT-4 模板]** 按钮 (备选)
+**提供商配置**:
+- **提供商类型**: 选择 `OpenAI` 或 `Anthropic`
+- **模型 ID**: 从预设模型中选择或输入自定义模型 ID
+  - OpenAI 预设: `gpt-4`, `gpt-4-turbo`, `gpt-3.5-turbo`, `gpt-4o`, `gpt-4o-mini`
+  - Anthropic 预设: `claude-sonnet-4-20250514`, `claude-3-7-sonnet-20250219`, `claude-3-5-haiku-20241022`
 
-点击后会自动填充完整的 JSON 配置到输入框。
+**认证信息**:
+- **API Key**: 输入您从提供商获取的 API 密钥
+  - 使用可见性切换按钮 (👁️) 显示/隐藏密钥
+- **Base URL**: 可选的自定义 API 端点 (用于代理或自托管服务)
 
-#### 步骤 3: 修改 API Key
+#### 步骤 3: 测试连接
 
-⚠️ **重要**: 请修改配置中的 API Key 为您的实际密钥
+在保存之前，使用 **测试连接** 功能验证您的配置:
 
-模板中的 API Key 是占位符,您需要:
-1. 找到配置中的 `ANTHROPIC_AUTH_TOKEN` 或 `OPENAI_API_KEY` 字段
-2. 将值替换为您从官网获取的真实 API Key
-3. 如果是 Anthropic 模型,建议同时修改 `ANTHROPIC_API_KEY`
+1. 点击 **测试连接** 按钮
+2. 系统会发送一个最小化的测试请求来验证:
+   - API Key 有效性
+   - 模型可用性
+   - 网络连通性
+3. 结果:
+   - ✅ "成功连接到 {模型}" - 配置有效
+   - ❌ 错误信息 - 检查您的 API 密钥或网络设置
 
-#### 步骤 4: 填写其他字段
+#### 步骤 4: 保存配置
 
-- **名称**: 给 Model 起一个描述性的名称 (如 `claude-sonnet-4-prod`)
-- **命名空间**: 通常使用 `default`
-- **JSON 配置**: 已通过模板填充,只需修改 API Key
+点击 **保存** 创建或更新模型。
 
-#### 步骤 5: 提交配置
-
-点击 **提交** 按钮创建 Model。
-
-系统会验证配置格式,如果有错误会提示。
+模型将出现在您的模型列表中，可以在 Bot 配置中使用。
 
 ---
 
@@ -490,6 +510,32 @@ sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
 
 ---
 
+## 🔄 任务中的模型选择
+
+### 单任务模型覆盖
+
+创建或发送任务时，您可以覆盖 Bot 的默认模型:
+
+1. 在聊天界面中，找到 **模型选择器** 下拉框
+2. 从可用模型中选择不同的模型
+3. 可选择启用 **强制覆盖** 以确保使用此模型，即使 Bot 已配置了模型
+
+**使用场景**:
+- 在不修改 Bot 配置的情况下测试不同模型
+- 对复杂任务使用更强大的模型
+- 对简单任务使用更快/更便宜的模型
+
+### 模型解析优先级
+
+当任务运行时，模型按以下顺序解析:
+
+1. **任务级覆盖** (如果 force_override_bot_model 为 true)
+2. **Bot 的 bind_model** (来自 agent_config)
+3. **Bot 的 modelRef** (旧版)
+4. **默认模型**
+
+---
+
 ## ✅ 配置验证
 
 配置 Model 后,**务必进行验证**以确保配置正确,避免后续使用时出错。