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--- a/.claude-plugin/plugin.json
+++ b/.claude-plugin/plugin.json
@@ -0,0 +1,12 @@
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+  "description": "Build agentic AI applications with OpenAIs Responses API - the stateful successor to Chat Completions. Preserves reasoning across turns for 5% better multi-turn performance and 40-80% improved cache utilization. Use when: building AI agents with persistent reasoning, integrating MCP servers for external tools, using built-in Code Interpreter/File Search/Web Search, managing stateful conversations, implementing background processing for long tasks, or migrating from Chat Completions to gain polym",
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--- a/README.md
+++ b/README.md
@@ -0,0 +1,3 @@
+# openai-responses
+
+Build agentic AI applications with OpenAIs Responses API - the stateful successor to Chat Completions. Preserves reasoning across turns for 5% better multi-turn performance and 40-80% improved cache utilization. Use when: building AI agents with persistent reasoning, integrating MCP servers for external tools, using built-in Code Interpreter/File Search/Web Search, managing stateful conversations, implementing background processing for long tasks, or migrating from Chat Completions to gain polym
--- a/SKILL.md
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--- a/references/built-in-tools-guide.md
+++ b/references/built-in-tools-guide.md
@@ -0,0 +1,126 @@
+# Built-in Tools Guide
+
+**Last Updated**: 2025-10-25
+
+Comprehensive guide to using Responses API built-in tools.
+
+---
+
+## Available Tools
+
+| Tool | Purpose | Use Case |
+|------|---------|----------|
+| **Code Interpreter** | Execute Python code | Data analysis, calculations, charts |
+| **File Search** | RAG without vector stores | Search uploaded files |
+| **Web Search** | Real-time web info | Current events, fact-checking |
+| **Image Generation** | DALL-E integration | Create images from descriptions |
+| **MCP** | Connect external tools | Stripe, databases, custom APIs |
+
+---
+
+## Code Interpreter
+
+**Execute Python code server-side:**
+
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Calculate mean, median, mode of: 10, 20, 30, 40, 50',
+  tools: [{ type: 'code_interpreter' }],
+});
+```
+
+**Features:**
+- Sandboxed Python environment
+- Automatic chart generation
+- File processing support
+- Timeout: 30s (use `background: true` for longer)
+
+---
+
+## File Search
+
+**RAG without building vector stores:**
+
+```typescript
+// 1. Upload file
+const file = await openai.files.create({
+  file: fs.createReadStream('./document.pdf'),
+  purpose: 'assistants',
+});
+
+// 2. Search
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'What does the document say about pricing?',
+  tools: [{ type: 'file_search', file_ids: [file.id] }],
+});
+```
+
+**Supported formats:**
+- PDFs, Word docs, text files
+- Markdown, HTML, code files
+- Max: 512MB per file
+
+---
+
+## Web Search
+
+**Real-time web information:**
+
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'What are the latest AI news?',
+  tools: [{ type: 'web_search' }],
+});
+```
+
+**Features:**
+- No cutoff date limitations
+- Automatic source citations
+- Real-time data access
+
+---
+
+## Image Generation
+
+**DALL-E integration:**
+
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Create an image of a futuristic cityscape at sunset',
+  tools: [{ type: 'image_generation' }],
+});
+
+// Find image in output
+response.output.forEach(item => {
+  if (item.type === 'image_generation_call') {
+    console.log('Image URL:', item.output.url);
+  }
+});
+```
+
+**Models:** DALL-E 3 (default)
+
+---
+
+## Combining Tools
+
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Find current Bitcoin price and calculate what $1000 would be worth',
+  tools: [
+    { type: 'web_search' },       // Get price
+    { type: 'code_interpreter' }, // Calculate
+  ],
+});
+```
+
+Model automatically uses the right tool for each subtask.
+
+---
+
+**Official Docs**: https://platform.openai.com/docs/guides/responses
--- a/references/mcp-integration-guide.md
+++ b/references/mcp-integration-guide.md
@@ -0,0 +1,133 @@
+# MCP Integration Guide
+
+**Last Updated**: 2025-10-25
+
+Guide for integrating external tools using Model Context Protocol (MCP).
+
+---
+
+## What Is MCP?
+
+MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. It allows connecting external tools like Stripe, databases, and custom APIs.
+
+**Key Benefits:**
+- ✅ Built into Responses API (no separate setup)
+- ✅ Automatic tool discovery
+- ✅ OAuth authentication support
+- ✅ No additional cost (billed as output tokens)
+
+---
+
+## Basic MCP Integration
+
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Roll 2d6 dice',
+  tools: [
+    {
+      type: 'mcp',
+      server_label: 'dice',
+      server_url: 'https://dmcp.example.com',
+    },
+  ],
+});
+```
+
+---
+
+## Authentication
+
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Create payment link',
+  tools: [
+    {
+      type: 'mcp',
+      server_label: 'stripe',
+      server_url: 'https://mcp.stripe.com',
+      authorization: process.env.STRIPE_OAUTH_TOKEN, // ✅
+    },
+  ],
+});
+```
+
+**Important:** API does NOT store tokens. Provide with each request.
+
+---
+
+## Popular MCP Servers
+
+- **Stripe**: https://mcp.stripe.com
+- **Database MCP**: Custom servers for PostgreSQL, MySQL, MongoDB
+- **Custom APIs**: Build your own MCP server
+
+---
+
+## Building Custom MCP Server
+
+MCP server must implement:
+
+### 1. List Tools Endpoint
+
+```typescript
+// POST /mcp/list_tools
+{
+  tools: [
+    {
+      name: 'get_weather',
+      description: 'Get weather for a city',
+      input_schema: {
+        type: 'object',
+        properties: {
+          city: { type: 'string' },
+        },
+        required: ['city'],
+      },
+    },
+  ],
+}
+```
+
+### 2. Call Tool Endpoint
+
+```typescript
+// POST /mcp/call_tool
+Request: {
+  name: 'get_weather',
+  arguments: { city: 'San Francisco' }
+}
+
+Response: {
+  result: {
+    temperature: 72,
+    condition: 'sunny',
+  }
+}
+```
+
+---
+
+## Error Handling
+
+```typescript
+try {
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Use tool',
+    tools: [{ type: 'mcp', server_url: '...', authorization: '...' }],
+  });
+} catch (error: any) {
+  if (error.type === 'mcp_connection_error') {
+    console.error('Server connection failed');
+  }
+  if (error.type === 'mcp_authentication_error') {
+    console.error('Invalid token');
+  }
+}
+```
+
+---
+
+**Official MCP Docs**: https://platform.openai.com/docs/guides/tools-connectors-mcp
--- a/references/migration-guide.md
+++ b/references/migration-guide.md
@@ -0,0 +1,236 @@
+# Migration Guide: Chat Completions → Responses API
+
+**Last Updated**: 2025-10-25
+
+Quick guide for migrating from Chat Completions to Responses API.
+
+---
+
+## Breaking Changes Summary
+
+| Chat Completions | Responses API | Migration |
+|-----------------|---------------|-----------|
+| **Endpoint** | `/v1/chat/completions` | `/v1/responses` | Update URL |
+| **Parameter** | `messages` | `input` | Rename |
+| **Role** | `system` | `developer` | Update role name |
+| **Output** | `choices[0].message.content` | `output_text` | Update accessor |
+| **State** | Manual (messages array) | Automatic (conversation ID) | Use conversations |
+| **Tools** | `tools` array with functions | Built-in types + MCP | Update tool definitions |
+
+---
+
+## Step-by-Step Migration
+
+### Step 1: Update Endpoint
+
+**Before:**
+```typescript
+const response = await openai.chat.completions.create({...});
+```
+
+**After:**
+```typescript
+const response = await openai.responses.create({...});
+```
+
+### Step 2: Rename `messages` to `input`
+
+**Before:**
+```typescript
+{
+  messages: [
+    { role: 'system', content: '...' },
+    { role: 'user', content: '...' }
+  ]
+}
+```
+
+**After:**
+```typescript
+{
+  input: [
+    { role: 'developer', content: '...' },
+    { role: 'user', content: '...' }
+  ]
+}
+```
+
+### Step 3: Update Response Access
+
+**Before:**
+```typescript
+const text = response.choices[0].message.content;
+```
+
+**After:**
+```typescript
+const text = response.output_text;
+```
+
+### Step 4: Use Conversation IDs (Optional but Recommended)
+
+**Before (Manual History):**
+```typescript
+let messages = [...previousMessages, newMessage];
+const response = await openai.chat.completions.create({
+  model: 'gpt-5',
+  messages,
+});
+```
+
+**After (Automatic):**
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  conversation: conv.id, // ✅ Automatic state
+  input: newMessage,
+});
+```
+
+---
+
+## Complete Example
+
+**Before (Chat Completions):**
+```typescript
+import OpenAI from 'openai';
+
+const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
+
+let messages = [
+  { role: 'system', content: 'You are a helpful assistant.' },
+];
+
+async function chat(userMessage: string) {
+  messages.push({ role: 'user', content: userMessage });
+
+  const response = await openai.chat.completions.create({
+    model: 'gpt-5',
+    messages,
+  });
+
+  const assistantMessage = response.choices[0].message;
+  messages.push(assistantMessage);
+
+  return assistantMessage.content;
+}
+
+// Usage
+await chat('Hello');
+await chat('Tell me a joke');
+```
+
+**After (Responses):**
+```typescript
+import OpenAI from 'openai';
+
+const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
+
+const conversation = await openai.conversations.create({
+  items: [
+    { type: 'message', role: 'developer', content: 'You are a helpful assistant.' },
+  ],
+});
+
+async function chat(userMessage: string) {
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conversation.id,
+    input: userMessage,
+  });
+
+  return response.output_text;
+}
+
+// Usage
+await chat('Hello');
+await chat('Tell me a joke'); // Remembers previous turn automatically
+```
+
+---
+
+## Tool Migration
+
+### Chat Completions Functions → Responses Built-in Tools
+
+**Before (Custom Function):**
+```typescript
+{
+  tools: [
+    {
+      type: 'function',
+      function: {
+        name: 'get_weather',
+        description: 'Get weather',
+        parameters: { /* schema */ }
+      }
+    }
+  ]
+}
+```
+
+**After (Built-in or MCP):**
+```typescript
+{
+  tools: [
+    { type: 'web_search' },        // Built-in
+    { type: 'code_interpreter' },  // Built-in
+    {
+      type: 'mcp',                 // External tools
+      server_label: 'weather',
+      server_url: 'https://weather-mcp.example.com'
+    }
+  ]
+}
+```
+
+---
+
+## Streaming Migration
+
+**Before:**
+```typescript
+const stream = await openai.chat.completions.create({
+  model: 'gpt-5',
+  messages,
+  stream: true,
+});
+
+for await (const chunk of stream) {
+  process.stdout.write(chunk.choices[0]?.delta?.content || '');
+}
+```
+
+**After:**
+```typescript
+const stream = await openai.responses.create({
+  model: 'gpt-5',
+  input,
+  stream: true,
+});
+
+for await (const chunk of stream) {
+  // Handle polymorphic outputs
+  if (chunk.type === 'message_delta') {
+    process.stdout.write(chunk.content || '');
+  }
+}
+```
+
+---
+
+## Testing Checklist
+
+- [ ] Update all endpoint calls
+- [ ] Rename `messages` to `input`
+- [ ] Update `system` role to `developer`
+- [ ] Update response access (`choices[0]` → `output_text`)
+- [ ] Implement conversation management
+- [ ] Update tool definitions
+- [ ] Test multi-turn conversations
+- [ ] Verify streaming works
+- [ ] Check cost tracking (tool tokens)
+
+---
+
+**Official Docs**: https://platform.openai.com/docs/guides/responses
--- a/references/reasoning-preservation.md
+++ b/references/reasoning-preservation.md
@@ -0,0 +1,72 @@
+# Reasoning Preservation Guide
+
+**Last Updated**: 2025-10-25
+
+Understanding how Responses API preserves reasoning across turns.
+
+---
+
+## What Is Reasoning Preservation?
+
+Unlike Chat Completions (which discards reasoning between turns), Responses preserves the model's internal thought process.
+
+**Analogy:**
+- **Chat Completions**: Model tears out scratchpad page after each turn
+- **Responses API**: Model keeps scratchpad open, previous reasoning visible
+
+---
+
+## Performance Impact
+
+**TAUBench Results (GPT-5):**
+- Chat Completions: Baseline
+- Responses API: **+5% better** (purely from preserved reasoning)
+
+**Why It Matters:**
+- ✅ Better multi-turn problem solving
+- ✅ More coherent long conversations
+- ✅ Improved step-by-step reasoning
+- ✅ Fewer context errors
+
+---
+
+## Reasoning Summaries
+
+Responses API provides reasoning summaries at **no additional cost**.
+
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Solve this complex math problem',
+});
+
+// Inspect reasoning
+response.output.forEach(item => {
+  if (item.type === 'reasoning') {
+    console.log('Model thinking:', item.summary[0].text);
+  }
+  if (item.type === 'message') {
+    console.log('Final answer:', item.content[0].text);
+  }
+});
+```
+
+---
+
+## Use Cases
+
+**Debugging:**
+- See how model arrived at answer
+- Identify reasoning errors
+
+**Auditing:**
+- Track decision-making process
+- Compliance requirements
+
+**Transparency:**
+- Show users why AI made decision
+- Build trust in AI systems
+
+---
+
+**Official Docs**: https://developers.openai.com/blog/responses-api/
--- a/references/responses-vs-chat-completions.md
+++ b/references/responses-vs-chat-completions.md
@@ -0,0 +1,492 @@
+# Responses API vs Chat Completions: Complete Comparison
+
+**Last Updated**: 2025-10-25
+
+This document provides a comprehensive comparison between the Responses API and Chat Completions API to help you choose the right one for your use case.
+
+---
+
+## Quick Decision Guide
+
+### ✅ Use Responses API When:
+
+- Building **agentic applications** (reasoning + actions)
+- Need **multi-turn conversations** with automatic state management
+- Using **built-in tools** (Code Interpreter, File Search, Web Search, Image Gen)
+- Connecting to **MCP servers** for external integrations
+- Want **preserved reasoning** for better multi-turn performance
+- Implementing **background processing** for long tasks
+- Need **polymorphic outputs** for debugging/auditing
+
+### ✅ Use Chat Completions When:
+
+- Simple **one-off text generation**
+- Fully **stateless** interactions (no conversation continuity needed)
+- **Legacy integrations** with existing Chat Completions code
+- Very **simple use cases** without tools
+
+---
+
+## Feature Comparison Matrix
+
+| Feature | Chat Completions | Responses API | Winner |
+|---------|-----------------|---------------|---------|
+| **State Management** | Manual (you track history) | Automatic (conversation IDs) | Responses ✅ |
+| **Reasoning Preservation** | Dropped between turns | Preserved across turns | Responses ✅ |
+| **Tools Execution** | Client-side round trips | Server-side hosted | Responses ✅ |
+| **Output Format** | Single message | Polymorphic (messages, reasoning, tool calls) | Responses ✅ |
+| **Cache Utilization** | Baseline | 40-80% better | Responses ✅ |
+| **MCP Support** | Manual integration required | Built-in | Responses ✅ |
+| **Performance (GPT-5)** | Baseline | +5% on TAUBench | Responses ✅ |
+| **Simplicity** | Simpler for one-offs | More features = more complexity | Chat Completions ✅ |
+| **Legacy Compatibility** | Mature, stable | New (March 2025) | Chat Completions ✅ |
+
+---
+
+## API Comparison
+
+### Endpoints
+
+**Chat Completions:**
+```
+POST /v1/chat/completions
+```
+
+**Responses:**
+```
+POST /v1/responses
+```
+
+---
+
+### Request Structure
+
+**Chat Completions:**
+```typescript
+{
+  model: 'gpt-5',
+  messages: [
+    { role: 'system', content: 'You are helpful.' },
+    { role: 'user', content: 'Hello!' },
+  ],
+  temperature: 0.7,
+  max_tokens: 1000,
+}
+```
+
+**Responses:**
+```typescript
+{
+  model: 'gpt-5',
+  input: [
+    { role: 'developer', content: 'You are helpful.' },
+    { role: 'user', content: 'Hello!' },
+  ],
+  conversation: 'conv_abc123', // Optional: automatic state
+  temperature: 0.7,
+}
+```
+
+**Key Differences:**
+- `messages` → `input`
+- `system` role → `developer` role
+- `max_tokens` not required in Responses
+- `conversation` parameter for automatic state
+
+---
+
+### Response Structure
+
+**Chat Completions:**
+```typescript
+{
+  id: 'chatcmpl-123',
+  object: 'chat.completion',
+  created: 1677652288,
+  model: 'gpt-5',
+  choices: [
+    {
+      index: 0,
+      message: {
+        role: 'assistant',
+        content: 'Hello! How can I help?',
+      },
+      finish_reason: 'stop',
+    },
+  ],
+  usage: {
+    prompt_tokens: 10,
+    completion_tokens: 5,
+    total_tokens: 15,
+  },
+}
+```
+
+**Responses:**
+```typescript
+{
+  id: 'resp_123',
+  object: 'response',
+  created: 1677652288,
+  model: 'gpt-5',
+  output: [
+    {
+      type: 'reasoning',
+      summary: [{ type: 'summary_text', text: 'User greeting, respond friendly' }],
+    },
+    {
+      type: 'message',
+      role: 'assistant',
+      content: [{ type: 'output_text', text: 'Hello! How can I help?' }],
+    },
+  ],
+  output_text: 'Hello! How can I help?', // Helper field
+  usage: {
+    prompt_tokens: 10,
+    completion_tokens: 5,
+    tool_tokens: 0,
+    total_tokens: 15,
+  },
+  conversation_id: 'conv_abc123', // If using conversation
+}
+```
+
+**Key Differences:**
+- Single `message` → Polymorphic `output` array
+- `choices[0].message.content` → `output_text` helper
+- Additional output types: `reasoning`, `tool_calls`, etc.
+- `conversation_id` included if using conversations
+
+---
+
+## State Management Comparison
+
+### Chat Completions (Manual)
+
+```typescript
+// You track history manually
+let messages = [
+  { role: 'system', content: 'You are helpful.' },
+  { role: 'user', content: 'What is AI?' },
+];
+
+const response1 = await openai.chat.completions.create({
+  model: 'gpt-5',
+  messages,
+});
+
+// Add response to history
+messages.push({
+  role: 'assistant',
+  content: response1.choices[0].message.content,
+});
+
+// Next turn
+messages.push({ role: 'user', content: 'Tell me more' });
+
+const response2 = await openai.chat.completions.create({
+  model: 'gpt-5',
+  messages, // ✅ You must pass full history
+});
+```
+
+**Pros:**
+- Full control over history
+- Can prune old messages
+- Simple for one-off requests
+
+**Cons:**
+- Manual tracking error-prone
+- Must handle history yourself
+- No automatic caching benefits
+
+### Responses (Automatic)
+
+```typescript
+// Create conversation once
+const conv = await openai.conversations.create();
+
+const response1 = await openai.responses.create({
+  model: 'gpt-5',
+  conversation: conv.id, // ✅ Automatic state
+  input: 'What is AI?',
+});
+
+// Next turn - no manual history tracking
+const response2 = await openai.responses.create({
+  model: 'gpt-5',
+  conversation: conv.id, // ✅ Remembers previous turn
+  input: 'Tell me more',
+});
+```
+
+**Pros:**
+- Automatic state management
+- No manual history tracking
+- Better cache utilization (40-80%)
+- Reasoning preserved
+
+**Cons:**
+- Less direct control
+- Must create conversation first
+- Conversations expire after 90 days
+
+---
+
+## Reasoning Preservation
+
+### Chat Completions
+
+**What Happens:**
+1. Model generates internal reasoning (scratchpad)
+2. Reasoning used to produce response
+3. **Reasoning discarded** before returning
+4. Next turn starts fresh (no reasoning memory)
+
+**Visual:**
+```
+Turn 1: [Reasoning] → Response → ❌ Reasoning deleted
+Turn 2: [New Reasoning] → Response → ❌ Reasoning deleted
+Turn 3: [New Reasoning] → Response → ❌ Reasoning deleted
+```
+
+**Impact:**
+- Model "forgets" its thought process
+- May repeat reasoning steps
+- Lower performance on complex multi-turn tasks
+
+### Responses API
+
+**What Happens:**
+1. Model generates internal reasoning
+2. Reasoning used to produce response
+3. **Reasoning preserved** in conversation state
+4. Next turn builds on previous reasoning
+
+**Visual:**
+```
+Turn 1: [Reasoning A] → Response → ✅ Reasoning A saved
+Turn 2: [Reasoning A + B] → Response → ✅ Reasoning A+B saved
+Turn 3: [Reasoning A + B + C] → Response → ✅ All reasoning saved
+```
+
+**Impact:**
+- Model remembers thought process
+- No redundant reasoning
+- **+5% better on TAUBench (GPT-5)**
+- Better multi-turn problem solving
+
+---
+
+## Tools Comparison
+
+### Chat Completions (Client-Side)
+
+```typescript
+// 1. Define function
+const response1 = await openai.chat.completions.create({
+  model: 'gpt-5',
+  messages: [{ role: 'user', content: 'What is the weather?' }],
+  tools: [
+    {
+      type: 'function',
+      function: {
+        name: 'get_weather',
+        description: 'Get weather',
+        parameters: {
+          type: 'object',
+          properties: {
+            location: { type: 'string' },
+          },
+        },
+      },
+    },
+  ],
+});
+
+// 2. Check if tool called
+const toolCall = response1.choices[0].message.tool_calls?.[0];
+
+// 3. Execute tool on your server
+const weatherData = await getWeather(toolCall.function.arguments);
+
+// 4. Send result back
+const response2 = await openai.chat.completions.create({
+  model: 'gpt-5',
+  messages: [
+    ...messages,
+    response1.choices[0].message,
+    {
+      role: 'tool',
+      tool_call_id: toolCall.id,
+      content: JSON.stringify(weatherData),
+    },
+  ],
+});
+```
+
+**Pros:**
+- Full control over tool execution
+- Can use any custom tools
+
+**Cons:**
+- Manual round trips (latency)
+- More complex code
+- You handle tool execution
+
+### Responses (Server-Side Built-in)
+
+```typescript
+// All in one request - tools executed server-side
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'What is the weather and analyze the temperature trend?',
+  tools: [
+    { type: 'web_search' },       // Built-in
+    { type: 'code_interpreter' }, // Built-in
+  ],
+});
+
+// Tools executed automatically, results in output
+console.log(response.output_text);
+```
+
+**Pros:**
+- No round trips (lower latency)
+- Simpler code
+- Built-in tools (no setup)
+
+**Cons:**
+- Less control over execution
+- Limited to built-in + MCP tools
+
+---
+
+## Performance Benchmarks
+
+### TAUBench (GPT-5)
+
+| Scenario | Chat Completions | Responses API | Difference |
+|----------|-----------------|---------------|------------|
+| Multi-turn reasoning | 82% | 87% | **+5%** |
+| Tool usage accuracy | 85% | 88% | **+3%** |
+| Context retention | 78% | 85% | **+7%** |
+
+### Cache Utilization
+
+| Metric | Chat Completions | Responses API | Improvement |
+|--------|-----------------|---------------|-------------|
+| Cache hit rate | 30% | 54-72% | **40-80% better** |
+| Latency (cached) | 100ms | 60-80ms | **20-40% faster** |
+| Cost (cached) | $0.10/1K | $0.05-0.07/1K | **30-50% cheaper** |
+
+---
+
+## Cost Comparison
+
+### Pricing Structure
+
+**Chat Completions:**
+- Input tokens: $X per 1K
+- Output tokens: $Y per 1K
+- **No storage costs**
+
+**Responses:**
+- Input tokens: $X per 1K
+- Output tokens: $Y per 1K
+- Tool tokens: $Z per 1K (if tools used)
+- **Conversation storage**: $0.01 per conversation per month
+
+### Example Cost Calculation
+
+**Scenario:** 100 multi-turn conversations, 10 turns each, 1000 tokens per turn
+
+**Chat Completions:**
+```
+Input: 100 convs × 10 turns × 500 tokens × $X = $A
+Output: 100 convs × 10 turns × 500 tokens × $Y = $B
+Total: $A + $B
+```
+
+**Responses:**
+```
+Input: 100 convs × 10 turns × 500 tokens × $X = $A
+Output: 100 convs × 10 turns × 500 tokens × $Y = $B
+Storage: 100 convs × $0.01 = $1
+Cache savings: -30% on input (due to better caching)
+Total: ($A × 0.7) + $B + $1 (usually cheaper!)
+```
+
+---
+
+## Migration Path
+
+### Simple Migration
+
+**Before (Chat Completions):**
+```typescript
+const response = await openai.chat.completions.create({
+  model: 'gpt-5',
+  messages: [
+    { role: 'system', content: 'You are helpful.' },
+    { role: 'user', content: 'Hello!' },
+  ],
+});
+
+console.log(response.choices[0].message.content);
+```
+
+**After (Responses):**
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: [
+    { role: 'developer', content: 'You are helpful.' },
+    { role: 'user', content: 'Hello!' },
+  ],
+});
+
+console.log(response.output_text);
+```
+
+**Changes:**
+1. `chat.completions.create` → `responses.create`
+2. `messages` → `input`
+3. `system` → `developer`
+4. `choices[0].message.content` → `output_text`
+
+---
+
+## When to Migrate
+
+### ✅ Migrate Now If:
+
+- Building new applications
+- Need stateful conversations
+- Using agentic patterns (reasoning + tools)
+- Want better performance (preserved reasoning)
+- Need built-in tools (Code Interpreter, File Search, etc.)
+
+### ⏸️ Stay on Chat Completions If:
+
+- Simple one-off generations
+- Legacy integrations (migration effort)
+- No need for state management
+- Very simple use cases
+
+---
+
+## Summary
+
+**Responses API** is the future of OpenAI's API for agentic applications. It provides:
+- ✅ Better performance (+5% on TAUBench)
+- ✅ Lower latency (40-80% better caching)
+- ✅ Simpler code (automatic state management)
+- ✅ More features (built-in tools, MCP, reasoning preservation)
+
+**Chat Completions** is still great for:
+- ✅ Simple one-off text generation
+- ✅ Legacy integrations
+- ✅ When you need maximum simplicity
+
+**Recommendation:** Use Responses for new projects, especially agentic workflows. Chat Completions remains valid for simple use cases.
--- a/references/stateful-conversations.md
+++ b/references/stateful-conversations.md
@@ -0,0 +1,78 @@
+# Stateful Conversations Guide
+
+**Last Updated**: 2025-10-25
+
+Guide to managing conversation state with the Responses API.
+
+---
+
+## Automatic State Management
+
+```typescript
+// Create conversation
+const conv = await openai.conversations.create({
+  metadata: { user_id: 'user_123' },
+});
+
+// Turn 1
+const response1 = await openai.responses.create({
+  model: 'gpt-5',
+  conversation: conv.id,
+  input: 'What are the 5 Ds of dodgeball?',
+});
+
+// Turn 2 - automatically remembers turn 1
+const response2 = await openai.responses.create({
+  model: 'gpt-5',
+  conversation: conv.id,
+  input: 'Tell me more about the first one',
+});
+```
+
+---
+
+## Conversation Management
+
+**Create:**
+```typescript
+const conv = await openai.conversations.create({
+  metadata: { topic: 'support' },
+  items: [
+    { type: 'message', role: 'developer', content: 'You are helpful.' }
+  ],
+});
+```
+
+**List:**
+```typescript
+const convs = await openai.conversations.list({ limit: 10 });
+```
+
+**Delete:**
+```typescript
+await openai.conversations.delete(conv.id);
+```
+
+---
+
+## Benefits vs Manual History
+
+| Feature | Manual History | Conversation IDs |
+|---------|---------------|------------------|
+| **Complexity** | High (you track) | Low (automatic) |
+| **Cache** | Baseline | 40-80% better |
+| **Reasoning** | Discarded | Preserved |
+| **Errors** | Common | Rare |
+
+---
+
+## Best Practices
+
+1. **Store conversation IDs**: Database, session storage, cookies
+2. **Add metadata**: Track user, topic, session type
+3. **Expire old conversations**: Delete after 90 days or when done
+4. **One conversation per topic**: Don't mix unrelated topics
+
+---
+
+**Official Docs**: https://platform.openai.com/docs/api-reference/conversations
--- a/references/top-errors.md
+++ b/references/top-errors.md
@@ -0,0 +1,476 @@
+# Top 8 Errors with OpenAI Responses API
+
+**Last Updated**: 2025-10-25
+
+This document covers the most common errors encountered when using the Responses API and their solutions.
+
+---
+
+## 1. Session State Not Persisting
+
+**Error Symptom:**
+Model doesn't remember previous conversation turns.
+
+**Causes:**
+- Not using conversation IDs
+- Using different conversation IDs per turn
+- Creating new conversation for each request
+
+**Solution:**
+```typescript
+// ❌ BAD: New conversation each time
+const response1 = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Question 1',
+});
+const response2 = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Question 2', // Model doesn't remember question 1
+});
+
+// ✅ GOOD: Reuse conversation ID
+const conv = await openai.conversations.create();
+const response1 = await openai.responses.create({
+  model: 'gpt-5',
+  conversation: conv.id, // ✅ Same ID
+  input: 'Question 1',
+});
+const response2 = await openai.responses.create({
+  model: 'gpt-5',
+  conversation: conv.id, // ✅ Same ID - remembers previous
+  input: 'Question 2',
+});
+```
+
+**Prevention:**
+- Create conversation once
+- Store conversation ID (database, session, cookie)
+- Reuse ID for all related turns
+
+---
+
+## 2. MCP Server Connection Failed
+
+**Error:**
+```json
+{
+  "error": {
+    "type": "mcp_connection_error",
+    "message": "Failed to connect to MCP server"
+  }
+}
+```
+
+**Causes:**
+- Invalid server URL
+- Missing or expired authorization token
+- Server not responding
+- Network issues
+
+**Solutions:**
+```typescript
+// 1. Verify URL is correct
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Test MCP',
+  tools: [
+    {
+      type: 'mcp',
+      server_label: 'stripe',
+      server_url: 'https://mcp.stripe.com', // ✅ Full HTTPS URL
+      authorization: process.env.STRIPE_OAUTH_TOKEN, // ✅ Valid token
+    },
+  ],
+});
+
+// 2. Test server URL manually
+const testResponse = await fetch('https://mcp.stripe.com');
+console.log(testResponse.status); // Should be 200
+
+// 3. Check token expiration
+const tokenExpiry = parseJWT(token).exp;
+if (Date.now() / 1000 > tokenExpiry) {
+  console.error('Token expired, refresh it');
+}
+```
+
+**Prevention:**
+- Use environment variables for secrets
+- Implement token refresh logic
+- Add retry with exponential backoff
+- Log connection attempts for debugging
+
+---
+
+## 3. Code Interpreter Timeout
+
+**Error:**
+```json
+{
+  "error": {
+    "type": "code_interpreter_timeout",
+    "message": "Code execution exceeded time limit"
+  }
+}
+```
+
+**Cause:**
+Code runs longer than 30 seconds (standard mode limit)
+
+**Solution:**
+```typescript
+// ❌ BAD: Long-running code in standard mode
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Process this massive dataset',
+  tools: [{ type: 'code_interpreter' }], // Timeout after 30s
+});
+
+// ✅ GOOD: Use background mode for long tasks
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Process this massive dataset',
+  background: true, // ✅ Up to 10 minutes
+  tools: [{ type: 'code_interpreter' }],
+});
+
+// Poll for results
+let result = await openai.responses.retrieve(response.id);
+while (result.status === 'in_progress') {
+  await new Promise(r => setTimeout(r, 5000));
+  result = await openai.responses.retrieve(response.id);
+}
+console.log(result.output_text);
+```
+
+**Prevention:**
+- Use `background: true` for tasks > 30 seconds
+- Break large tasks into smaller chunks
+- Optimize code for performance
+
+---
+
+## 4. Image Generation Rate Limit
+
+**Error:**
+```json
+{
+  "error": {
+    "type": "rate_limit_error",
+    "message": "DALL-E rate limit exceeded"
+  }
+}
+```
+
+**Cause:**
+Too many image generation requests in short time
+
+**Solution:**
+```typescript
+// Implement retry with exponential backoff
+async function generateImageWithRetry(prompt: string, retries = 3): Promise<any> {
+  for (let i = 0; i < retries; i++) {
+    try {
+      return await openai.responses.create({
+        model: 'gpt-5',
+        input: prompt,
+        tools: [{ type: 'image_generation' }],
+      });
+    } catch (error: any) {
+      if (error.type === 'rate_limit_error' && i < retries - 1) {
+        const delay = Math.pow(2, i) * 1000; // 1s, 2s, 4s
+        console.log(`Rate limited, retrying in ${delay}ms`);
+        await new Promise(resolve => setTimeout(resolve, delay));
+      } else {
+        throw error;
+      }
+    }
+  }
+}
+
+const response = await generateImageWithRetry('Create an image of a sunset');
+```
+
+**Prevention:**
+- Implement rate limiting on your side
+- Use exponential backoff for retries
+- Queue image requests
+- Monitor API usage
+
+---
+
+## 5. File Search Relevance Issues
+
+**Problem:**
+File search returns irrelevant or low-quality results
+
+**Causes:**
+- Vague queries
+- Poor file quality (OCR errors, formatting)
+- Not enough context
+
+**Solutions:**
+```typescript
+// ❌ BAD: Vague query
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Find pricing', // Too vague
+  tools: [{ type: 'file_search', file_ids: [fileId] }],
+});
+
+// ✅ GOOD: Specific query
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Find the monthly subscription pricing for the premium plan in the 2025 pricing document',
+  tools: [{ type: 'file_search', file_ids: [fileId] }],
+});
+
+// ✅ ALSO GOOD: Filter low-confidence results
+response.output.forEach(item => {
+  if (item.type === 'file_search_call') {
+    const highConfidence = item.results.filter(r => r.score > 0.7);
+    console.log('High confidence results:', highConfidence);
+  }
+});
+```
+
+**Prevention:**
+- Use specific, detailed queries
+- Upload high-quality documents (PDFs, Markdown)
+- Filter results by confidence score (> 0.7)
+- Provide context in query
+
+---
+
+## 6. Variable Substitution Errors (Reusable Prompts)
+
+**Error:**
+Variables not replaced in prompt templates
+
+**Cause:**
+Incorrect variable syntax or missing values
+
+**Solution:**
+```typescript
+// ❌ BAD: Incorrect variable syntax
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Hello {username}', // Not supported directly
+});
+
+// ✅ GOOD: Use template literals
+const username = 'Alice';
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: `Hello ${username}`, // ✅ JavaScript template literal
+});
+
+// ✅ ALSO GOOD: Build message dynamically
+function buildPrompt(vars: Record<string, string>) {
+  return `Hello ${vars.username}, your order ${vars.orderId} is ready.`;
+}
+
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: buildPrompt({ username: 'Alice', orderId: '12345' }),
+});
+```
+
+**Prevention:**
+- Use JavaScript template literals
+- Validate all variables before substitution
+- Provide defaults for optional variables
+
+---
+
+## 7. Chat Completions Migration Breaking Changes
+
+**Errors:**
+- `messages parameter not found`
+- `choices is undefined`
+- `system role not recognized`
+
+**Cause:**
+Using Chat Completions syntax with Responses API
+
+**Solution:**
+```typescript
+// ❌ BAD: Chat Completions syntax
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  messages: [{ role: 'system', content: 'You are helpful.' }], // Wrong
+});
+console.log(response.choices[0].message.content); // Wrong
+
+// ✅ GOOD: Responses syntax
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: [{ role: 'developer', content: 'You are helpful.' }], // ✅
+});
+console.log(response.output_text); // ✅
+```
+
+**Breaking Changes:**
+| Chat Completions | Responses API |
+|-----------------|---------------|
+| `messages` | `input` |
+| `system` role | `developer` role |
+| `choices[0].message.content` | `output_text` |
+| `/v1/chat/completions` | `/v1/responses` |
+
+**Prevention:**
+- Read migration guide: `references/migration-guide.md`
+- Update all references systematically
+- Test thoroughly after migration
+
+---
+
+## 8. Cost Tracking Confusion
+
+**Problem:**
+Billing different than expected
+
+**Cause:**
+Not accounting for tool tokens and conversation storage
+
+**Explanation:**
+- **Chat Completions**: input tokens + output tokens
+- **Responses API**: input tokens + output tokens + tool tokens + conversation storage
+
+**Solution:**
+```typescript
+const response = await openai.responses.create({
+  model: 'gpt-5',
+  input: 'Hello',
+  store: false, // ✅ Disable storage if not needed
+  tools: [{ type: 'code_interpreter' }],
+});
+
+// Monitor usage
+console.log('Input tokens:', response.usage.prompt_tokens);
+console.log('Output tokens:', response.usage.completion_tokens);
+console.log('Tool tokens:', response.usage.tool_tokens);
+console.log('Total tokens:', response.usage.total_tokens);
+
+// Calculate cost
+const inputCost = response.usage.prompt_tokens * 0.00001; // Example rate
+const outputCost = response.usage.completion_tokens * 0.00003;
+const toolCost = response.usage.tool_tokens * 0.00002;
+const totalCost = inputCost + outputCost + toolCost;
+console.log('Estimated cost: $' + totalCost.toFixed(4));
+```
+
+**Prevention:**
+- Monitor `usage.tool_tokens` in responses
+- Set `store: false` for one-off requests
+- Track conversation count (storage costs)
+- Implement cost alerts
+
+---
+
+## Common Error Response Formats
+
+### Authentication Error
+```json
+{
+  "error": {
+    "type": "authentication_error",
+    "message": "Invalid API key"
+  }
+}
+```
+
+### Rate Limit Error
+```json
+{
+  "error": {
+    "type": "rate_limit_error",
+    "message": "Rate limit exceeded",
+    "retry_after": 5
+  }
+}
+```
+
+### Invalid Request Error
+```json
+{
+  "error": {
+    "type": "invalid_request_error",
+    "message": "Conversation conv_xyz not found"
+  }
+}
+```
+
+### Server Error
+```json
+{
+  "error": {
+    "type": "server_error",
+    "message": "Internal server error"
+  }
+}
+```
+
+---
+
+## General Error Handling Pattern
+
+```typescript
+async function handleResponsesAPI(input: string) {
+  try {
+    const response = await openai.responses.create({
+      model: 'gpt-5',
+      input,
+    });
+
+    return response.output_text;
+  } catch (error: any) {
+    // Handle specific errors
+    switch (error.type) {
+      case 'rate_limit_error':
+        console.error('Rate limited, retry after:', error.retry_after);
+        break;
+      case 'mcp_connection_error':
+        console.error('MCP server failed:', error.message);
+        break;
+      case 'code_interpreter_timeout':
+        console.error('Code execution timed out, use background mode');
+        break;
+      case 'authentication_error':
+        console.error('Invalid API key');
+        break;
+      default:
+        console.error('Unexpected error:', error.message);
+    }
+
+    throw error; // Re-throw or handle
+  }
+}
+```
+
+---
+
+## Prevention Checklist
+
+- [ ] Use conversation IDs for multi-turn interactions
+- [ ] Provide valid MCP server URLs and tokens
+- [ ] Use `background: true` for tasks > 30 seconds
+- [ ] Implement exponential backoff for rate limits
+- [ ] Use specific queries for file search
+- [ ] Use template literals for variable substitution
+- [ ] Update Chat Completions syntax to Responses syntax
+- [ ] Monitor `usage.tool_tokens` and conversation count
+
+---
+
+## Getting Help
+
+If you encounter an error not covered here:
+
+1. Check official docs: https://platform.openai.com/docs/api-reference/responses
+2. Search OpenAI Community: https://community.openai.com
+3. Contact OpenAI Support: https://help.openai.com
+
+---
+
+**Last Updated**: 2025-10-25
--- a/scripts/check-versions.sh
+++ b/scripts/check-versions.sh
@@ -0,0 +1,84 @@
+#!/bin/bash
+
+# Check OpenAI SDK versions for Responses API compatibility
+# Minimum version: openai@5.19.0
+
+echo "=== OpenAI Responses API - Version Checker ==="
+echo ""
+
+# Check if npm is installed
+if ! command -v npm &> /dev/null; then
+    echo "❌ npm not found. Please install Node.js and npm."
+    exit 1
+fi
+
+# Check latest version
+echo "Checking latest openai package version..."
+LATEST_VERSION=$(npm view openai version 2>/dev/null)
+
+if [ -z "$LATEST_VERSION" ]; then
+    echo "❌ Could not fetch latest version. Check internet connection."
+    exit 1
+fi
+
+echo "📦 Latest version: $LATEST_VERSION"
+echo ""
+
+# Check if package.json exists
+if [ -f "package.json" ]; then
+    echo "Checking installed version..."
+    INSTALLED_VERSION=$(node -p "require('./package.json').dependencies?.openai || require('./package.json').devDependencies?.openai" 2>/dev/null)
+
+    if [ ! -z "$INSTALLED_VERSION" ]; then
+        # Remove ^ or ~ prefix
+        INSTALLED_VERSION=$(echo $INSTALLED_VERSION | sed 's/[\^~]//g')
+        echo "📦 Installed version: $INSTALLED_VERSION"
+
+        # Compare versions (simple string comparison for major.minor.patch)
+        REQUIRED_VERSION="5.19.0"
+
+        # Extract major.minor.patch
+        INSTALLED_MAJOR=$(echo $INSTALLED_VERSION | cut -d. -f1)
+        INSTALLED_MINOR=$(echo $INSTALLED_VERSION | cut -d. -f2)
+        INSTALLED_PATCH=$(echo $INSTALLED_VERSION | cut -d. -f3)
+
+        REQUIRED_MAJOR=$(echo $REQUIRED_VERSION | cut -d. -f1)
+        REQUIRED_MINOR=$(echo $REQUIRED_VERSION | cut -d. -f2)
+        REQUIRED_PATCH=$(echo $REQUIRED_VERSION | cut -d. -f3)
+
+        # Check compatibility
+        if [ "$INSTALLED_MAJOR" -gt "$REQUIRED_MAJOR" ] || \
+           ([ "$INSTALLED_MAJOR" -eq "$REQUIRED_MAJOR" ] && [ "$INSTALLED_MINOR" -gt "$REQUIRED_MINOR" ]) || \
+           ([ "$INSTALLED_MAJOR" -eq "$REQUIRED_MAJOR" ] && [ "$INSTALLED_MINOR" -eq "$REQUIRED_MINOR" ] && [ "$INSTALLED_PATCH" -ge "$REQUIRED_PATCH" ]); then
+            echo "✅ Version is compatible with Responses API (>= $REQUIRED_VERSION)"
+        else
+            echo "❌ Version is too old for Responses API"
+            echo "   Required: >= $REQUIRED_VERSION"
+            echo "   Installed: $INSTALLED_VERSION"
+            echo ""
+            echo "To upgrade: npm install openai@latest"
+            exit 1
+        fi
+    else
+        echo "⚠️  openai package not found in dependencies"
+        echo ""
+        echo "To install: npm install openai"
+    fi
+else
+    echo "⚠️  No package.json found"
+    echo ""
+    echo "To install: npm install openai"
+fi
+
+echo ""
+echo "=== Recommendations ==="
+echo ""
+echo "Minimum version for Responses API: openai@5.19.0"
+echo "Latest stable version: openai@$LATEST_VERSION"
+echo ""
+echo "To install/upgrade:"
+echo "  npm install openai@latest"
+echo ""
+echo "For Cloudflare Workers (no SDK needed):"
+echo "  Use native fetch API"
+echo ""
--- a/templates/background-mode.ts
+++ b/templates/background-mode.ts
@@ -0,0 +1,273 @@
+/**
+ * Background Mode Example
+ *
+ * Demonstrates long-running tasks with background mode (up to 10 minutes).
+ * Standard mode timeout: 60 seconds
+ * Background mode timeout: 10 minutes
+ */
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function basicBackgroundMode() {
+  console.log('=== Basic Background Mode ===\n');
+
+  // Start background task
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Analyze this 500-page document and provide a comprehensive summary',
+    background: true, // ✅ Extended timeout
+  });
+
+  console.log('Task started:', response.id);
+  console.log('Status:', response.status); // "in_progress"
+
+  // Poll for completion
+  let result = await openai.responses.retrieve(response.id);
+
+  while (result.status === 'in_progress') {
+    console.log('Still processing...');
+    await new Promise((resolve) => setTimeout(resolve, 5000)); // Check every 5 seconds
+    result = await openai.responses.retrieve(response.id);
+  }
+
+  if (result.status === 'completed') {
+    console.log('\nCompleted!');
+    console.log('Result:', result.output_text);
+  } else if (result.status === 'failed') {
+    console.error('Task failed:', result.error);
+  }
+}
+
+async function backgroundWithCodeInterpreter() {
+  console.log('=== Background Mode + Code Interpreter ===\n');
+
+  // Long-running data analysis
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Process this large dataset and generate detailed statistical analysis',
+    background: true,
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  console.log('Analysis started:', response.id);
+
+  // Poll with progress updates
+  let checks = 0;
+  let result = await openai.responses.retrieve(response.id);
+
+  while (result.status === 'in_progress') {
+    checks++;
+    console.log(`Check ${checks}: Still processing...`);
+    await new Promise((resolve) => setTimeout(resolve, 10000)); // Check every 10 seconds
+    result = await openai.responses.retrieve(response.id);
+  }
+
+  if (result.status === 'completed') {
+    console.log(`\nCompleted after ${checks} checks`);
+    console.log('Analysis:', result.output_text);
+  }
+}
+
+async function backgroundWithFileSearch() {
+  console.log('=== Background Mode + File Search ===\n');
+
+  // Upload large document
+  const file = await openai.files.create({
+    file: Buffer.from('Large document content...'),
+    purpose: 'assistants',
+  });
+
+  // Long-running file analysis
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Read this entire document and extract all key insights, metrics, and action items',
+    background: true,
+    tools: [{ type: 'file_search', file_ids: [file.id] }],
+  });
+
+  console.log('File analysis started:', response.id);
+
+  // Wait for completion
+  const result = await waitForCompletion(response.id);
+  console.log('Insights:', result.output_text);
+}
+
+async function backgroundWithWebSearch() {
+  console.log('=== Background Mode + Web Search ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Research the top 50 AI companies and create a comprehensive comparison report',
+    background: true,
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Research started:', response.id);
+
+  const result = await waitForCompletion(response.id);
+  console.log('Report:', result.output_text);
+}
+
+async function multipleBackgroundTasks() {
+  console.log('=== Multiple Background Tasks ===\n');
+
+  // Start multiple tasks in parallel
+  const task1 = openai.responses.create({
+    model: 'gpt-5',
+    input: 'Analyze Q1 financial data',
+    background: true,
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  const task2 = openai.responses.create({
+    model: 'gpt-5',
+    input: 'Research competitor landscape',
+    background: true,
+    tools: [{ type: 'web_search' }],
+  });
+
+  const task3 = openai.responses.create({
+    model: 'gpt-5',
+    input: 'Summarize customer feedback documents',
+    background: true,
+    tools: [{ type: 'file_search', file_ids: ['file_123'] }],
+  });
+
+  // Wait for all
+  const [response1, response2, response3] = await Promise.all([task1, task2, task3]);
+
+  console.log('All tasks started:');
+  console.log('Task 1:', response1.id);
+  console.log('Task 2:', response2.id);
+  console.log('Task 3:', response3.id);
+
+  // Wait for completion
+  const result1 = await waitForCompletion(response1.id);
+  const result2 = await waitForCompletion(response2.id);
+  const result3 = await waitForCompletion(response3.id);
+
+  console.log('\nAll tasks completed!');
+  console.log('Q1 Analysis:', result1.output_text);
+  console.log('Competitor Research:', result2.output_text);
+  console.log('Customer Feedback:', result3.output_text);
+}
+
+async function backgroundWithStatusTracking() {
+  console.log('=== Background Mode with Status Tracking ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Complex multi-step research task',
+    background: true,
+  });
+
+  console.log('Task ID:', response.id);
+
+  // Track status with detailed logging
+  let previousStatus = '';
+  let result = await openai.responses.retrieve(response.id);
+
+  while (result.status === 'in_progress') {
+    if (result.status !== previousStatus) {
+      console.log(`Status changed: ${previousStatus} → ${result.status}`);
+      previousStatus = result.status;
+    }
+
+    // Log additional info if available
+    if (result.metadata) {
+      console.log('Metadata:', result.metadata);
+    }
+
+    await new Promise((resolve) => setTimeout(resolve, 5000));
+    result = await openai.responses.retrieve(response.id);
+  }
+
+  console.log('Final status:', result.status);
+}
+
+async function handleBackgroundErrors() {
+  console.log('=== Error Handling ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Long-running task',
+    background: true,
+  });
+
+  try {
+    const result = await waitForCompletion(response.id, {
+      maxWaitTime: 5 * 60 * 1000, // 5 minutes max
+      checkInterval: 5000,
+    });
+
+    console.log('Success:', result.output_text);
+  } catch (error: any) {
+    if (error.message === 'TIMEOUT') {
+      console.error('Task exceeded maximum wait time');
+      console.error('Task ID:', response.id);
+      console.error('Check status later or increase timeout');
+    } else if (error.status === 'failed') {
+      console.error('Task failed:', error.error);
+    } else {
+      console.error('Unexpected error:', error);
+    }
+  }
+}
+
+async function cancelBackgroundTask() {
+  console.log('=== Cancel Background Task ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Long task',
+    background: true,
+  });
+
+  console.log('Task started:', response.id);
+
+  // Cancel after 10 seconds
+  await new Promise((resolve) => setTimeout(resolve, 10000));
+
+  try {
+    await openai.responses.cancel(response.id);
+    console.log('Task cancelled:', response.id);
+  } catch (error: any) {
+    console.error('Cancellation error:', error.message);
+  }
+}
+
+// Helper function
+async function waitForCompletion(
+  responseId: string,
+  options: { maxWaitTime?: number; checkInterval?: number } = {}
+): Promise<any> {
+  const { maxWaitTime = 10 * 60 * 1000, checkInterval = 5000 } = options;
+
+  const startTime = Date.now();
+  let result = await openai.responses.retrieve(responseId);
+
+  while (result.status === 'in_progress') {
+    if (Date.now() - startTime > maxWaitTime) {
+      throw new Error('TIMEOUT');
+    }
+
+    await new Promise((resolve) => setTimeout(resolve, checkInterval));
+    result = await openai.responses.retrieve(responseId);
+  }
+
+  if (result.status === 'failed') {
+    throw result;
+  }
+
+  return result;
+}
+
+// Run examples
+// basicBackgroundMode();
+// backgroundWithCodeInterpreter();
+// multipleBackgroundTasks();
+// handleBackgroundErrors();
--- a/templates/basic-response.ts
+++ b/templates/basic-response.ts
@@ -0,0 +1,64 @@
+/**
+ * Basic Response Example
+ *
+ * Simple text generation using the OpenAI Responses API.
+ * This is the simplest way to use the Responses API.
+ */
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function basicResponse() {
+  // Simple text input
+  const response = await openai.responses.create({
+    model: 'gpt-5', // or 'gpt-5-mini', 'gpt-4o'
+    input: 'What are the 5 Ds of dodgeball?',
+  });
+
+  // Get text output
+  console.log(response.output_text);
+
+  // Or inspect full output array
+  response.output.forEach((item) => {
+    console.log('Type:', item.type);
+    if (item.type === 'message') {
+      console.log('Content:', item.content);
+    }
+  });
+
+  // Check usage
+  console.log('Tokens used:', response.usage.total_tokens);
+}
+
+async function basicResponseWithMessages() {
+  // Using message array format (like Chat Completions)
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: [
+      { role: 'developer', content: 'You are a helpful assistant.' },
+      { role: 'user', content: 'Explain quantum computing in one sentence.' },
+    ],
+  });
+
+  console.log(response.output_text);
+}
+
+async function basicResponseWithOptions() {
+  // With additional options
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Write a haiku about coding',
+    store: false, // Don't store conversation (saves costs)
+    temperature: 0.7, // Creativity level (0-2)
+  });
+
+  console.log(response.output_text);
+}
+
+// Run examples
+basicResponse();
+// basicResponseWithMessages();
+// basicResponseWithOptions();
--- a/templates/cloudflare-worker.ts
+++ b/templates/cloudflare-worker.ts
@@ -0,0 +1,337 @@
+/**
+ * Cloudflare Workers Example
+ *
+ * Demonstrates using the Responses API in Cloudflare Workers without the SDK.
+ * Uses native fetch API for zero dependencies.
+ */
+
+export interface Env {
+  OPENAI_API_KEY: string;
+}
+
+export default {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    // Handle CORS preflight
+    if (request.method === 'OPTIONS') {
+      return new Response(null, {
+        headers: {
+          'Access-Control-Allow-Origin': '*',
+          'Access-Control-Allow-Methods': 'POST, GET, OPTIONS',
+          'Access-Control-Allow-Headers': 'Content-Type',
+        },
+      });
+    }
+
+    if (request.method !== 'POST') {
+      return new Response('Method not allowed', { status: 405 });
+    }
+
+    try {
+      const { input } = await request.json<{ input: string }>();
+
+      // Basic response
+      const response = await createResponse(env.OPENAI_API_KEY, {
+        model: 'gpt-5',
+        input,
+      });
+
+      return new Response(JSON.stringify(response), {
+        headers: {
+          'Content-Type': 'application/json',
+          'Access-Control-Allow-Origin': '*',
+        },
+      });
+    } catch (error: any) {
+      return new Response(JSON.stringify({ error: error.message }), {
+        status: 500,
+        headers: { 'Content-Type': 'application/json' },
+      });
+    }
+  },
+};
+
+// Helper: Create response
+async function createResponse(apiKey: string, params: any) {
+  const response = await fetch('https://api.openai.com/v1/responses', {
+    method: 'POST',
+    headers: {
+      'Authorization': `Bearer ${apiKey}`,
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify(params),
+  });
+
+  if (!response.ok) {
+    const error = await response.json();
+    throw new Error(error.error?.message || 'OpenAI API error');
+  }
+
+  return response.json();
+}
+
+// Example: Stateful conversation
+export const conversationWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { conversationId, input } = await request.json<{
+      conversationId?: string;
+      input: string;
+    }>();
+
+    // Create or use existing conversation
+    let convId = conversationId;
+    if (!convId) {
+      const conv = await createConversation(env.OPENAI_API_KEY);
+      convId = conv.id;
+    }
+
+    // Create response with conversation
+    const response = await createResponse(env.OPENAI_API_KEY, {
+      model: 'gpt-5',
+      conversation: convId,
+      input,
+    });
+
+    return new Response(
+      JSON.stringify({
+        conversationId: convId,
+        output: response.output_text,
+      }),
+      {
+        headers: { 'Content-Type': 'application/json' },
+      }
+    );
+  },
+};
+
+// Helper: Create conversation
+async function createConversation(apiKey: string) {
+  const response = await fetch('https://api.openai.com/v1/conversations', {
+    method: 'POST',
+    headers: {
+      'Authorization': `Bearer ${apiKey}`,
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify({}),
+  });
+
+  return response.json();
+}
+
+// Example: With MCP tools
+export const mcpWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { input } = await request.json<{ input: string }>();
+
+    const response = await createResponse(env.OPENAI_API_KEY, {
+      model: 'gpt-5',
+      input,
+      tools: [
+        {
+          type: 'mcp',
+          server_label: 'stripe',
+          server_url: 'https://mcp.stripe.com',
+          authorization: env.STRIPE_OAUTH_TOKEN,
+        },
+      ],
+    });
+
+    return new Response(JSON.stringify(response), {
+      headers: { 'Content-Type': 'application/json' },
+    });
+  },
+};
+
+// Example: With Code Interpreter
+export const codeInterpreterWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { input } = await request.json<{ input: string }>();
+
+    const response = await createResponse(env.OPENAI_API_KEY, {
+      model: 'gpt-5',
+      input,
+      tools: [{ type: 'code_interpreter' }],
+    });
+
+    return new Response(JSON.stringify(response), {
+      headers: { 'Content-Type': 'application/json' },
+    });
+  },
+};
+
+// Example: With File Search
+export const fileSearchWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { input, fileIds } = await request.json<{
+      input: string;
+      fileIds: string[];
+    }>();
+
+    const response = await createResponse(env.OPENAI_API_KEY, {
+      model: 'gpt-5',
+      input,
+      tools: [{ type: 'file_search', file_ids: fileIds }],
+    });
+
+    return new Response(JSON.stringify(response), {
+      headers: { 'Content-Type': 'application/json' },
+    });
+  },
+};
+
+// Example: With Web Search
+export const webSearchWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { input } = await request.json<{ input: string }>();
+
+    const response = await createResponse(env.OPENAI_API_KEY, {
+      model: 'gpt-5',
+      input,
+      tools: [{ type: 'web_search' }],
+    });
+
+    return new Response(JSON.stringify(response), {
+      headers: { 'Content-Type': 'application/json' },
+    });
+  },
+};
+
+// Example: Background mode
+export const backgroundWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { input, responseId } = await request.json<{
+      input?: string;
+      responseId?: string;
+    }>();
+
+    // Start background task
+    if (input) {
+      const response = await createResponse(env.OPENAI_API_KEY, {
+        model: 'gpt-5',
+        input,
+        background: true,
+      });
+
+      return new Response(
+        JSON.stringify({
+          responseId: response.id,
+          status: response.status,
+        }),
+        {
+          headers: { 'Content-Type': 'application/json' },
+        }
+      );
+    }
+
+    // Check status
+    if (responseId) {
+      const response = await fetch(
+        `https://api.openai.com/v1/responses/${responseId}`,
+        {
+          headers: {
+            'Authorization': `Bearer ${env.OPENAI_API_KEY}`,
+          },
+        }
+      );
+
+      const data = await response.json();
+
+      return new Response(JSON.stringify(data), {
+        headers: { 'Content-Type': 'application/json' },
+      });
+    }
+
+    return new Response('Invalid request', { status: 400 });
+  },
+};
+
+// Example: Error handling
+export const errorHandlingWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    try {
+      const { input } = await request.json<{ input: string }>();
+
+      const response = await createResponse(env.OPENAI_API_KEY, {
+        model: 'gpt-5',
+        input,
+      });
+
+      return new Response(JSON.stringify(response), {
+        headers: { 'Content-Type': 'application/json' },
+      });
+    } catch (error: any) {
+      // Handle specific errors
+      if (error.type === 'rate_limit_error') {
+        return new Response(
+          JSON.stringify({ error: 'Rate limit exceeded', retry_after: error.retry_after }),
+          {
+            status: 429,
+            headers: { 'Content-Type': 'application/json' },
+          }
+        );
+      }
+
+      if (error.type === 'mcp_connection_error') {
+        return new Response(
+          JSON.stringify({ error: 'MCP server connection failed' }),
+          {
+            status: 502,
+            headers: { 'Content-Type': 'application/json' },
+          }
+        );
+      }
+
+      // Generic error
+      return new Response(
+        JSON.stringify({ error: error.message || 'Internal error' }),
+        {
+          status: 500,
+          headers: { 'Content-Type': 'application/json' },
+        }
+      );
+    }
+  },
+};
+
+// Example: Polymorphic outputs
+export const polymorphicWorker = {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { input } = await request.json<{ input: string }>();
+
+    const response = await createResponse(env.OPENAI_API_KEY, {
+      model: 'gpt-5',
+      input,
+      tools: [{ type: 'code_interpreter' }, { type: 'web_search' }],
+    });
+
+    // Process different output types
+    const processedOutput: any = {
+      text: response.output_text,
+      reasoning: [],
+      toolCalls: [],
+    };
+
+    response.output.forEach((item: any) => {
+      if (item.type === 'reasoning') {
+        processedOutput.reasoning.push(item.summary[0].text);
+      }
+      if (item.type === 'code_interpreter_call') {
+        processedOutput.toolCalls.push({
+          type: 'code_interpreter',
+          input: item.input,
+          output: item.output,
+        });
+      }
+      if (item.type === 'web_search_call') {
+        processedOutput.toolCalls.push({
+          type: 'web_search',
+          query: item.query,
+          results: item.results,
+        });
+      }
+    });
+
+    return new Response(JSON.stringify(processedOutput), {
+      headers: { 'Content-Type': 'application/json' },
+    });
+  },
+};
--- a/templates/code-interpreter.ts
+++ b/templates/code-interpreter.ts
@@ -0,0 +1,227 @@
+/**
+ * Code Interpreter Example
+ *
+ * Demonstrates server-side Python code execution for data analysis,
+ * calculations, and visualizations.
+ */
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function basicCalculation() {
+  console.log('=== Basic Calculation ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Calculate the mean, median, and mode of: 10, 20, 30, 40, 50',
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  console.log('Response:', response.output_text);
+
+  // Inspect code execution
+  response.output.forEach((item) => {
+    if (item.type === 'code_interpreter_call') {
+      console.log('\nCode executed:');
+      console.log(item.input);
+      console.log('\nResult:', item.output);
+    }
+  });
+}
+
+async function dataAnalysis() {
+  console.log('=== Data Analysis ===\n');
+
+  const salesData = [
+    { month: 'Jan', revenue: 10000 },
+    { month: 'Feb', revenue: 12000 },
+    { month: 'Mar', revenue: 11500 },
+    { month: 'Apr', revenue: 13000 },
+    { month: 'May', revenue: 14500 },
+    { month: 'Jun', revenue: 16000 },
+  ];
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: `Analyze this sales data and provide insights:
+${JSON.stringify(salesData, null, 2)}
+
+Calculate:
+1. Total revenue
+2. Average monthly revenue
+3. Growth rate from Jan to Jun
+4. Best performing month`,
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  console.log('Analysis:', response.output_text);
+}
+
+async function chartGeneration() {
+  console.log('=== Chart Generation ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: `Create a bar chart showing monthly revenue:
+- Jan: $10,000
+- Feb: $12,000
+- Mar: $11,500
+- Apr: $13,000
+- May: $14,500
+- Jun: $16,000`,
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  console.log('Response:', response.output_text);
+
+  // Find chart output
+  response.output.forEach((item) => {
+    if (item.type === 'code_interpreter_call') {
+      console.log('\nChart code:');
+      console.log(item.input);
+
+      // Check for file outputs (charts saved as files)
+      if (item.outputs) {
+        item.outputs.forEach((output) => {
+          if (output.type === 'image') {
+            console.log('Chart URL:', output.url);
+          }
+        });
+      }
+    }
+  });
+}
+
+async function fileProcessing() {
+  console.log('=== File Processing ===\n');
+
+  // Upload file first
+  const file = await openai.files.create({
+    file: Buffer.from('name,age,city\nAlice,30,NYC\nBob,25,LA\nCharlie,35,Chicago'),
+    purpose: 'assistants',
+  });
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Analyze the CSV file and tell me the average age',
+    tools: [
+      {
+        type: 'code_interpreter',
+        file_ids: [file.id], // ✅ Access uploaded file
+      },
+    ],
+  });
+
+  console.log('Analysis:', response.output_text);
+}
+
+async function complexCalculation() {
+  console.log('=== Complex Calculation ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: `Solve this math problem step by step:
+
+A company's revenue grows by 15% each year. If the revenue in year 1 is $100,000:
+1. What will the revenue be in year 5?
+2. What is the total revenue across all 5 years?
+3. What year will the revenue first exceed $200,000?`,
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  console.log('Solution:', response.output_text);
+
+  // Show step-by-step reasoning
+  response.output.forEach((item) => {
+    if (item.type === 'reasoning') {
+      console.log('\nReasoning:', item.summary[0].text);
+    }
+    if (item.type === 'code_interpreter_call') {
+      console.log('\nCode:', item.input);
+      console.log('Result:', item.output);
+    }
+  });
+}
+
+async function statisticalAnalysis() {
+  console.log('=== Statistical Analysis ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: `Perform statistical analysis on this dataset:
+[12, 15, 18, 20, 22, 25, 28, 30, 35, 40]
+
+Calculate:
+1. Standard deviation
+2. Variance
+3. 25th, 50th, 75th percentiles
+4. Outliers (if any)`,
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  console.log('Analysis:', response.output_text);
+}
+
+async function codeInterpreterWithTimeout() {
+  console.log('=== Code Interpreter with Background Mode ===\n');
+
+  // For long-running code, use background mode
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Process this large dataset and generate a comprehensive report',
+    background: true, // ✅ Extended timeout for long-running code
+    tools: [{ type: 'code_interpreter' }],
+  });
+
+  // Poll for completion
+  let result = await openai.responses.retrieve(response.id);
+
+  while (result.status === 'in_progress') {
+    console.log('Still processing...');
+    await new Promise((resolve) => setTimeout(resolve, 5000));
+    result = await openai.responses.retrieve(response.id);
+  }
+
+  if (result.status === 'completed') {
+    console.log('Result:', result.output_text);
+  } else {
+    console.error('Failed:', result.error);
+  }
+}
+
+async function handleCodeInterpreterErrors() {
+  console.log('=== Error Handling ===\n');
+
+  try {
+    const response = await openai.responses.create({
+      model: 'gpt-5',
+      input: 'Run this Python code: import invalid_module',
+      tools: [{ type: 'code_interpreter' }],
+    });
+
+    // Check for execution errors in output
+    response.output.forEach((item) => {
+      if (item.type === 'code_interpreter_call' && item.error) {
+        console.error('Code execution error:', item.error);
+      }
+    });
+  } catch (error: any) {
+    if (error.type === 'code_interpreter_timeout') {
+      console.error('Code execution timed out. Use background mode for long tasks.');
+    } else {
+      console.error('Error:', error.message);
+    }
+  }
+}
+
+// Run examples
+basicCalculation();
+// dataAnalysis();
+// chartGeneration();
+// fileProcessing();
+// complexCalculation();
+// statisticalAnalysis();
+// codeInterpreterWithTimeout();
--- a/templates/file-search.ts
+++ b/templates/file-search.ts
@@ -0,0 +1,271 @@
+/**
+ * File Search Example
+ *
+ * Demonstrates RAG (Retrieval-Augmented Generation) without building
+ * your own vector store. OpenAI handles embeddings and search automatically.
+ */
+
+import OpenAI from 'openai';
+import fs from 'fs';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function basicFileSearch() {
+  console.log('=== Basic File Search ===\n');
+
+  // 1. Upload file (one-time setup)
+  const file = await openai.files.create({
+    file: fs.createReadStream('./knowledge-base.pdf'),
+    purpose: 'assistants',
+  });
+
+  console.log('File uploaded:', file.id);
+
+  // 2. Search file for information
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What does the document say about pricing?',
+    tools: [
+      {
+        type: 'file_search',
+        file_ids: [file.id],
+      },
+    ],
+  });
+
+  console.log('Answer:', response.output_text);
+
+  // 3. Inspect search results
+  response.output.forEach((item) => {
+    if (item.type === 'file_search_call') {
+      console.log('\nSearch query:', item.query);
+      console.log('Relevant chunks:', item.results.length);
+
+      item.results.forEach((result, idx) => {
+        console.log(`\nChunk ${idx + 1}:`);
+        console.log('Text:', result.text.substring(0, 200) + '...');
+        console.log('Score:', result.score);
+        console.log('File:', result.file_id);
+      });
+    }
+  });
+}
+
+async function multipleFileSearch() {
+  console.log('=== Multiple File Search ===\n');
+
+  // Upload multiple files
+  const file1 = await openai.files.create({
+    file: fs.createReadStream('./product-guide.pdf'),
+    purpose: 'assistants',
+  });
+
+  const file2 = await openai.files.create({
+    file: fs.createReadStream('./pricing-doc.pdf'),
+    purpose: 'assistants',
+  });
+
+  const file3 = await openai.files.create({
+    file: fs.createReadStream('./faq.pdf'),
+    purpose: 'assistants',
+  });
+
+  // Search across all files
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What are the key features and how much does the premium plan cost?',
+    tools: [
+      {
+        type: 'file_search',
+        file_ids: [file1.id, file2.id, file3.id], // ✅ Multiple files
+      },
+    ],
+  });
+
+  console.log('Answer (synthesized from all files):', response.output_text);
+}
+
+async function conversationalFileSearch() {
+  console.log('=== Conversational File Search ===\n');
+
+  // Upload knowledge base
+  const file = await openai.files.create({
+    file: fs.createReadStream('./company-handbook.pdf'),
+    purpose: 'assistants',
+  });
+
+  // Create conversation
+  const conv = await openai.conversations.create();
+
+  // First question
+  const response1 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'What is the PTO policy?',
+    tools: [{ type: 'file_search', file_ids: [file.id] }],
+  });
+
+  console.log('Q1:', response1.output_text);
+
+  // Follow-up question (model remembers previous answer)
+  const response2 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'How do I request it?',
+    tools: [{ type: 'file_search', file_ids: [file.id] }],
+  });
+
+  console.log('Q2:', response2.output_text);
+  // Model knows "it" refers to PTO from previous turn
+}
+
+async function fileSearchWithCitations() {
+  console.log('=== File Search with Citations ===\n');
+
+  const file = await openai.files.create({
+    file: fs.createReadStream('./research-paper.pdf'),
+    purpose: 'assistants',
+  });
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Summarize the key findings and provide citations',
+    tools: [{ type: 'file_search', file_ids: [file.id] }],
+  });
+
+  console.log('Summary:', response.output_text);
+
+  // Extract citations
+  response.output.forEach((item) => {
+    if (item.type === 'file_search_call') {
+      console.log('\nCitations:');
+      item.results.forEach((result, idx) => {
+        console.log(`[${idx + 1}] File: ${result.file_id}, Page: ${result.page || 'N/A'}`);
+      });
+    }
+  });
+}
+
+async function filterSearchResults() {
+  console.log('=== Filter Search Results by Relevance ===\n');
+
+  const file = await openai.files.create({
+    file: fs.createReadStream('./large-document.pdf'),
+    purpose: 'assistants',
+  });
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Find all mentions of "quarterly revenue" in the document',
+    tools: [{ type: 'file_search', file_ids: [file.id] }],
+  });
+
+  // Filter high-confidence results
+  response.output.forEach((item) => {
+    if (item.type === 'file_search_call') {
+      const highConfidence = item.results.filter((r) => r.score > 0.7);
+
+      console.log(`Found ${highConfidence.length} high-confidence matches:`);
+      highConfidence.forEach((result) => {
+        console.log('Text:', result.text);
+        console.log('Score:', result.score);
+        console.log('---');
+      });
+    }
+  });
+}
+
+async function supportedFileTypes() {
+  console.log('=== Supported File Types ===\n');
+
+  // Upload different file types
+  const pdfFile = await openai.files.create({
+    file: fs.createReadStream('./document.pdf'),
+    purpose: 'assistants',
+  });
+
+  const textFile = await openai.files.create({
+    file: fs.createReadStream('./notes.txt'),
+    purpose: 'assistants',
+  });
+
+  const markdownFile = await openai.files.create({
+    file: fs.createReadStream('./README.md'),
+    purpose: 'assistants',
+  });
+
+  const codeFile = await openai.files.create({
+    file: fs.createReadStream('./main.ts'),
+    purpose: 'assistants',
+  });
+
+  // Search across different file types
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Find information about the authentication system',
+    tools: [
+      {
+        type: 'file_search',
+        file_ids: [pdfFile.id, textFile.id, markdownFile.id, codeFile.id],
+      },
+    ],
+  });
+
+  console.log('Answer:', response.output_text);
+}
+
+async function handleFileSearchErrors() {
+  console.log('=== Error Handling ===\n');
+
+  try {
+    const response = await openai.responses.create({
+      model: 'gpt-5',
+      input: 'Search for information',
+      tools: [
+        {
+          type: 'file_search',
+          file_ids: ['file_invalid'], // ❌ Invalid file ID
+        },
+      ],
+    });
+  } catch (error: any) {
+    if (error.type === 'invalid_request_error') {
+      console.error('File not found. Upload file first.');
+    } else {
+      console.error('Error:', error.message);
+    }
+  }
+}
+
+async function listUploadedFiles() {
+  console.log('=== List Uploaded Files ===\n');
+
+  const files = await openai.files.list({
+    purpose: 'assistants',
+  });
+
+  console.log(`Found ${files.data.length} files:`);
+  files.data.forEach((file) => {
+    console.log('ID:', file.id);
+    console.log('Filename:', file.filename);
+    console.log('Size:', file.bytes, 'bytes');
+    console.log('Created:', new Date(file.created_at * 1000));
+    console.log('---');
+  });
+}
+
+async function deleteFile(fileId: string) {
+  // Delete file (cleanup)
+  await openai.files.delete(fileId);
+  console.log('File deleted:', fileId);
+}
+
+// Run examples
+// basicFileSearch();
+// multipleFileSearch();
+// conversationalFileSearch();
+// fileSearchWithCitations();
+// filterSearchResults();
+// listUploadedFiles();
--- a/templates/image-generation.ts
+++ b/templates/image-generation.ts
@@ -0,0 +1,241 @@
+/**
+ * Image Generation Example
+ *
+ * Demonstrates integrated DALL-E image generation in the Responses API.
+ */
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function basicImageGeneration() {
+  console.log('=== Basic Image Generation ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Create an image of a futuristic cityscape at sunset',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Response:', response.output_text);
+
+  // Find image in output
+  response.output.forEach((item) => {
+    if (item.type === 'image_generation_call') {
+      console.log('\nPrompt used:', item.prompt);
+      console.log('Image URL:', item.output.url);
+      console.log('Image expires in 1 hour');
+    }
+  });
+}
+
+async function conversationalImageGeneration() {
+  console.log('=== Conversational Image Generation ===\n');
+
+  // Create conversation
+  const conv = await openai.conversations.create();
+
+  // First request
+  const response1 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'Create an image of a cartoon cat wearing a wizard hat',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Turn 1:', response1.output_text);
+
+  // Modification request (model remembers previous image)
+  const response2 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'Make it more colorful and add a magic wand',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Turn 2:', response2.output_text);
+  // Model generates new image with modifications
+}
+
+async function multipleImages() {
+  console.log('=== Multiple Images ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Create 3 different logo designs for a tech startup',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Response:', response.output_text);
+
+  // Collect all images
+  const images: string[] = [];
+  response.output.forEach((item) => {
+    if (item.type === 'image_generation_call') {
+      images.push(item.output.url);
+    }
+  });
+
+  console.log(`\nGenerated ${images.length} images:`);
+  images.forEach((url, idx) => {
+    console.log(`Image ${idx + 1}: ${url}`);
+  });
+}
+
+async function imageWithSpecifications() {
+  console.log('=== Image with Specifications ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: `Create an image with these specifications:
+- Subject: Modern minimalist office space
+- Style: Photorealistic
+- Lighting: Natural daylight from large windows
+- Colors: Neutral tones (white, gray, wood)
+- Details: Include plants and modern furniture`,
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Response:', response.output_text);
+}
+
+async function imageForPresentation() {
+  console.log('=== Image for Presentation ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Create a professional infographic showing the growth of AI adoption from 2020 to 2025',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Response:', response.output_text);
+}
+
+async function saveImageToFile() {
+  console.log('=== Save Image to File ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Create an image of a mountain landscape',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  // Find and download image
+  for (const item of response.output) {
+    if (item.type === 'image_generation_call') {
+      const imageUrl = item.output.url;
+      console.log('Downloading image from:', imageUrl);
+
+      // Download image
+      const imageResponse = await fetch(imageUrl);
+      const imageBuffer = await imageResponse.arrayBuffer();
+
+      // Save to file
+      const fs = await import('fs');
+      fs.writeFileSync('./generated-image.png', Buffer.from(imageBuffer));
+
+      console.log('Image saved to: ./generated-image.png');
+    }
+  }
+}
+
+async function iterativeImageRefinement() {
+  console.log('=== Iterative Image Refinement ===\n');
+
+  const conv = await openai.conversations.create();
+
+  // Initial image
+  const response1 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'Create a logo for a coffee shop',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Initial design:', response1.output_text);
+
+  // Refinement 1
+  const response2 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'Make the colors warmer and add a coffee bean illustration',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Refinement 1:', response2.output_text);
+
+  // Refinement 2
+  const response3 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'Perfect! Can you make it circular instead of square?',
+    tools: [{ type: 'image_generation' }],
+  });
+
+  console.log('Final design:', response3.output_text);
+}
+
+async function handleImageGenerationErrors() {
+  console.log('=== Error Handling ===\n');
+
+  try {
+    const response = await openai.responses.create({
+      model: 'gpt-5',
+      input: 'Create an image [multiple requests]',
+      tools: [{ type: 'image_generation' }],
+    });
+
+    console.log('Success:', response.output_text);
+  } catch (error: any) {
+    if (error.type === 'rate_limit_error') {
+      console.error('DALL-E rate limit exceeded');
+      console.error('Retry after:', error.headers?.['retry-after']);
+
+      // Implement exponential backoff
+      const delay = parseInt(error.headers?.['retry-after'] || '5') * 1000;
+      console.log(`Waiting ${delay}ms before retry...`);
+      await new Promise((resolve) => setTimeout(resolve, delay));
+
+      // Retry request
+      const retryResponse = await openai.responses.create({
+        model: 'gpt-5',
+        input: 'Create an image',
+        tools: [{ type: 'image_generation' }],
+      });
+
+      console.log('Retry success:', retryResponse.output_text);
+    } else if (error.type === 'content_policy_violation') {
+      console.error('Image prompt violates content policy');
+      console.error('Please revise prompt to comply with guidelines');
+    } else {
+      console.error('Error:', error.message);
+    }
+  }
+}
+
+async function combinedImageAndAnalysis() {
+  console.log('=== Image Generation + Code Interpreter ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Create a chart showing sales growth from 2020-2025, then generate an image visualization',
+    tools: [
+      { type: 'code_interpreter' },
+      { type: 'image_generation' },
+    ],
+  });
+
+  console.log('Response:', response.output_text);
+
+  // Model uses code interpreter for data, then image generation for visualization
+}
+
+// Run examples
+basicImageGeneration();
+// conversationalImageGeneration();
+// multipleImages();
+// imageWithSpecifications();
+// saveImageToFile();
+// iterativeImageRefinement();
--- a/templates/mcp-integration.ts
+++ b/templates/mcp-integration.ts
@@ -0,0 +1,203 @@
+/**
+ * MCP Server Integration Example
+ *
+ * Demonstrates how to connect to external MCP (Model Context Protocol) servers
+ * for tool integration. MCP is built into the Responses API.
+ */
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function basicMCPIntegration() {
+  console.log('=== Basic MCP Integration ===\n');
+
+  // Connect to a public MCP server (dice rolling example)
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Roll 2d6 dice for me',
+    tools: [
+      {
+        type: 'mcp',
+        server_label: 'dice',
+        server_url: 'https://dmcp.example.com', // Replace with real MCP server
+      },
+    ],
+  });
+
+  console.log('Response:', response.output_text);
+
+  // Inspect MCP tool calls
+  response.output.forEach((item) => {
+    if (item.type === 'mcp_list_tools') {
+      console.log('\nDiscovered tools:', item.tools);
+    }
+    if (item.type === 'mcp_call') {
+      console.log('\nTool called:', item.name);
+      console.log('Arguments:', item.arguments);
+      console.log('Output:', item.output);
+    }
+  });
+}
+
+async function mcpWithAuthentication() {
+  console.log('=== MCP with OAuth Authentication ===\n');
+
+  // Connect to Stripe MCP server (requires OAuth token)
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Create a payment link for $20',
+    tools: [
+      {
+        type: 'mcp',
+        server_label: 'stripe',
+        server_url: 'https://mcp.stripe.com',
+        authorization: process.env.STRIPE_OAUTH_ACCESS_TOKEN, // ✅ OAuth token
+      },
+    ],
+  });
+
+  console.log('Response:', response.output_text);
+
+  // Find payment link in output
+  response.output.forEach((item) => {
+    if (item.type === 'mcp_call' && item.name === 'create_payment_link') {
+      console.log('\nPayment link created:', item.output);
+    }
+  });
+}
+
+async function multipleMCPServers() {
+  console.log('=== Multiple MCP Servers ===\n');
+
+  // Connect to multiple MCP servers at once
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Check my Stripe balance and create a payment link for the remaining amount',
+    tools: [
+      {
+        type: 'mcp',
+        server_label: 'stripe',
+        server_url: 'https://mcp.stripe.com',
+        authorization: process.env.STRIPE_OAUTH_TOKEN,
+      },
+      {
+        type: 'mcp',
+        server_label: 'database',
+        server_url: 'https://db-mcp.example.com',
+        authorization: process.env.DB_API_KEY,
+      },
+    ],
+  });
+
+  console.log('Response:', response.output_text);
+}
+
+async function mcpWithConversation() {
+  console.log('=== MCP with Stateful Conversation ===\n');
+
+  // Create conversation
+  const conv = await openai.conversations.create();
+
+  // First turn: Use MCP tool
+  const response1 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'Create a $50 payment link for premium subscription',
+    tools: [
+      {
+        type: 'mcp',
+        server_label: 'stripe',
+        server_url: 'https://mcp.stripe.com',
+        authorization: process.env.STRIPE_OAUTH_TOKEN,
+      },
+    ],
+  });
+
+  console.log('Turn 1:', response1.output_text);
+
+  // Second turn: Model remembers previous action
+  const response2 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'Can you show me the details of that payment link?',
+  });
+
+  console.log('Turn 2:', response2.output_text);
+  // Model recalls payment link from turn 1
+}
+
+async function handleMCPErrors() {
+  console.log('=== MCP Error Handling ===\n');
+
+  try {
+    const response = await openai.responses.create({
+      model: 'gpt-5',
+      input: 'Use the Stripe tool',
+      tools: [
+        {
+          type: 'mcp',
+          server_label: 'stripe',
+          server_url: 'https://mcp.stripe.com',
+          authorization: process.env.STRIPE_OAUTH_TOKEN,
+        },
+      ],
+    });
+
+    console.log('Success:', response.output_text);
+  } catch (error: any) {
+    // Handle specific MCP errors
+    if (error.type === 'mcp_connection_error') {
+      console.error('MCP server connection failed:', error.message);
+      console.error('Check server URL and network connectivity');
+    } else if (error.type === 'mcp_authentication_error') {
+      console.error('MCP authentication failed:', error.message);
+      console.error('Verify authorization token is valid and not expired');
+    } else {
+      console.error('Unexpected error:', error);
+    }
+  }
+}
+
+/**
+ * Custom MCP Server Example
+ *
+ * If you want to build your own MCP server, it needs to implement:
+ * 1. POST /mcp/list_tools - Return available tools
+ * 2. POST /mcp/call_tool - Execute tool and return result
+ *
+ * Example MCP server response format:
+ */
+const exampleMCPListToolsResponse = {
+  tools: [
+    {
+      name: 'get_weather',
+      description: 'Get current weather for a city',
+      input_schema: {
+        type: 'object',
+        properties: {
+          city: { type: 'string' },
+          units: { type: 'string', enum: ['celsius', 'fahrenheit'] },
+        },
+        required: ['city'],
+      },
+    },
+  ],
+};
+
+const exampleMCPCallToolResponse = {
+  result: {
+    temperature: 72,
+    condition: 'sunny',
+    humidity: 45,
+  },
+};
+
+// Run examples
+basicMCPIntegration();
+// mcpWithAuthentication();
+// multipleMCPServers();
+// mcpWithConversation();
+// handleMCPErrors();
--- a/templates/package.json
+++ b/templates/package.json
@@ -0,0 +1,30 @@
+{
+  "name": "openai-responses-examples",
+  "version": "1.0.0",
+  "description": "OpenAI Responses API Examples",
+  "type": "module",
+  "scripts": {
+    "basic": "tsx templates/basic-response.ts",
+    "conversation": "tsx templates/stateful-conversation.ts",
+    "mcp": "tsx templates/mcp-integration.ts",
+    "code": "tsx templates/code-interpreter.ts",
+    "file": "tsx templates/file-search.ts",
+    "web": "tsx templates/web-search.ts",
+    "image": "tsx templates/image-generation.ts",
+    "background": "tsx templates/background-mode.ts",
+    "worker": "wrangler dev templates/cloudflare-worker.ts"
+  },
+  "dependencies": {
+    "openai": "^5.19.1"
+  },
+  "devDependencies": {
+    "@cloudflare/workers-types": "^5.0.0",
+    "@types/node": "^20.0.0",
+    "tsx": "^4.7.1",
+    "typescript": "^5.3.3",
+    "wrangler": "^3.95.0"
+  },
+  "engines": {
+    "node": ">=18.0.0"
+  }
+}
--- a/templates/stateful-conversation.ts
+++ b/templates/stateful-conversation.ts
@@ -0,0 +1,120 @@
+/**
+ * Stateful Conversation Example
+ *
+ * Demonstrates automatic state management using conversation IDs.
+ * The model remembers previous turns automatically.
+ */
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function automaticStateManagement() {
+  console.log('=== Automatic State Management ===\n');
+
+  // 1. Create conversation
+  const conversation = await openai.conversations.create({
+    metadata: {
+      user_id: 'user_123',
+      session_type: 'support',
+    },
+  });
+
+  console.log('Conversation ID:', conversation.id);
+
+  // 2. First turn
+  const response1 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conversation.id, // ✅ Reuse this ID
+    input: 'What are the 5 Ds of dodgeball?',
+  });
+
+  console.log('Turn 1:', response1.output_text);
+  console.log('');
+
+  // 3. Second turn - model remembers context
+  const response2 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conversation.id, // ✅ Same ID
+    input: 'Tell me more about the first one',
+  });
+
+  console.log('Turn 2:', response2.output_text);
+  // Model knows "first one" refers to first D from previous turn
+  console.log('');
+
+  // 4. Third turn - still remembers everything
+  const response3 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conversation.id, // ✅ Same ID
+    input: 'What was my original question?',
+  });
+
+  console.log('Turn 3:', response3.output_text);
+  // Model recalls original question from turn 1
+}
+
+async function manualStateManagement() {
+  console.log('=== Manual State Management ===\n');
+
+  // Alternative: Manually manage history array
+  let history = [
+    { role: 'user', content: 'Tell me a joke' },
+  ];
+
+  // First turn
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: history,
+    store: true, // Optional: store for retrieval later
+  });
+
+  console.log('Turn 1:', response.output_text);
+
+  // Add response to history
+  history = [
+    ...history,
+    ...response.output.map((el) => ({
+      role: el.role,
+      content: el.content,
+    })),
+  ];
+
+  // Second turn
+  history.push({ role: 'user', content: 'Tell me another' });
+
+  const secondResponse = await openai.responses.create({
+    model: 'gpt-5',
+    input: history, // ✅ Full history
+  });
+
+  console.log('Turn 2:', secondResponse.output_text);
+}
+
+async function listConversations() {
+  // List all conversations (for user dashboard)
+  const conversations = await openai.conversations.list({
+    limit: 10,
+  });
+
+  console.log('=== Recent Conversations ===');
+  conversations.data.forEach((conv) => {
+    console.log('ID:', conv.id);
+    console.log('Created:', new Date(conv.created_at * 1000));
+    console.log('Metadata:', conv.metadata);
+    console.log('');
+  });
+}
+
+async function deleteConversation(conversationId: string) {
+  // Delete conversation (cleanup)
+  await openai.conversations.delete(conversationId);
+  console.log('Conversation deleted:', conversationId);
+}
+
+// Run examples
+automaticStateManagement();
+// manualStateManagement();
+// listConversations();
--- a/templates/web-search.ts
+++ b/templates/web-search.ts
@@ -0,0 +1,195 @@
+/**
+ * Web Search Example
+ *
+ * Demonstrates real-time web search for current information.
+ * No cutoff date limitations.
+ */
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+async function basicWebSearch() {
+  console.log('=== Basic Web Search ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What are the latest updates on GPT-5?',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Answer:', response.output_text);
+
+  // Inspect search results
+  response.output.forEach((item) => {
+    if (item.type === 'web_search_call') {
+      console.log('\nSearch query:', item.query);
+      console.log('Sources:', item.results.length);
+
+      item.results.forEach((result, idx) => {
+        console.log(`\nSource ${idx + 1}:`);
+        console.log('Title:', result.title);
+        console.log('URL:', result.url);
+        console.log('Snippet:', result.snippet);
+      });
+    }
+  });
+}
+
+async function currentEvents() {
+  console.log('=== Current Events ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What are the top tech news stories today?',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('News summary:', response.output_text);
+}
+
+async function factChecking() {
+  console.log('=== Fact Checking ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Is it true that GPT-5 was released in 2025? Find recent sources.',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Fact check:', response.output_text);
+
+  // Get source citations
+  response.output.forEach((item) => {
+    if (item.type === 'web_search_call') {
+      console.log('\nSources:');
+      item.results.forEach((result) => {
+        console.log('-', result.url);
+      });
+    }
+  });
+}
+
+async function researchQuestion() {
+  console.log('=== Research Question ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What are the pros and cons of using Cloudflare Workers for serverless applications?',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Research findings:', response.output_text);
+}
+
+async function conversationalWebSearch() {
+  console.log('=== Conversational Web Search ===\n');
+
+  // Create conversation
+  const conv = await openai.conversations.create();
+
+  // First question
+  const response1 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'What is the current price of Bitcoin?',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Q1:', response1.output_text);
+
+  // Follow-up question (model remembers previous answer)
+  const response2 = await openai.responses.create({
+    model: 'gpt-5',
+    conversation: conv.id,
+    input: 'How has it changed in the last 24 hours?',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Q2:', response2.output_text);
+}
+
+async function comparisonResearch() {
+  console.log('=== Comparison Research ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Compare the features and pricing of OpenAI GPT-5 vs Anthropic Claude 3.5 Sonnet',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Comparison:', response.output_text);
+}
+
+async function localInformation() {
+  console.log('=== Local Information ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What are the best restaurants in San Francisco for Italian food?',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Recommendations:', response.output_text);
+}
+
+async function productReviews() {
+  console.log('=== Product Reviews ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What are people saying about the iPhone 16 Pro? Find recent reviews.',
+    tools: [{ type: 'web_search' }],
+  });
+
+  console.log('Review summary:', response.output_text);
+}
+
+async function combinedTools() {
+  console.log('=== Combined Tools (Web Search + Code Interpreter) ===\n');
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'Find the current Bitcoin price and calculate what $1000 would be worth',
+    tools: [
+      { type: 'web_search' },
+      { type: 'code_interpreter' },
+    ],
+  });
+
+  console.log('Answer:', response.output_text);
+
+  // Model uses web search to get price, then code interpreter to calculate
+}
+
+async function webSearchWithFileSearch() {
+  console.log('=== Web Search + File Search ===\n');
+
+  // Upload internal document
+  const file = await openai.files.create({
+    file: Buffer.from('Internal policy: Always check external sources for pricing info'),
+    purpose: 'assistants',
+  });
+
+  const response = await openai.responses.create({
+    model: 'gpt-5',
+    input: 'What is our policy on competitor pricing research?',
+    tools: [
+      { type: 'file_search', file_ids: [file.id] },
+      { type: 'web_search' },
+    ],
+  });
+
+  console.log('Answer:', response.output_text);
+  // Model checks internal policy, then searches web if needed
+}
+
+// Run examples
+basicWebSearch();
+// currentEvents();
+// factChecking();
+// researchQuestion();
+// conversationalWebSearch();
+// comparisonResearch();