diff --git a/developer-guides/llm-sdks-and-frameworks/anthropic.mdx b/developer-guides/llm-sdks-and-frameworks/anthropic.mdx
new file mode 100644
index 0000000..7ff259f
--- /dev/null
+++ b/developer-guides/llm-sdks-and-frameworks/anthropic.mdx
@@ -0,0 +1,162 @@
+---
+title: 'Anthropic'
+description: 'Use ScrapeGraphAI with Claude for web scraping + AI workflows'
+---
+
+> Integrate ScrapeGraphAI with Claude to build AI applications powered by web data.
+
+## Setup
+
+```bash
+npm install scrapegraph-js @anthropic-ai/sdk zod zod-to-json-schema
+```
+
+Create `.env` file:
+
+```bash
+SGAI_APIKEY=your_scrapegraph_key
+ANTHROPIC_API_KEY=your_anthropic_key
+```
+
+<Note>
+If using Node < 20, install `dotenv` and add `import 'dotenv/config'` to your code.
+</Note>
+
+## Scrape + Summarize
+
+This example demonstrates a simple workflow: scrape a website and summarize the content using Claude.
+
+```typescript
+import { smartScraper } from 'scrapegraph-js';
+import Anthropic from '@anthropic-ai/sdk';
+
+const apiKey = process.env.SGAI_APIKEY;
+const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
+
+const scrapeResult = await smartScraper(
+    apiKey,
+    'https://scrapegraphai.com',
+    'Extract all content from this page'
+);
+
+console.log('Scraped content length:', JSON.stringify(scrapeResult.result).length);
+
+const message = await anthropic.messages.create({
+    model: 'claude-haiku-4-5',
+    max_tokens: 1024,
+    messages: [
+        { role: 'user', content: `Summarize in 100 words: ${JSON.stringify(scrapeResult.result)}` }
+    ]
+});
+
+console.log('Response:', message);
+```
+
+## Tool Use
+
+This example shows how to use Claude's tool use feature to let the model decide when to scrape websites based on user requests.
+
+```typescript
+import { smartScraper } from 'scrapegraph-js';
+import { Anthropic } from '@anthropic-ai/sdk';
+import { z } from 'zod';
+import { zodToJsonSchema } from 'zod-to-json-schema';
+
+const apiKey = process.env.SGAI_APIKEY;
+const anthropic = new Anthropic({
+    apiKey: process.env.ANTHROPIC_API_KEY
+});
+
+const ScrapeArgsSchema = z.object({
+    url: z.string()
+});
+
+console.log("Sending user message to Claude and requesting tool use if necessary...");
+const response = await anthropic.messages.create({
+    model: 'claude-haiku-4-5',
+    max_tokens: 1024,
+    tools: [{
+        name: 'scrape_website',
+        description: 'Scrape and extract structured data from a website URL',
+        input_schema: zodToJsonSchema(ScrapeArgsSchema, 'ScrapeArgsSchema') as any
+    }],
+    messages: [{
+        role: 'user',
+        content: 'What is ScrapeGraphAI? Check scrapegraphai.com'
+    }]
+});
+
+const toolUse = response.content.find(block => block.type === 'tool_use');
+
+if (toolUse && toolUse.type === 'tool_use') {
+    const input = toolUse.input as { url: string };
+    console.log(`Calling tool: ${toolUse.name} | URL: ${input.url}`);
+
+    const result = await smartScraper(
+        apiKey,
+        input.url,
+        'Extract all content from this page'
+    );
+
+    console.log(`Scraped content preview: ${JSON.stringify(result.result)?.substring(0, 300)}...`);
+    // Continue with the conversation or process the scraped content as needed
+}
+```
+
+## Structured Extraction
+
+This example demonstrates how to use Claude to extract structured data from scraped website content.
+
+```typescript
+import { smartScraper } from 'scrapegraph-js';
+import Anthropic from '@anthropic-ai/sdk';
+import { z } from 'zod';
+
+const apiKey = process.env.SGAI_APIKEY;
+const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
+
+const CompanyInfoSchema = z.object({
+    name: z.string(),
+    industry: z.string().optional(),
+    description: z.string().optional()
+});
+
+const scrapeResult = await smartScraper(
+    apiKey,
+    'https://stripe.com',
+    'Extract all content from this page'
+);
+
+const prompt = `Extract company information from this website content.
+
+Output ONLY valid JSON in this exact format (no markdown, no explanation):
+
+{
+  "name": "Company Name",
+  "industry": "Industry",
+  "description": "One sentence description"
+}
+
+Website content:
+${JSON.stringify(scrapeResult.result)}`;
+
+const message = await anthropic.messages.create({
+    model: 'claude-haiku-4-5',
+    max_tokens: 1024,
+    messages: [
+        { role: 'user', content: prompt },
+        { role: 'assistant', content: '{' }
+    ]
+});
+
+const textBlock = message.content.find(block => block.type === 'text');
+
+if (textBlock && textBlock.type === 'text') {
+    const jsonText = '{' + textBlock.text;
+    const companyInfo = CompanyInfoSchema.parse(JSON.parse(jsonText));
+  
+    console.log(companyInfo);
+}
+```
+
+For more examples, check the [Claude documentation](https://docs.anthropic.com/claude/docs).
diff --git a/developer-guides/llm-sdks-and-frameworks/gemini.mdx b/developer-guides/llm-sdks-and-frameworks/gemini.mdx
new file mode 100644
index 0000000..55a2311
--- /dev/null
+++ b/developer-guides/llm-sdks-and-frameworks/gemini.mdx
@@ -0,0 +1,131 @@
+---
+title: 'Gemini'
+description: 'Use ScrapeGraphAI with Google Gemini AI for web scraping + AI workflows'
+---
+
+> Integrate ScrapeGraphAI with Google's Gemini for AI applications powered by web data.
+
+## Setup
+
+```bash
+npm install scrapegraph-js @google/genai
+```
+
+Create `.env` file:
+
+```bash
+SGAI_APIKEY=your_scrapegraph_key
+GEMINI_API_KEY=your_gemini_key
+```
+
+<Note>
+If using Node < 20, install `dotenv` and add `import 'dotenv/config'` to your code.
+</Note>
+
+## Scrape + Summarize
+
+This example demonstrates a simple workflow: scrape a website and summarize the content using Gemini.
+
+```typescript
+import { smartScraper } from 'scrapegraph-js';
+import { GoogleGenAI } from '@google/genai';
+
+const apiKey = process.env.SGAI_APIKEY;
+const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
+
+const scrapeResult = await smartScraper(
+    apiKey,
+    'https://scrapegraphai.com',
+    'Extract all content from this page'
+);
+
+console.log('Scraped content length:', JSON.stringify(scrapeResult.result).length);
+
+const response = await ai.models.generateContent({
+    model: 'gemini-2.5-flash',
+    contents: `Summarize: ${JSON.stringify(scrapeResult.result)}`,
+});
+
+console.log('Summary:', response.text);
+```
+
+## Content Analysis
+
+This example shows how to analyze website content using Gemini's multi-turn conversation capabilities.
+
+```typescript
+import { smartScraper } from 'scrapegraph-js';
+import { GoogleGenAI } from '@google/genai';
+
+const apiKey = process.env.SGAI_APIKEY;
+const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
+
+const scrapeResult = await smartScraper(
+    apiKey,
+    'https://news.ycombinator.com/',
+    'Extract all content from this page'
+);
+
+console.log('Scraped content length:', JSON.stringify(scrapeResult.result).length);
+
+const chat = ai.chats.create({
+    model: 'gemini-2.5-flash'
+});
+
+// Ask for the top 3 stories on Hacker News
+const result1 = await chat.sendMessage({
+    message: `Based on this website content from Hacker News, what are the top 3 stories right now?\n\n${JSON.stringify(scrapeResult.result)}`
+});
+console.log('Top 3 Stories:', result1.text);
+
+// Ask for the 4th and 5th stories on Hacker News
+const result2 = await chat.sendMessage({
+    message: `Now, what are the 4th and 5th top stories on Hacker News from the same content?`
+});
+console.log('4th and 5th Stories:', result2.text);
+```
+
+## Structured Extraction
+
+This example demonstrates how to extract structured data using Gemini's JSON mode from scraped website content.
+
+```typescript
+import { smartScraper } from 'scrapegraph-js';
+import { GoogleGenAI, Type } from '@google/genai';
+
+const apiKey = process.env.SGAI_APIKEY;
+const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
+
+const scrapeResult = await smartScraper(
+    apiKey,
+    'https://stripe.com',
+    'Extract all content from this page'
+);
+
+console.log('Scraped content length:', JSON.stringify(scrapeResult.result).length);
+
+const response = await ai.models.generateContent({
+    model: 'gemini-2.5-flash',
+    contents: `Extract company information: ${JSON.stringify(scrapeResult.result)}`,
+    config: {
+        responseMimeType: 'application/json',
+        responseSchema: {
+            type: Type.OBJECT,
+            properties: {
+                name: { type: Type.STRING },
+                industry: { type: Type.STRING },
+                description: { type: Type.STRING },
+                products: {
+                    type: Type.ARRAY,
+                    items: { type: Type.STRING }
+                }
+            },
+            propertyOrdering: ['name', 'industry', 'description', 'products']
+        }
+    }
+});
+
+console.log('Extracted company info:', response?.text);
+```
+
+For more examples, check the [Gemini documentation](https://ai.google.dev/docs).
diff --git a/docs.json b/docs.json
index bf42925..ff87599 100644
--- a/docs.json
+++ b/docs.json
@@ -99,6 +99,13 @@
               "integrations/x402"
             ]
           },
+          {
+            "group": "LLM SDKs & Frameworks",
+            "pages": [
+              "developer-guides/llm-sdks-and-frameworks/gemini",
+              "developer-guides/llm-sdks-and-frameworks/anthropic"
+            ]
+          },
           {
             "group": "Contribute",
             "pages": [