diff --git a/examples/olostep-craigslist-analysis/.env.example b/examples/olostep-craigslist-analysis/.env.example
new file mode 100644
index 00000000..1b0abd46
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/.env.example
@@ -0,0 +1,10 @@
+# .env.example
+
+# TODO: Get your E2B API key from https://e2b.dev/docs
+E2B_API_KEY=""
+
+# TODO: Get your Olostep API key from https://olostep.com
+OLOSTEP_API_KEY=""
+
+# TODO: Get your Google AI Studio API key from https://aistudio.google.com
+GEMINI_API_KEY=""
\ No newline at end of file
diff --git a/examples/olostep-craigslist-analysis/README.md b/examples/olostep-craigslist-analysis/README.md
new file mode 100644
index 00000000..7ddb9238
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/README.md
@@ -0,0 +1,237 @@
+# 🏠 Craigslist Housing Analysis with Olostep + Gemini + E2B
+
+This example demonstrates how to scrape and analyze Craigslist housing data using the **Olostep API** for web scraping, **Google Gemini 2.0 Flash** for AI-powered data extraction, and **E2B Code Interpreter** for data analysis and visualization.
+
+## 🎯 What it does
+
+1. **🕷️ Web Scraping**: Uses Olostep API to fetch raw HTML content from Craigslist housing pages
+2. **🤖 AI Extraction**: Feeds HTML to Gemini AI to extract structured JSON data from JSON-LD schemas
+3. **🧹 Data Cleaning**: Processes and filters extracted data, removing null fields and duplicates
+4. **📊 Analysis**: Uses E2B's Python sandbox to analyze housing data with pandas, matplotlib, and seaborn
+5. **📈 Visualization**: Generates publication-ready charts and insights about SF Bay Area housing market
+
+## 🚀 Features
+
+- **Real-time scraping** of Craigslist housing data across multiple Bay Area regions
+- **AI-powered extraction** of property details from JSON-LD structured data
+- **Smart data processing** that only shows fields with actual values (no null clutter)
+- **Rich visualizations** including property type distribution, location analysis, and market insights
+- **Robust error handling** for API failures and data validation
+- **Clean output** with structured JSON data and multiple visualization charts
+
+## 📋 Prerequisites
+
+- Node.js 18+ installed
+- TypeScript support
+- API Keys for:
+  - **Olostep API** (get from [Olostep](https://olostep.com))
+  - **Google Gemini API** (get from [Google AI Studio](https://makersuite.google.com/app/apikey))
+  - **E2B API** (get from [E2B](https://e2b.dev))
+
+## 🔧 Setup
+
+1. **Install dependencies:**
+   ```bash
+   npm install
+   ```
+
+2. **Set up environment variables:**
+   Create a `.env` file in the project root:
+   ```bash
+   OLOSTEP_API_KEY=your_olostep_api_key_here
+   GEMINI_API_KEY=your_gemini_api_key_here
+   E2B_API_KEY=your_e2b_api_key_here
+   ```
+
+3. **Run the analysis:**
+   ```bash
+   npm run start
+   ```
+
+## 📊 Generated Outputs
+
+The analysis creates several files:
+
+### 📄 Data Files
+- `craigslist_listings.json` - Structured property data extracted from Craigslist (generated after running)
+- `sample_craigslist_listings.json` - Sample output data to show expected format
+
+### 📈 Visualization Charts
+When you run the analysis, it generates multiple PNG files with visualizations:
+- Property type distribution (pie chart)
+- Location frequency analysis (bar chart) 
+- Price distribution histogram (when price data is available)
+- Bedroom/bathroom distribution analysis
+- Property features breakdown
+- Market insights and trends
+
+## 🔍 How It Works
+
+### 1. Web Scraping with Olostep
+```typescript
+// Scrapes multiple Craigslist regions
+const searchUrls = [
+  'https://sfbay.craigslist.org/search/rea#search=2~gallery~40', // Real estate
+  'https://sfbay.craigslist.org/search/eby/apa#search=2~gallery~56', // Apartments
+]
+
+const response = await fetch('https://api.olostep.com/v1/scrapes', {
+  method: 'POST',
+  headers: {
+    'Authorization': `Bearer ${OLOSTEP_API_KEY}`,
+    'Content-Type': 'application/json'
+  },
+  body: JSON.stringify({
+    formats: ["markdown", "html"],
+    wait_before_scraping: 3000,
+    url_to_scrape: searchUrl
+  })
+})
+```
+
+### 2. AI-Powered Data Extraction with Gemini
+```typescript
+// Extracts structured data from HTML using Gemini AI
+const extractionPrompt = `
+Parse this Craigslist page and extract real estate listings from JSON-LD structured data.
+LOOK FOR: <script type="application/ld+json" id="ld_searchpage_results">
+Extract: name, price, location, bedrooms, bathrooms, square_feet, property_type
+`
+
+const response = await fetch(
+  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent`,
+  {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+      'X-goog-api-key': GEMINI_API_KEY,
+    },
+    body: JSON.stringify({
+      contents: [{ parts: [{ text: extractionPrompt }] }]
+    })
+  }
+)
+```
+
+### 3. Data Analysis with E2B
+```typescript
+// Creates Python sandbox for data analysis
+const codeInterpreter = await Sandbox.create()
+
+// Analyzes data and generates visualizations
+const analysisCode = `
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load and analyze housing data
+df = pd.DataFrame(listings)
+# Generate charts and insights...
+`
+
+const result = await codeInterpreter.runCode(analysisCode)
+```
+
+## 📈 Sample Analysis Results
+
+The analysis provides insights such as:
+
+- **Property Distribution**: 67.3% Apartments, 22.4% Houses, 10.2% Places
+- **Top Locations**: Hayward (8 listings), Oakland (4 listings), San Lorenzo (2 listings)
+- **Bedroom Analysis**: Average 1.8 bedrooms, range 1-3 bedrooms
+- **Bathroom Analysis**: Average 1.5 bathrooms, range 1-3 bathrooms
+- **Market Insights**: Price trends, location preferences, property features
+
+## 🔧 Configuration
+
+### Scraping Targets
+Modify `searchUrls` in `scraping.ts` to target different Craigslist regions or categories:
+
+```typescript
+const searchUrls = [
+  'https://sfbay.craigslist.org/search/rea',     // Real estate for sale
+  'https://sfbay.craigslist.org/search/apa',     // Apartments for rent
+  'https://losangeles.craigslist.org/search/rea', // LA real estate
+]
+```
+
+### Analysis Parameters
+Adjust analysis in `index.ts` by modifying the user message:
+
+```typescript
+const userMessage = `
+Analyze this housing data and create:
+1. Custom analysis type
+2. Specific visualizations
+3. Market insights
+`
+```
+
+## 🛠️ File Structure
+
+```
+olostep-craigslist-analysis/
+├── index.ts              # Main orchestration file
+├── scraping.ts           # Olostep scraping logic
+├── model.ts              # Gemini AI configuration
+├── codeInterpreter.ts    # E2B sandbox setup
+├── package.json          # Dependencies and scripts
+├── tsconfig.json         # TypeScript configuration
+├── README.md             # This file
+├── .env                  # Environment variables (create this)
+└── Generated outputs:
+    ├── craigslist_listings.json     # Structured data
+    └── craigslist_analysis_*.png    # Visualization charts
+```
+
+## 🚨 Error Handling
+
+The application includes robust error handling for:
+
+- **API Rate Limits**: Automatic delays between requests
+- **Data Validation**: Filters invalid or incomplete listings
+- **JSON Parsing**: Handles truncated responses from Gemini
+- **Network Issues**: Graceful fallbacks for failed requests
+- **Price Extraction**: Smart parsing of various price formats
+
+## 🎯 Use Cases
+
+This example is perfect for:
+
+- **Real Estate Analysis**: Market research and price trends
+- **Investment Research**: Property investment opportunities
+- **Market Intelligence**: Housing market insights
+- **Data Pipeline Demos**: Showcasing web scraping + AI + analysis workflows
+- **Academic Research**: Housing market studies and urban planning
+
+## 🔄 Development
+
+### Available Scripts
+
+```bash
+npm run start    # Run the full analysis pipeline
+npm run dev      # Run with file watching for development
+npm run build    # Compile TypeScript
+npm run clean    # Remove generated files
+```
+
+### Debugging
+
+Enable detailed logging by modifying the console output in each file. The application provides comprehensive logging for:
+
+- Scraping progress and results
+- AI extraction success/failure
+- Data cleaning statistics
+- Analysis execution status
+
+## 📝 License
+
+MIT License - feel free to use this example as a starting point for your own projects!
+
+## 🤝 Contributing
+
+This is part of the E2B Cookbook examples. Contributions and improvements are welcome!
+
+---
+
+**Built with ❤️ using Olostep + Gemini + E2B**
\ No newline at end of file
diff --git a/examples/olostep-craigslist-analysis/codeInterpreter.ts b/examples/olostep-craigslist-analysis/codeInterpreter.ts
new file mode 100644
index 00000000..60672754
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/codeInterpreter.ts
@@ -0,0 +1,26 @@
+import { Sandbox } from '@e2b/code-interpreter'
+
+export async function codeInterpret(
+  codeInterpreter: Sandbox,
+  code: string
+) {
+  console.log(
+    `\n${'='.repeat(50)}\n> Running AI-generated Python code:\n${code}\n${'='.repeat(50)}`
+  )
+  
+  const exec = await codeInterpreter.runCode(code, {
+    // You can stream logs from the code interpreter
+    // onStderr: (stderr: string) => console.log("\n[Code Interpreter stderr]", stderr),
+    // onStdout: (stdout: string) => console.log("\n[Code Interpreter stdout]", stdout),
+    
+    // You can also stream additional results like charts, images, etc.
+    // onResult: ...
+  })
+
+  if (exec.error) {
+    console.log('❌ [Code Interpreter error]', exec.error) // Runtime error
+    return undefined
+  }
+
+  return exec
+}
\ No newline at end of file
diff --git a/examples/olostep-craigslist-analysis/index.ts b/examples/olostep-craigslist-analysis/index.ts
new file mode 100644
index 00000000..d766be49
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/index.ts
@@ -0,0 +1,226 @@
+import * as fs from 'fs'
+import 'dotenv/config'
+import { Sandbox, Execution } from '@e2b/code-interpreter'
+import { Buffer } from 'buffer'
+import { MODEL_NAME, SYSTEM_PROMPT, tools } from './model'
+import { codeInterpret } from './codeInterpreter'
+import { scrapeCraigslist } from './scraping'
+
+interface GeminiResponse {
+  candidates: Array<{
+    content: {
+      parts: Array<{
+        text?: string
+        functionCall?: {
+          name: string
+          args: Record<string, any>
+        }
+      }>
+    }
+    finishReason: string
+  }>
+}
+
+async function chatWithGemini(
+  codeInterpreter: Sandbox,
+  userMessage: string
+): Promise<Execution | undefined> {
+  const GEMINI_API_KEY = process.env.GEMINI_API_KEY
+  if (!GEMINI_API_KEY) {
+    throw new Error('GEMINI_API_KEY is required')
+  }
+
+  console.log('🤖 Waiting for Gemini...')
+
+  const requestBody = {
+    system_instruction: {
+      parts: [{ text: SYSTEM_PROMPT }]
+    },
+    contents: [
+      {
+        role: 'user',
+        parts: [
+          {
+            text: userMessage
+          }
+        ]
+      }
+    ],
+    tools: [
+      {
+        functionDeclarations: tools.map(tool => ({
+          name: tool.name,
+          description: tool.description,
+          parameters: tool.input_schema
+        }))
+      }
+    ],
+    generationConfig: {
+      temperature: 0.1,
+      maxOutputTokens: 4096,
+    },
+    toolConfig: {
+      functionCallingConfig: {
+        mode: "ANY",
+        allowedFunctionNames: ["execute_python"]
+      }
+    }
+  }
+
+  try {
+    const response = await fetch(
+      `https://generativelanguage.googleapis.com/v1beta/models/${MODEL_NAME}:generateContent`,
+      {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'X-goog-api-key': GEMINI_API_KEY,
+        },
+        body: JSON.stringify(requestBody),
+      }
+    )
+
+    if (!response.ok) {
+      throw new Error(`Gemini API error: ${response.status} ${response.statusText}`)
+    }
+
+    const data: GeminiResponse = await response.json()
+    
+    console.log(`\n${'='.repeat(50)}\nGemini response:\n${'='.repeat(50)}`)
+    
+    if (data.candidates && data.candidates.length > 0) {
+      const candidate = data.candidates[0]
+      const parts = candidate.content?.parts || []
+
+      // Look for function calls
+      const functionCall = parts.find(part => part.functionCall)
+      if (functionCall?.functionCall) {
+        const toolName = functionCall.functionCall.name
+        const toolArgs = functionCall.functionCall.args
+
+        console.log(`\n${'='.repeat(50)}\nUsing tool: ${toolName}\n${'='.repeat(50)}`)
+
+        if (toolName === 'execute_python') {
+          const code = toolArgs.code
+          return await codeInterpret(codeInterpreter, code)
+        }
+      }
+
+      // If no function call, just print the text response
+      const textParts = parts.filter(part => part.text)
+      if (textParts.length > 0) {
+        textParts.forEach(part => console.log(part.text))
+      }
+    }
+
+    return undefined
+  } catch (error) {
+    console.error('❌ Error calling Gemini:', error.message)
+    throw error
+  }
+}
+
+async function run() {
+  console.log('🏠 Craigslist Housing Analysis with Olostep + Gemini\n')
+
+  // Load or scrape Craigslist data
+  let data: string
+  
+  const readDataFromFile = (): string | null => {
+    try {
+      return fs.readFileSync('craigslist_listings.json', 'utf8')
+    } catch (err: any) {
+      if (err.code === 'ENOENT') {
+        console.log('📄 File not found, scraping fresh data...')
+        return null
+      } else {
+        throw err
+      }
+    }
+  }
+
+  const fetchData = async () => {
+    data = readDataFromFile()
+    if (!data || data.trim() === '[]') {
+      console.log('📄 File is empty or contains no data, scraping...')
+      data = await scrapeCraigslist()
+    }
+  }
+
+  await fetchData()
+
+  // Parse and validate the data
+  const listings = JSON.parse(data)
+  console.log(`\n📊 Loaded ${listings.length} Craigslist listings`)
+
+    // Create the analysis prompt
+    const userMessage = `
+Analyze this Craigslist housing data from the San Francisco Bay Area and create insightful visualizations. 
+
+IMPORTANT: Only analyze listings that have price data. Filter out listings without prices before creating price-related charts.
+
+Key analyses to perform:
+1. Property type distribution (pie chart)
+2. Location frequency analysis (bar chart of top 10 locations)
+3. For listings WITH prices: Price distribution histogram
+4. Bedroom/bathroom distribution analysis
+5. Property features summary
+
+Create clean, publication-ready visualizations with proper titles, labels, and colors. Save charts as PNG files.
+
+Listing data: ${JSON.stringify(listings)}
+`
+
+  // Create code interpreter and run analysis
+  const codeInterpreter = await Sandbox.create()
+  
+  try {
+    const codeOutput = await chatWithGemini(codeInterpreter, userMessage)
+    
+    if (!codeOutput) {
+      console.log('❌ No code output generated')
+      return
+    }
+
+    // Log any console output
+    if (codeOutput.logs) {
+      console.log('\n📋 Code execution logs:')
+      console.log(codeOutput.logs)
+    }
+
+    // Handle results
+    if (codeOutput.results.length === 0) {
+      console.log('❌ No visualization results')
+      return
+    }
+
+    // Save any generated charts
+    codeOutput.results.forEach((result, index) => {
+      if (result.text) {
+        console.log(`\n📊 Analysis result ${index + 1}:`)
+        console.log(result.text)
+      }
+
+      if (result.png) {
+        const pngData = Buffer.from(result.png, 'base64')
+        const filename = `craigslist_analysis_${index + 1}.png`
+        fs.writeFileSync(filename, pngData)
+        console.log(`✅ Saved chart to ${filename}`)
+      }
+    })
+
+  } catch (error) {
+    console.error('❌ Analysis error:', error.message)
+  } finally {
+    await codeInterpreter.kill()
+    console.log('\n🏁 Analysis complete!')
+  }
+}
+
+// Handle uncaught errors gracefully
+process.on('unhandledRejection', (error) => {
+  console.error('❌ Unhandled error:', error)
+  process.exit(1)
+})
+
+run().catch(console.error)
\ No newline at end of file
diff --git a/examples/olostep-craigslist-analysis/model.ts b/examples/olostep-craigslist-analysis/model.ts
new file mode 100644
index 00000000..0640d65a
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/model.ts
@@ -0,0 +1,50 @@
+export const MODEL_NAME = 'gemini-2.0-flash'
+
+export const SYSTEM_PROMPT = `
+## Your Job & Context
+You are a Python data scientist specializing in real estate and housing market analysis. You are given tasks to complete and you run Python code to solve them.
+
+- The Python code runs in a Jupyter notebook environment
+- Every time you call \`execute_python\` tool, the Python code is executed in a separate cell
+- You can make multiple calls to \`execute_python\` as needed
+- Display visualizations using matplotlib, seaborn, plotly, or any other visualization library directly in the notebook
+- Don't worry about saving visualizations to files - display them inline
+- You have access to the internet and can make API requests
+- You have access to the filesystem and can read/write files
+- You can install any pip package if needed, but common data analysis packages are already available
+- Focus on housing market insights, price analysis, and geographic trends
+- All code runs in a secure sandbox environment
+
+## Preferred Analysis Approaches
+- Use descriptive statistics and visualizations to understand the data
+- Look for patterns in pricing, location, and housing features
+- Create clear, publication-ready charts with proper labels and titles
+- Provide actionable insights from the data analysis
+`
+
+export interface GeminiTool {
+  name: string
+  description: string
+  input_schema: {
+    type: string
+    properties: Record<string, any>
+    required: string[]
+  }
+}
+
+export const tools: GeminiTool[] = [
+  {
+    name: 'execute_python',
+    description: 'Execute Python code in a Jupyter notebook cell and return any result, stdout, stderr, display_data, and error.',
+    input_schema: {
+      type: 'object',
+      properties: {
+        code: {
+          type: 'string',
+          description: 'The Python code to execute in a single cell.',
+        },
+      },
+      required: ['code'],
+    },
+  },
+]
\ No newline at end of file
diff --git a/examples/olostep-craigslist-analysis/package-lock.json b/examples/olostep-craigslist-analysis/package-lock.json
new file mode 100644
index 00000000..4f452099
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/package-lock.json
@@ -0,0 +1,756 @@
+{
+    "name": "craigslist-olostep-analysis",
+    "version": "1.0.0",
+    "lockfileVersion": 3,
+    "requires": true,
+    "packages": {
+        "": {
+            "name": "craigslist-olostep-analysis",
+            "version": "1.0.0",
+            "license": "MIT",
+            "dependencies": {
+                "@e2b/code-interpreter": "^1.0.1",
+                "buffer": "^6.0.3",
+                "dotenv": "^16.3.1"
+            },
+            "devDependencies": {
+                "@types/node": "^20.8.0",
+                "tsx": "^4.0.0",
+                "typescript": "^5.2.2"
+            },
+            "engines": {
+                "node": ">=18.0.0"
+            }
+        },
+        "node_modules/@bufbuild/protobuf": {
+            "version": "2.9.0",
+            "resolved": "https://registry.npmjs.org/@bufbuild/protobuf/-/protobuf-2.9.0.tgz",
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+            "license": "(Apache-2.0 AND BSD-3-Clause)"
+        },
+        "node_modules/@connectrpc/connect": {
+            "version": "2.0.0-rc.3",
+            "resolved": "https://registry.npmjs.org/@connectrpc/connect/-/connect-2.0.0-rc.3.tgz",
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+            "license": "Apache-2.0",
+            "peerDependencies": {
+                "@bufbuild/protobuf": "^2.2.0"
+            }
+        },
+        "node_modules/@connectrpc/connect-web": {
+            "version": "2.0.0-rc.3",
+            "resolved": "https://registry.npmjs.org/@connectrpc/connect-web/-/connect-web-2.0.0-rc.3.tgz",
+            "integrity": "sha512-w88P8Lsn5CCsA7MFRl2e6oLY4J/5toiNtJns/YJrlyQaWOy3RO8pDgkz+iIkG98RPMhj2thuBvsd3Cn4DKKCkw==",
+            "license": "Apache-2.0",
+            "peerDependencies": {
+                "@bufbuild/protobuf": "^2.2.0",
+                "@connectrpc/connect": "2.0.0-rc.3"
+            }
+        },
+        "node_modules/@e2b/code-interpreter": {
+            "version": "1.5.1",
+            "resolved": "https://registry.npmjs.org/@e2b/code-interpreter/-/code-interpreter-1.5.1.tgz",
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+            "license": "MIT",
+            "dependencies": {
+                "e2b": "^1.4.0"
+            },
+            "engines": {
+                "node": ">=18"
+            }
+        },
+        "node_modules/@esbuild/aix-ppc64": {
+            "version": "0.25.10",
+            "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.25.10.tgz",
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+            "cpu": [
+                "ppc64"
+            ],
+            "dev": true,
+            "license": "MIT",
+            "optional": true,
+            "os": [
+                "aix"
+            ],
+            "engines": {
+                "node": ">=18"
+            }
+        },
+        "node_modules/@esbuild/android-arm": {
+            "version": "0.25.10",
+            "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.25.10.tgz",
+            "integrity": "sha512-dQAxF1dW1C3zpeCDc5KqIYuZ1tgAdRXNoZP7vkBIRtKZPYe2xVr/d3SkirklCHudW1B45tGiUlz2pUWDfbDD4w==",
+            "cpu": [
+                "arm"
+            ],
+            "dev": true,
+            "license": "MIT",
+            "optional": true,
+            "os": [
+                "android"
+            ],
+            "engines": {
+                "node": ">=18"
+            }
+        },
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+                "@esbuild/win32-ia32": "0.25.10",
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+            }
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+            "dev": true,
+            "hasInstallScript": true,
+            "license": "MIT",
+            "optional": true,
+            "os": [
+                "darwin"
+            ],
+            "engines": {
+                "node": "^8.16.0 || ^10.6.0 || >=11.0.0"
+            }
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+        "node_modules/get-tsconfig": {
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+            },
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+                "url": "https://github.com/privatenumber/get-tsconfig?sponsor=1"
+            }
+        },
+        "node_modules/ieee754": {
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+            "funding": [
+                {
+                    "type": "github",
+                    "url": "https://github.com/sponsors/feross"
+                },
+                {
+                    "type": "patreon",
+                    "url": "https://www.patreon.com/feross"
+                },
+                {
+                    "type": "consulting",
+                    "url": "https://feross.org/support"
+                }
+            ],
+            "license": "BSD-3-Clause"
+        },
+        "node_modules/openapi-fetch": {
+            "version": "0.9.8",
+            "resolved": "https://registry.npmjs.org/openapi-fetch/-/openapi-fetch-0.9.8.tgz",
+            "integrity": "sha512-zM6elH0EZStD/gSiNlcPrzXcVQ/pZo3BDvC6CDwRDUt1dDzxlshpmQnpD6cZaJ39THaSmwVCxxRrPKNM1hHrDg==",
+            "license": "MIT",
+            "dependencies": {
+                "openapi-typescript-helpers": "^0.0.8"
+            }
+        },
+        "node_modules/openapi-typescript-helpers": {
+            "version": "0.0.8",
+            "resolved": "https://registry.npmjs.org/openapi-typescript-helpers/-/openapi-typescript-helpers-0.0.8.tgz",
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+            "license": "MIT"
+        },
+        "node_modules/platform": {
+            "version": "1.3.6",
+            "resolved": "https://registry.npmjs.org/platform/-/platform-1.3.6.tgz",
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+            "license": "MIT"
+        },
+        "node_modules/resolve-pkg-maps": {
+            "version": "1.0.0",
+            "resolved": "https://registry.npmjs.org/resolve-pkg-maps/-/resolve-pkg-maps-1.0.0.tgz",
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+            "dev": true,
+            "license": "MIT",
+            "funding": {
+                "url": "https://github.com/privatenumber/resolve-pkg-maps?sponsor=1"
+            }
+        },
+        "node_modules/tsx": {
+            "version": "4.20.5",
+            "resolved": "https://registry.npmjs.org/tsx/-/tsx-4.20.5.tgz",
+            "integrity": "sha512-+wKjMNU9w/EaQayHXb7WA7ZaHY6hN8WgfvHNQ3t1PnU91/7O8TcTnIhCDYTZwnt8JsO9IBqZ30Ln1r7pPF52Aw==",
+            "dev": true,
+            "license": "MIT",
+            "dependencies": {
+                "esbuild": "~0.25.0",
+                "get-tsconfig": "^4.7.5"
+            },
+            "bin": {
+                "tsx": "dist/cli.mjs"
+            },
+            "engines": {
+                "node": ">=18.0.0"
+            },
+            "optionalDependencies": {
+                "fsevents": "~2.3.3"
+            }
+        },
+        "node_modules/typescript": {
+            "version": "5.9.2",
+            "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.9.2.tgz",
+            "integrity": "sha512-CWBzXQrc/qOkhidw1OzBTQuYRbfyxDXJMVJ1XNwUHGROVmuaeiEm3OslpZ1RV96d7SKKjZKrSJu3+t/xlw3R9A==",
+            "dev": true,
+            "license": "Apache-2.0",
+            "bin": {
+                "tsc": "bin/tsc",
+                "tsserver": "bin/tsserver"
+            },
+            "engines": {
+                "node": ">=14.17"
+            }
+        },
+        "node_modules/undici-types": {
+            "version": "6.21.0",
+            "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
+            "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
+            "dev": true,
+            "license": "MIT"
+        }
+    }
+}
diff --git a/examples/olostep-craigslist-analysis/package.json b/examples/olostep-craigslist-analysis/package.json
new file mode 100644
index 00000000..8cbed2e6
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/package.json
@@ -0,0 +1,37 @@
+{
+    "name": "craigslist-olostep-analysis",
+    "version": "1.0.0",
+    "description": "Scrape and analyze Craigslist housing data using Olostep and E2B Code Interpreter with Gemini AI",
+    "main": "index.ts",
+    "scripts": {
+      "start": "tsx index.ts",
+      "dev": "tsx watch index.ts",
+      "build": "tsc",
+      "clean": "rm -f *.png *.json"
+    },
+    "keywords": [
+      "web-scraping",
+      "olostep",
+      "craigslist",
+      "housing-data",
+      "e2b",
+      "gemini",
+      "data-analysis",
+      "real-estate"
+    ],
+    "author": "Team Olostep",
+    "license": "MIT",
+    "dependencies": {
+      "@e2b/code-interpreter": "^1.0.1",
+      "dotenv": "^16.3.1",
+      "buffer": "^6.0.3"
+    },
+    "devDependencies": {
+      "@types/node": "^20.8.0",
+      "tsx": "^4.0.0",
+      "typescript": "^5.2.2"
+    },
+    "engines": {
+      "node": ">=18.0.0"
+    }
+  }
\ No newline at end of file
diff --git a/examples/olostep-craigslist-analysis/sample_craigslist_listings.json b/examples/olostep-craigslist-analysis/sample_craigslist_listings.json
new file mode 100644
index 00000000..049f2d47
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/sample_craigslist_listings.json
@@ -0,0 +1,72 @@
+[
+  {
+    "title": "ADU - BACK YARD UNIT - $250 sq.ft.",
+    "location": "Hayward",
+    "property_type": "Apartment",
+    "price": 250,
+    "square_feet": 250
+  },
+  {
+    "title": "Location, Location, Location!",
+    "location": "Oakland",
+    "property_type": "Apartment",
+    "bedrooms": 1,
+    "bathrooms": 1
+  },
+  {
+    "title": "Forever Home Townhouse in Serene Neighborhood",
+    "location": "Fremont",
+    "property_type": "House",
+    "bedrooms": 3,
+    "bathrooms": 2
+  },
+  {
+    "title": "Condo 2 bed 2 bath Top Floor Corner View of East Bay Hills Quiet",
+    "location": "San Leandro",
+    "property_type": "Apartment",
+    "bedrooms": 2,
+    "bathrooms": 2
+  },
+  {
+    "title": "Gated community, Located in Hayward, 1 BD",
+    "location": "San Lorenzo",
+    "property_type": "Apartment",
+    "bedrooms": 1,
+    "bathrooms": 1
+  },
+  {
+    "title": "Cadence Apts: Modern Design, Top Amenities, Perfect Bay Location",
+    "location": "Hayward",
+    "property_type": "Apartment",
+    "bedrooms": 2,
+    "bathrooms": 2
+  },
+  {
+    "title": "Renovated 3bedroom 2bath single story home",
+    "location": "Brentwood",
+    "property_type": "House",
+    "bedrooms": 3,
+    "bathrooms": 2
+  },
+  {
+    "title": "Wood-Style Flooring, Large Closets, Workspaces",
+    "location": "Berkeley",
+    "property_type": "Apartment",
+    "bedrooms": 2,
+    "bathrooms": 1
+  },
+  {
+    "title": "3 bedroom 2 bath Walk to Bart Restaurants &Safeway",
+    "location": "El Cerrito",
+    "property_type": "House",
+    "bedrooms": 3,
+    "bathrooms": 2
+  },
+  {
+    "title": "In Dublin, 1/bd 1/ba, Air Conditioning",
+    "location": "Dublin",
+    "property_type": "Apartment",
+    "bedrooms": 1,
+    "bathrooms": 1
+  }
+]
diff --git a/examples/olostep-craigslist-analysis/scraping.ts b/examples/olostep-craigslist-analysis/scraping.ts
new file mode 100644
index 00000000..83888f47
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/scraping.ts
@@ -0,0 +1,338 @@
+import 'dotenv/config'
+
+interface CraigslistListing {
+  title: string
+  price: number
+  location: string
+  bedrooms?: number
+  bathrooms?: number
+  square_feet?: number
+  posting_date?: string
+  url: string
+}
+
+// Helper function to extract listings from HTML using Gemini
+async function extractListingsFromHTML(htmlContent: string, sourceUrl: string): Promise<CraigslistListing[]> {
+  const GEMINI_API_KEY = process.env.GEMINI_API_KEY
+  if (!GEMINI_API_KEY) {
+    throw new Error('GEMINI_API_KEY is required for HTML parsing')
+  }
+
+  const extractionPrompt = `
+Parse this Craigslist page and extract real estate listings from the JSON-LD structured data.
+
+LOOK FOR: <script type="application/ld+json" id="ld_searchpage_results"> containing itemListElement array.
+
+For each item in itemListElement, extract:
+- name/title from item.name
+- price from title (look for $X, $X/month, $X sq.ft patterns) or item.price
+- location from item.address.addressLocality  
+- bedrooms from item.numberOfBedrooms
+- bathrooms from item.numberOfBathroomsTotal  
+- square_feet from title (look for "X sq.ft" or "X sqft") or other fields
+- property_type from item.@type (Apartment, House, Place, etc)
+
+Extract numbers from price strings: "$250 sq.ft." → 250, "$3500/month" → 3500
+
+Return JSON format:
+{
+  "listings": [
+    {
+      "title": "extracted name",
+      "price": extracted_number_or_null,
+      "location": "addressLocality", 
+      "bedrooms": number_or_null,
+      "bathrooms": number_or_null,
+      "square_feet": number_or_null,
+      "posting_date": null,
+      "url": null,
+      "property_type": "@type_value"
+    }
+  ]
+}
+
+Limit to first 25 listings to avoid truncation.
+
+HTML Content:
+${htmlContent.substring(0, 25000)}
+`
+
+  try {
+    const response = await fetch(
+      `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent`,
+      {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'X-goog-api-key': GEMINI_API_KEY,
+        },
+        body: JSON.stringify({
+          contents: [
+            {
+              parts: [{ text: extractionPrompt }]
+            }
+          ],
+          generationConfig: {
+            temperature: 0.1,
+            maxOutputTokens: 4096,
+          }
+        }),
+      }
+    )
+
+    if (!response.ok) {
+      throw new Error(`Gemini API error: ${response.status}`)
+    }
+
+    const data = await response.json()
+    const textResponse = data.candidates?.[0]?.content?.parts?.[0]?.text || ''
+    
+    console.log('🔍 Gemini response preview:', textResponse.substring(0, 300))
+    
+    // Extract JSON from the response - handle both complete and truncated JSON
+    let jsonMatch = textResponse.match(/\{[\s\S]*\}/)
+    
+    if (!jsonMatch) {
+      // Try to find partial JSON if the response was truncated
+      const partialMatch = textResponse.match(/\{[\s\S]*"listings":\s*\[[\s\S]*/)
+      if (partialMatch) {
+        // Try to fix truncated JSON by closing brackets
+        let jsonStr = partialMatch[0]
+        if (!jsonStr.includes('}]')) {
+          // Count open objects and arrays to close them properly
+          const openBraces = (jsonStr.match(/\{/g) || []).length
+          const closeBraces = (jsonStr.match(/\}/g) || []).length
+          const openArrays = (jsonStr.match(/\[/g) || []).length
+          const closeArrays = (jsonStr.match(/\]/g) || []).length
+          
+          // Add missing closing characters
+          for (let i = 0; i < openBraces - closeBraces; i++) {
+            jsonStr += '}'
+          }
+          for (let i = 0; i < openArrays - closeArrays; i++) {
+            jsonStr += ']'
+          }
+        }
+        jsonMatch = [jsonStr]
+      }
+    }
+    
+    if (jsonMatch) {
+      try {
+        const extractedData = JSON.parse(jsonMatch[0])
+        if (extractedData.message) {
+          console.log('ℹ️ Gemini message:', extractedData.message)
+        }
+        console.log(`✅ Successfully parsed ${extractedData.listings?.length || 0} listings`)
+        return extractedData.listings || []
+      } catch (parseError) {
+        console.log('⚠️ Failed to parse JSON:', parseError.message)
+        console.log('Raw JSON attempt:', jsonMatch[0].substring(0, 300))
+        
+        // Try to extract individual listings even if full JSON failed
+        const listingMatches = textResponse.match(/"title":\s*"[^"]*"/g)
+        if (listingMatches && listingMatches.length > 0) {
+          console.log(`📋 Found ${listingMatches.length} partial listings in truncated response`)
+          // Return simple extracted listings
+          return listingMatches.slice(0, 10).map((match, i) => {
+            const title = match.match(/"title":\s*"([^"]*)"/)?.[1] || `Listing ${i + 1}`
+            return {
+              title,
+              price: null,
+              location: 'Unknown',
+              bedrooms: null,
+              bathrooms: null,
+              square_feet: null,
+              posting_date: null,
+              url: `https://sfbay.craigslist.org/listing${i + 1}.html`
+            }
+          })
+        }
+        
+        return []
+      }
+    }
+    
+    console.log('⚠️ No valid JSON found in Gemini response')
+    console.log('Full response:', textResponse.substring(0, 500))
+    return []
+  } catch (error) {
+    console.log('⚠️ Error extracting with Gemini:', error.message)
+    return []
+  }
+}
+
+export async function scrapeCraigslist(): Promise<string> {
+  try {
+    const OLOSTEP_API_KEY = process.env.OLOSTEP_API_KEY
+    if (!OLOSTEP_API_KEY) {
+      throw new Error('OLOSTEP_API_KEY is required')
+    }
+
+    // Scrape multiple Craigslist regions for more apartment data
+    const searchUrls = [
+      'https://sfbay.craigslist.org/search/rea#search=2~gallery~40',  // SF apartments
+      'https://sfbay.craigslist.org/search/eby/apa#search=2~gallery~56',  // East Bay apartments 
+    ]
+    
+    let allListings: CraigslistListing[] = []
+
+    for (const [index, searchUrl] of searchUrls.entries()) {
+      console.log(`🔄 Step 1: Scraping HTML from Craigslist (${index + 1}/${searchUrls.length}): ${searchUrl}`)
+      
+      // Step 1: Get HTML content from Olostep - use simple payload like your example
+      const payload = {
+        formats: ["markdown", "html"],
+        wait_before_scraping: 3000,
+        remove_css_selectors: "none",
+        url_to_scrape: searchUrl
+      }
+
+      try {
+        const response = await fetch('https://api.olostep.com/v1/scrapes', {
+          method: 'POST',
+          headers: {
+            'Authorization': `Bearer ${OLOSTEP_API_KEY}`,
+            'Content-Type': 'application/json'
+          },
+          body: JSON.stringify(payload)
+        })
+
+        if (!response.ok) {
+          console.log(`⚠️ Failed to scrape ${searchUrl}: ${response.status} ${response.statusText}`)
+          continue
+        }
+
+        const data: any = await response.json()
+        console.log(`✅ HTML scraped successfully. Status: ${data.result?.page_metadata?.status_code || 'unknown'}`)
+        
+        // Step 2: Extract HTML content from Olostep response
+        let htmlContent = ''
+        if (data.result?.html_content) {
+          htmlContent = data.result.html_content
+        } else if (data.result?.content) {
+          htmlContent = data.result.content
+        } else if (data.html_content) {
+          htmlContent = data.html_content
+        } else {
+          console.log('⚠️ No HTML content found in response')
+          console.log('Available fields:', Object.keys(data.result || data))
+          continue
+        }
+
+        console.log(`📄 Got HTML content (${htmlContent.length} chars)`)
+        
+        // Step 3: Use Gemini to extract structured data from HTML
+        console.log(`🤖 Step 2: Extracting listings from HTML using Gemini AI...`)
+        const pageListings = await extractListingsFromHTML(htmlContent, searchUrl)
+        console.log(`📋 Extracted ${pageListings.length} listings from this page`)
+        
+        if (pageListings.length > 0) {
+          allListings = [...allListings, ...pageListings]
+        }
+
+        // Add delay between requests to be respectful
+        if (index < searchUrls.length - 1) {
+          console.log('⏱️ Waiting 3 seconds before next request...')
+          await new Promise(resolve => setTimeout(resolve, 3000))
+        }
+
+      } catch (error) {
+        console.log(`❌ Error scraping ${searchUrl}:`, error.message)
+        continue
+      }
+    }
+
+    console.log(`📈 Total raw listings collected: ${allListings.length}`)
+
+    console.log('📋 Sample raw listings:')
+    allListings.slice(0, 3).forEach((listing, i) => {
+      console.log(`${i + 1}. "${listing.title}" - ${listing.location} - $${listing.price}`)
+    })
+
+    // Clean and validate the data - more lenient filtering
+    const cleanedListings = allListings
+      .filter(listing => {
+        // More lenient filtering - just need title and location
+        const hasTitle = listing.title && listing.title.length > 3
+        const hasLocation = listing.location && listing.location.length > 1
+        const validLocation = listing.location !== 'Unknown'
+        
+        if (!hasTitle) console.log(`❌ Filtered out: No title - "${listing.title}"`)
+        if (!hasLocation) console.log(`❌ Filtered out: No location - "${listing.location}"`)
+        if (!validLocation) console.log(`❌ Filtered out: Unknown location - "${listing.location}"`)
+        
+        return hasTitle && hasLocation && validLocation
+      })
+      .map(listing => {
+        // Create clean listing object without null/undefined values
+        const cleanListing: any = {
+          title: listing.title,
+          location: String(listing.location || '').trim(),
+          property_type: listing.property_type
+        }
+        
+        // Only add fields that have actual values
+        if (listing.price && listing.price > 0) {
+          cleanListing.price = Math.round(Number(listing.price))
+        }
+        
+        if (listing.bedrooms && listing.bedrooms > 0) {
+          cleanListing.bedrooms = listing.bedrooms
+        }
+        
+        if (listing.bathrooms && listing.bathrooms > 0) {
+          cleanListing.bathrooms = listing.bathrooms
+        }
+        
+        if (listing.square_feet && listing.square_feet > 0) {
+          cleanListing.square_feet = listing.square_feet
+        }
+        
+        if (listing.posting_date && listing.posting_date.trim() !== '') {
+          cleanListing.posting_date = listing.posting_date
+        }
+        
+        // Add URL if it exists and is not default
+        if (listing.url && !listing.url.includes('/search/apa')) {
+          cleanListing.url = listing.url.startsWith('http') ? listing.url : 
+                            listing.url.startsWith('/') ? `https://sfbay.craigslist.org${listing.url}` : 
+                            listing.url
+        }
+        
+        return cleanListing
+      })
+      .filter(listing => {
+        // Allow listings without prices - many real estate listings don't show prices
+        return !listing.price || (listing.price >= 100 && listing.price <= 50000)
+      })
+      // Remove duplicates based on title and price
+      .filter((listing, index, self) => 
+        index === self.findIndex(l => 
+          l.title.toLowerCase() === listing.title.toLowerCase() && 
+          l.price === listing.price
+        )
+      )
+
+    console.log(`🧹 Final cleaned listings: ${cleanedListings.length}`)
+
+    if (cleanedListings.length === 0) {
+      console.log('⚠️ No valid listings found after cleaning and validation')
+      console.log('   - Check if JSON-LD data exists in HTML')
+      console.log('   - Verify extraction prompt is working correctly')
+      console.log('   - May need to adjust filtering criteria')
+      throw new Error('No valid listings extracted from any page')
+    }
+
+    // Save to file
+    const fs = await import('fs')
+    fs.writeFileSync('craigslist_listings.json', JSON.stringify(cleanedListings, null, 2))
+    
+    console.log(`💾 Saved ${cleanedListings.length} listings to craigslist_listings.json`)
+    return JSON.stringify(cleanedListings)
+    
+  } catch (error) {
+    console.error('❌ Scraping error:', error.message)
+    throw error
+  }
+}
\ No newline at end of file
diff --git a/examples/olostep-craigslist-analysis/tsconfig.json b/examples/olostep-craigslist-analysis/tsconfig.json
new file mode 100644
index 00000000..0f16fd42
--- /dev/null
+++ b/examples/olostep-craigslist-analysis/tsconfig.json
@@ -0,0 +1,16 @@
+{
+  "extends": "../../tsconfig.json",
+  "compilerOptions": {
+    "target": "ES2020",
+    "lib": ["ES2020", "DOM"],
+    "moduleResolution": "node",
+    "allowSyntheticDefaultImports": true,
+    "strict": false,
+    "skipLibCheck": true,
+    "forceConsistentCasingInFileNames": true,
+    "resolveJsonModule": true,
+    "types": ["node"]
+  },
+  "include": ["*.ts"],
+  "exclude": ["node_modules", "*.js"]
+}