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📝 Update more documentations (phase 6)
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doc/docs/.vitepress/config.mts

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@@ -3,7 +3,7 @@ import { defineConfig } from 'vitepress'
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// https://vitepress.dev/reference/site-config
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export default defineConfig({
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base: '/doc/',
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title: "Nexent",
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title: "Nexent Doc",
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description: "A zero-code platform for auto-generating agents — no orchestration, no complex drag-and-drop required.",
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// Ignore localhost links as they are meant for local deployment access
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root: {
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label: 'English',
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lang: 'en',
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link: '/en/',
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link: '/en/getting-started/overview',
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themeConfig: {
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nav: [
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{ text: 'Home', link: 'http://nexent.tech' },
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items: [
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{ text: 'SDK Overview', link: '/en/sdk/overview' },
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{ text: 'Basic Usage', link: '/en/sdk/basic-usage' },
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{ text: 'Features Explained', link: '/en/sdk/features' },
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{
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text: 'Core Modules',
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items: [
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{ text: 'Agents', link: '/en/sdk/core/agents' },
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{ text: 'Tools', link: '/en/sdk/core/tools' },
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{ text: 'Models', link: '/en/sdk/core/models' }
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]
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{ text: 'Prompt Development', link: '/en/backend/prompt-development' }
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]
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},
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{
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text: 'AI Agents',
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items: [
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{ text: 'Agent Overview', link: '/en/agents/overview' }
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]
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},
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{
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text: 'Container Build & Containerized Development',
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items: [
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}
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],
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socialLinks: [
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{ icon: 'github', link: 'https://github.com/ModelEngine-Group/nexent' }
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{ icon: 'github', link: 'https://github.com/ModelEngine-Group/nexent' },
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{ icon: 'discord', link: 'https://discord.gg/tb5H3S3wyv' },
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{ icon: 'wechat', link: 'http://nexent.tech/contact' }
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]
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}
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},
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zh: {
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label: '简体中文',
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lang: 'zh-CN',
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link: '/zh/',
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link: '/zh/getting-started/overview',
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themeConfig: {
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nav: [
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{ text: '首页', link: 'http://nexent.tech' },
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items: [
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{ text: 'SDK 概览', link: '/zh/sdk/overview' },
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{ text: '基本使用', link: '/zh/sdk/basic-usage' },
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{ text: '特性详解', link: '/zh/sdk/features' },
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{
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text: '核心模块',
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items: [
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{ text: '智能体模块', link: '/zh/sdk/core/agents' },
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{ text: '工具模块', link: '/zh/sdk/core/tools' },
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{ text: '模型模块', link: '/zh/sdk/core/models' }
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]
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{ text: '提示词开发', link: '/zh/backend/prompt-development' }
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]
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},
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{
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text: 'AI 智能体',
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items: [
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{ text: '智能体概览', link: '/zh/agents/overview' }
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]
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},
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{
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text: '容器构建与容器化开发',
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items: [
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}
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],
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socialLinks: [
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{ icon: 'github', link: 'https://github.com/ModelEngine-Group/nexent' }
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{ icon: 'github', link: 'https://github.com/ModelEngine-Group/nexent' },
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{ icon: 'discord', link: 'https://discord.gg/tb5H3S3wyv' },
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{ icon: 'wechat', link: 'http://nexent.tech/contact' }
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]
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}
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}

doc/docs/api-examples.md

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doc/docs/en/getting-started/development-guide.md

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@@ -84,10 +84,10 @@ python backend/nexent_mcp_service.py # MCP service
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### 🤖 AI Agent Development
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- **Framework**: Enterprise agent framework based on smolagents
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- **Core Features**: Agent creation, tool integration, reasoning execution, multi-modal support
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- **Custom Agents**: See [Agent Overview](../agents/overview.md)
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- **Custom Agents**: See [Agents](../sdk/core/agents)
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- **System Prompts**: Located in `backend/prompts/`
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- **Implementation Steps**: Create instance → Configure tools → Set prompts → Test run
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- **Details**: See [Agent Overview](../agents/overview.md)
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- **Details**: See [Agents](../sdk/core/agents)
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### 🛠️ Tool Development
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- **MCP Tool System**: Based on Model Context Protocol

doc/docs/en/index.md

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doc/docs/en/sdk/basic-usage.md

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This guide provides a comprehensive introduction to using the Nexent SDK for building intelligent agents.
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## 🚀 Quick Start
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## 🚀 Installation
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### User Installation
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If you want to use Nexent:
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```bash
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# Recommended: Install from source
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git clone https://github.com/ModelEngine-Group/nexent.git
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cd nexent/sdk
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uv pip install -e .
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# Or install using uv
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uv add nexent
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```
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### Development Environment Setup
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If you are a third-party SDK developer:
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```bash
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# Install complete development environment (including Nexent)
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cd nexent/sdk
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uv pip install -e ".[dev]" # Includes all development tools (testing, code quality checks, etc.)
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```
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The development environment includes the following additional features:
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- Code quality checking tools (ruff)
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- Testing framework (pytest)
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- Data processing dependencies (unstructured)
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- Other development dependencies
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## ⚡ Quick Start
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### Basic Import
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agent.tools.append(custom_tool)
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```
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### 🎭 Multi-modal Agent Setup
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```python
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from nexent.core.models import OpenAIVLMModel
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# Create vision-capable model
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vision_model = OpenAIVLMModel(
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observer=observer,
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model_id="gpt-4-vision-preview",
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api_key="your-api-key"
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)
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# Create agent with vision capabilities
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vision_agent = CoreAgent(
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observer=observer,
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tools=[search_tool],
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model=vision_model,
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name="vision_agent"
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)
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```
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### 📡 Streaming Output Handling
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### 📡 Streaming Output Processing
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```python
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# Monitor streaming output
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elif process_type == ProcessType.FINAL_ANSWER:
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print(f"✅ Answer: {message}")
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# Set up observer with custom handler
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# Set observer with custom handler
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observer.set_message_handler(handle_stream)
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```
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### 🔧 Tool Error Handling
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```python
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# Tools automatically handle errors and provide fallbacks
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# Tools automatically handle errors and provide fallback options
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search_tool = ExaSearchTool(
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exa_api_key="your-exa-key",
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observer=observer,
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max_results=5,
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fallback_to_keyword=True # Fallback to keyword search if neural fails
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fallback_to_keyword=True # Fallback to keyword search if neural search fails
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)
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```
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## 🎭 Multi-modal Examples
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## 📚 More Resources
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### 🖼️ Image Processing
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```python
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# Process image with vision model
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result = vision_agent.run(
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"Describe what you see in this image",
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image_path="path/to/image.jpg"
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)
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```
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### 🎤 Voice Processing
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```python
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from nexent.core.tools import SpeechToTextTool, TextToSpeechTool
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# Add voice capabilities
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stt_tool = SpeechToTextTool(observer=observer)
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tts_tool = TextToSpeechTool(observer=observer)
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voice_agent = CoreAgent(
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observer=observer,
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tools=[stt_tool, tts_tool, search_tool],
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model=model,
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name="voice_agent"
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)
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```
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## 🔍 Best Practices
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### ⚡ Performance Optimization
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- **Connection Pooling**: Reuse connections for better performance
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- **Batch Processing**: Process multiple requests together when possible
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- **Caching**: Implement caching for frequently accessed data
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- **Async Operations**: Use async/await for I/O intensive operations
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### 🔒 Security Considerations
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- **API Key Management**: Store API keys securely using environment variables
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- **Input Validation**: Validate all inputs before processing
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- **Rate Limiting**: Implement rate limiting to prevent abuse
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- **Error Logging**: Log errors without exposing sensitive information
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### 🔍 Monitoring and Debugging
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```python
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# Enable detailed logging
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import logging
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logging.basicConfig(level=logging.DEBUG)
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# Monitor agent execution
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for step in agent.execution_steps:
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print(f"Step {step.step_number}: {step.action}")
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print(f"Result: {step.result}")
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```
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For more advanced usage patterns and detailed API documentation, please refer to:
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For more advanced usage patterns and detailed API documentation, see the **[Core Components](./overview)** and **[Tool Development](./core/tools)** guides.
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- **[Tool Development Guide](./core/tools)** - Detailed tool development standards and examples
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- **[Model Architecture Guide](./core/models)** - Model integration and usage documentation
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- **[Agents](./core/agents)** - Best practices and advanced patterns for agent development

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