diff --git a/cookbook/chat-webpage-simple-rag/scrapegraph_burr_lancedb.ipynb b/cookbook/chat-webpage-simple-rag/scrapegraph_burr_lancedb.ipynb
new file mode 100644
index 0000000..4ca09a7
--- /dev/null
+++ b/cookbook/chat-webpage-simple-rag/scrapegraph_burr_lancedb.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jEkuKbcRrPcK"
+   },
+   "source": [
+    "## \ud83d\udd77\ufe0f Chat with your webpage with `scrapegraph`, `burr` and `lancedb`\n",
+    "\n",
+    "[![Open in Alph](https://www.runalph.ai/badges/open-in-alph.svg)](https://www.runalph.ai/notebooks/scrapegraphai/scrapegraph-burr-lancedb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pbDpLKW9pbSIPrI5DbQQE2q_7eaDUAH9?usp=sharing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dsJlQnfA60D0"
+   },
+   "source": [
+    "![scrapegraph 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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IzsyDXEWwPVt"
+   },
+   "source": [
+    "### \ud83d\udd27 Install `dependencies`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "os_vm0MkIxr9"
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install scrapegraph-py burr[start,opentelemetry] lancedb openai opentelemetry-instrumentation-openai opentelemetry-instrumentation-lancedb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "apBsL-L2KzM7"
+   },
+   "source": [
+    "### \ud83d\udd11 Import `ScrapeGraph` and `OpenAI` API keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ol9gQbAFkh9b"
+   },
+   "source": [
+    "You can find the Scrapegraph API key [here](https://scrapegraphai.com/dashboard)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "sffqFG2EJ8bI",
+    "outputId": "bd2fbb27-980c-420f-ef50-fc1f23bdae0a"
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Scrapegraph API key:\n",
+      " \u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\n",
+      "OpenAI API key:\n",
+      " \u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.environ.get(\"SGAI_API_KEY\"):\n",
+    "    os.environ[\"SGAI_API_KEY\"] = getpass.getpass(\"Scrapegraph API key:\\n\")\n",
+    "\n",
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API key:\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cDGH0b2DkY63"
+   },
+   "source": [
+    "### \ud83d\ude80 Define the extraction and query flow using `burr` and run it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "M3gmZCI0eqS1"
+   },
+   "source": [
+    "`burr` is an open-source `orchestrator framework` that makes it easy to develop applications that make decisions (chatbots, agents, simulations, etc...). It also features a cool self-hosted UI to trace what's happening in the application.\n",
+    "\n",
+    "> Check the [Github Repo](https://github.com/DAGWorks-Inc/burr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "359QH2__gAZo"
+   },
+   "source": [
+    "Our goal is to define a flow (DAG) that:\n",
+    "\n",
+    "1.   Fetches markdown from webpages ([scrapegraph](https://scrapegraphai.com/))\n",
+    "2.   Chunks the content and stores it in a vector store ([lancedb](https://lancedb.com/))\n",
+    "3.   Allows to query the db and generate an answer using a LLM\n",
+    "\n",
+    "We can see all of this as *Nodes* connected together in a *Graph*, where the *Nodes* are the `actions` we want to perform.\n",
+    "\n",
+    "In burr we define actions by simply adding the `action decorator` to each function and specifying what that function needs to `read` and `write` from the graph's state.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2aWKZrJgijJR"
+   },
+   "source": [
+    "All imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "HeYtTm0DigBM"
+   },
+   "outputs": [],
+   "source": [
+    "from burr.core import action, State, ApplicationBuilder\n",
+    "\n",
+    "from scrapegraph_py import ScrapeGraphAI, MarkdownFormatConfig\n",
+    "\n",
+    "import logging\n",
+    "import re\n",
+    "import lancedb\n",
+    "from lancedb.pydantic import LanceModel, Vector\n",
+    "from lancedb.embeddings import get_registry\n",
+    "\n",
+    "import openai\n",
+    "import tiktoken\n",
+    "from typing import List, Optional\n",
+    "\n",
+    "from opentelemetry.instrumentation.lancedb import LanceInstrumentor\n",
+    "from opentelemetry.instrumentation.openai import OpenAIInstrumentor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i1Ei6gDTdzYW"
+   },
+   "source": [
+    "#### \ud83d\udd0d Define `fetch_webpage` action to fetch and convert a webpage into `markdown`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "M1KSXffZopUD"
+   },
+   "source": [
+    "Here we use `scrape` with the markdown format to fetch a webpage and convert it into markdown, which is suitable for LLM ingestion.\n",
+    "\n",
+    "You can find more info in the [official scrapegraph documentation](https://docs.scrapegraphai.com)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JS3DZIAhbjD1",
+    "outputId": "de22fff8-96b1-489f-bbd7-e2eef24c99c5"
+   },
+   "outputs": [],
+   "source": [
+    "logging.basicConfig(level=logging.INFO)\n",
+    "\n",
+    "# Define scrapegraph sync client (API KEY is read from SGAI_API_KEY env var)\n",
+    "sgai_client = ScrapeGraphAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ySoE0Rowjgp1"
+   },
+   "outputs": [],
+   "source": [
+    "@action(reads=[], writes=[\"markdown_content\"])\n",
+    "def fetch_webpage(state: State, webpage_url: str) -> State:\n",
+    "    \"\"\"Fetch a webpage and convert it to Markdown.\"\"\"\n",
+    "    response = sgai_client.scrape(webpage_url, formats=[MarkdownFormatConfig()])\n",
+    "    if response.status != \"success\":\n",
+    "        raise RuntimeError(f\"scrape failed: {response.error}\")\n",
+    "    markdown_content = \"\\n\\n\".join(response.data.results[\"markdown\"][\"data\"])\n",
+    "    print(f\"Elapsed: {response.elapsed_ms}ms\")\n",
+    "    print(f\"Markdown content: {markdown_content[:200]}... (truncated)\")\n",
+    "    return state.update(markdown_content=markdown_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BNXvZkkLsOed"
+   },
+   "source": [
+    "#### \ud83d\udcc1 Define `embed_and_store` action to chunk the markdown and store it in a local vector store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zj-fQ7rbt4uc"
+   },
+   "source": [
+    "Define the data structure to hold the chunks in the `lancedb` vector store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "dqVyq0ONl6DY"
+   },
+   "outputs": [],
+   "source": [
+    "embedding_model = get_registry().get(\"openai\").create()\n",
+    "\n",
+    "class TextDocument(LanceModel):\n",
+    "    \"\"\"Simple data structure to hold a piece of text associated with a url.\"\"\"\n",
+    "    url: str\n",
+    "    position: int\n",
+    "    text: str = embedding_model.SourceField()\n",
+    "    vector: Vector(dim=embedding_model.ndims()) = embedding_model.VectorField()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "R97nRo-EtsGP"
+   },
+   "source": [
+    "Utils to create chunks based on the `number of tokens`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "OfFv8Zb9qaTD"
+   },
+   "outputs": [],
+   "source": [
+    "# Constants\n",
+    "CHUNK_SIZE = 2000  # Target size of each chunk in tokens\n",
+    "MIN_CHUNK_LENGTH_TO_EMBED = 5  # Discard chunks shorter than this\n",
+    "\n",
+    "# Initialize the tokenizer\n",
+    "tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n",
+    "\n",
+    "def get_text_chunks(text: str, chunk_token_size: Optional[int] = CHUNK_SIZE) -> List[str]:\n",
+    "    \"\"\"\n",
+    "    Split text into chunks of approximately `chunk_token_size` tokens.\n",
+    "\n",
+    "    Args:\n",
+    "        text: The input text to chunk.\n",
+    "        chunk_token_size: Target size of each chunk in tokens.\n",
+    "\n",
+    "    Returns:\n",
+    "        A list of text chunks.\n",
+    "    \"\"\"\n",
+    "    if not text.strip():\n",
+    "        return []\n",
+    "\n",
+    "    tokens = tokenizer.encode(text)\n",
+    "    chunks = []\n",
+    "    chunk_size = chunk_token_size or CHUNK_SIZE\n",
+    "\n",
+    "    while tokens:\n",
+    "        # Extract chunk tokens and decode to text\n",
+    "        chunk_tokens = tokens[:chunk_size]\n",
+    "        chunk_text = tokenizer.decode(chunk_tokens).strip()\n",
+    "\n",
+    "        # Skip chunks that are too short\n",
+    "        if len(chunk_text) > MIN_CHUNK_LENGTH_TO_EMBED:\n",
+    "            chunks.append(chunk_text)\n",
+    "\n",
+    "        # Remove processed tokens\n",
+    "        tokens = tokens[len(chunk_tokens):]\n",
+    "\n",
+    "    return chunks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ep4THhetuTey"
+   },
+   "source": [
+    "Let's define the action. It creates a webpages `local vector store` if not present and add the chunks to the `chunks table`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "RMk8gjUtk0gL"
+   },
+   "outputs": [],
+   "source": [
+    "@action(reads=[\"markdown_content\"], writes=[])\n",
+    "def embed_and_store(state: State, webpage_url: str) -> State:\n",
+    "    \"\"\"Embed and store the Markdown content.\"\"\"\n",
+    "    markdown_content = state[\"markdown_content\"]\n",
+    "\n",
+    "    chunks = get_text_chunks(markdown_content)\n",
+    "\n",
+    "    # Embed and store the chunks using LanceDB\n",
+    "    con = lancedb.connect(\"./webpages\")\n",
+    "    table = con.create_table(\"chunks\", exist_ok=True, schema=TextDocument)\n",
+    "    table.add([\n",
+    "        {\"text\": c, \"url\": webpage_url, \"position\": i}\n",
+    "        for i, c in enumerate(chunks)\n",
+    "    ])\n",
+    "\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zbP0km8BtN73"
+   },
+   "source": [
+    "#### \ud83d\udcac Define `ask_question` action to retrieve the most relevant chunks from the vector store and query them with a llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HTShnZowukj8"
+   },
+   "source": [
+    "Fetches the first 3 relevant chunks based on the user query and generate and answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "VXHbMtHxldKQ"
+   },
+   "outputs": [],
+   "source": [
+    "@action(reads=[], writes=[\"llm_answer\"])\n",
+    "def ask_question(state: State, user_query: str) -> State:\n",
+    "    \"\"\"Reply to the user's query using the webpage's content.\"\"\"\n",
+    "    # Retrieve the most relevant chunks\n",
+    "    chunks_table = lancedb.connect(\"./webpages\").open_table(\"chunks\")\n",
+    "    search_results = (\n",
+    "        chunks_table\n",
+    "        .search(user_query)\n",
+    "        .select([\"text\", \"url\", \"position\"])\n",
+    "        .limit(3)\n",
+    "        .to_list()\n",
+    "    )\n",
+    "    relevant_content = \"\\n\".join([r[\"text\"] for r in search_results])\n",
+    "\n",
+    "    # Prompt the LLM with the relevant content\n",
+    "    system_prompt = (\n",
+    "        \"Answer the user's questions based on the provided webpage content. \"\n",
+    "        f\"WEBPAGE CONTENT:\\n{relevant_content}\"\n",
+    "    )\n",
+    "\n",
+    "    client = openai.OpenAI()\n",
+    "    response = client.chat.completions.create(\n",
+    "        model=\"gpt-4o-mini\",\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": user_query}\n",
+    "        ],\n",
+    "    )\n",
+    "    llm_answer = response.choices[0].message.content\n",
+    "    return state.update(llm_answer=llm_answer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OOnKVmJTvORy"
+   },
+   "source": [
+    "#### \ud83e\udd16 Define the burr `application graph` and run it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "PL9oRtxpnYHy"
+   },
+   "outputs": [],
+   "source": [
+    "# Instrumenting OpenTelemetry for LanceDB and OpenAI\n",
+    "OpenAIInstrumentor().instrument()\n",
+    "LanceInstrumentor().instrument()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "W3f0m3CCm50A"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:burr.tracking.client:Creating storage directory: /Users/vikrant/.burr/chat-webpage-simple-rag\n",
+      "INFO:burr.tracking.client:Creating application directory: /Users/vikrant/.burr/chat-webpage-simple-rag/f709a752-e1d4-4f3c-a66f-372041de75d7\n",
+      "INFO:burr.telemetry:Note: Burr collects completely anonymous data about usage. This will help us improve Burr over time. See https://github.com/dagworks-inc/burr#usage-analytics--data-privacy for details.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the application graph\n",
+    "app = (\n",
+    "    ApplicationBuilder()\n",
+    "    .with_actions(fetch_webpage=fetch_webpage, embed_and_store=embed_and_store, ask_question=ask_question)\n",
+    "    .with_transitions((\"fetch_webpage\", \"embed_and_store\"), (\"embed_and_store\", \"ask_question\"))\n",
+    "    .with_entrypoint(\"fetch_webpage\")\n",
+    "    .with_tracker(project=\"chat-webpage-simple-rag\", use_otel_tracing=True)\n",
+    "    .build()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TRPUyVtbnGBZ",
+    "outputId": "9ddfbfbf-60fe-4592-aee7-caba292ce2eb"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:httpx:HTTP Request: POST https://v2-api.scrapegraphai.com/api/scrape \"HTTP/1.1 200 OK\"\n",
+      "ERROR:burr.core.application:\n",
+      "********************************************************************************\n",
+      "-------------------------------------------------------------------\n",
+      "Oh no an error! Need help with Burr?\n",
+      "Join our discord and ask for help! https://discord.gg/4FxBMyzW5n\n",
+      "-------------------------------------------------------------------\n",
+      "> Action: `embed_and_store` encountered an error!<\n",
+      "> State (at time of action):\n",
+      "{'__PRIOR_STEP': 'fetch_webpage',\n",
+      " '__SEQUENCE_ID': 1,\n",
+      " 'markdown_content': \"['[Introducing ScrapeGraphAI V2 -- better, faster,...\"}\n",
+      "> Inputs (at time of action):\n",
+      "{'user_query': 'Who are the founders?',\n",
+      " 'webpage_url': 'https://scrapegraphai.com/'}\n",
+      "********************************************************************************\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/vikrant/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py\", line 900, in _step\n",
+      "    result, new_state = _run_single_step_action(\n",
+      "                        ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/vikrant/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py\", line 298, in _run_single_step_action\n",
+      "    action.run_and_update(state, **inputs), action.name, action.schema\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/vikrant/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/action.py\", line 671, in run_and_update\n",
+      "    return self._fn(state, **self._bound_params, **run_kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/var/folders/yd/9h_4byqs4d93gx_kfmkc24gm0000gn/T/ipykernel_13467/1585446328.py\", line 6, in embed_and_store\n",
+      "    chunks = get_text_chunks(markdown_content)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/var/folders/yd/9h_4byqs4d93gx_kfmkc24gm0000gn/T/ipykernel_13467/4237614586.py\", line 19, in get_text_chunks\n",
+      "    if not text.strip():\n",
+      "           ^^^^^^^^^^\n",
+      "AttributeError: 'list' object has no attribute 'strip'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elapsed: 483ms\n",
+      "Markdown content: ['[Introducing ScrapeGraphAI V2 -- better, faster, cheaper APIs. Read the blog ->](https://scrapegraphai.com/blog/scrapegraphai-v2)\\n\\n[![ScrapeGraphAI](https://scrapegraphai.com/logos/logo-light.svg)![ScrapeGraphAI](https://scrapegraphai.com/logos/logo-dark.svg)ScrapeGraphAI](https://scrapegraphai.com/)[Pricing](https://scrapegraphai.com/pricing)[Blog](https://scrapegraphai.com/blog)[Docs](https://docs.scrapegraphai.com/introduction)[For Startups](https://scrapegraphai.com/for-startups)Resources\\n\\n[Sign In](https://scrapegraphai.com/login)//\\u200b[Sign Up](https://scrapegraphai.com/signup)//\\u200b[...](https://github.com/ScrapeGraphAI/Scrapegraph-ai)//\\u200b[Book a Call](https://calendly.com/lorenzo-padoan-scrapegraphai/30min)//\\u200b\\n\\n[Sign In](https://scrapegraphai.com/login)//\\u200b[Sign Up](https://scrapegraphai.com/signup)\\n\\nDarkToggle menu\\n\\n* * *\\n\\n/ Yearly Plans\\n\\nBilled upfront with 15% savings on all plans.\\n\\n[Discover ->](https://scrapegraphai.com/pricing)\\n\\n|\\n\\n# The scraper  \\nfor the AI Era\\n\\n[ 01 ]No proxies.\\n\\n[ 02 ]No maintenance.\\n\\n[ 03 ]Just reliable data extraction.\\n\\n[Get Started ->](https://scrapegraphai.com/dashboard)[Read the Documentation ->](https://docs.scrapegraphai.com/)\\n\\n// Trusted worldwide\\n\\n## Who uses us\\n\\n[26.5k+Stars on GitHub](https://github.com/ScrapeGraphAI/Scrapegraph-ai)\\n\\n40M+\\n\\nExtracted Webpages\\n\\n1M+\\n\\nUnique Users\\n\\n// Connect with your stack\\n\\n## Official integrations\\n\\n![Python SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fpython-logo.webp&w=3840&q=75)\\n\\n### Python SDK\\n\\n[Install SDK](https://pypi.org/project/scrapegraph-py/)\\n\\n![JavaScript SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fjavascript.webp&w=3840&q=75)\\n\\n### JavaScript SDK\\n\\n[Install SDK](https://www.npmjs.com/package/scrapegraph-js)\\n\\n![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-light.webp&w=3840&q=75)![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-dark.webp&w=3840&q=75)\\n\\n### CLI\\n\\n[Get started](https://skills.sh/scrapegraphai/just-scrape/just-scrape)\\n\\n![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp-light.webp&w=3840&q=75)![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp.webp&w=3840&q=75)\\n\\n### MCP\\n\\n[View repo](https://github.com/ScrapeGraphAI/scrapegraph-mcp)\\n\\n![LangChain](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Flangchain.webp&w=3840&q=75)\\n\\n### LangChain\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/langchain)\\n\\n![CrewAI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcrewai.webp&w=3840&q=75)\\n\\n### CrewAI\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/crewai)\\n\\n![LlamaIndex](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fllama_index.webp&w=3840&q=75)\\n\\n### LlamaIndex\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/llamaindex)\\n\\n![Agno](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fagno.webp&w=3840&q=75)\\n\\n### Agno\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/agno)\\n\\n![Smithery](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fsmithery.webp&w=3840&q=75)\\n\\n### Smithery\\n\\n[View docs](https://docs.scrapegraphai.com/services/mcp-server/smithery)\\n\\n![n8n](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fn8n.webp&w=3840&q=75)\\n\\n### n8n\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/n8n)\\n\\n![Zapier](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fzapier.webp&w=3840&q=75)\\n\\n### Zapier\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/zapier)\\n\\n![Make](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmake.webp&w=3840&q=75)\\n\\n### Make\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/make)\\n\\n![Python SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fpython-logo.webp&w=3840&q=75)\\n\\n### Python SDK\\n\\n[Install SDK](https://pypi.org/project/scrapegraph-py/)\\n\\n![JavaScript SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fjavascript.webp&w=3840&q=75)\\n\\n### JavaScript SDK\\n\\n[Install SDK](https://www.npmjs.com/package/scrapegraph-js)\\n\\n![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-light.webp&w=3840&q=75)![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-dark.webp&w=3840&q=75)\\n\\n### CLI\\n\\n[Get started](https://skills.sh/scrapegraphai/just-scrape/just-scrape)\\n\\n![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp-light.webp&w=3840&q=75)![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp.webp&w=3840&q=75)\\n\\n### MCP\\n\\n[View repo](https://github.com/ScrapeGraphAI/scrapegraph-mcp)\\n\\n![LangChain](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Flangchain.webp&w=3840&q=75)\\n\\n### LangChain\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/langchain)\\n\\n![CrewAI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcrewai.webp&w=3840&q=75)\\n\\n### CrewAI\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/crewai)\\n\\n![LlamaIndex](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fllama_index.webp&w=3840&q=75)\\n\\n### LlamaIndex\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/llamaindex)\\n\\n![Agno](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fagno.webp&w=3840&q=75)\\n\\n### Agno\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/agno)\\n\\n![Smithery](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fsmithery.webp&w=3840&q=75)\\n\\n### Smithery\\n\\n[View docs](https://docs.scrapegraphai.com/services/mcp-server/smithery)\\n\\n![n8n](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fn8n.webp&w=3840&q=75)\\n\\n### n8n\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/n8n)\\n\\n![Zapier](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fzapier.webp&w=3840&q=75)\\n\\n### Zapier\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/zapier)\\n\\n![Make](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmake.webp&w=3840&q=75)\\n\\n### Make\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/make)\\n\\n![Python SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fpython-logo.webp&w=3840&q=75)\\n\\n### Python SDK\\n\\n[Install SDK](https://pypi.org/project/scrapegraph-py/)\\n\\n![JavaScript SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fjavascript.webp&w=3840&q=75)\\n\\n### JavaScript SDK\\n\\n[Install SDK](https://www.npmjs.com/package/scrapegraph-js)\\n\\n![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-light.webp&w=3840&q=75)![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-dark.webp&w=3840&q=75)\\n\\n### CLI\\n\\n[Get started](https://skills.sh/scrapegraphai/just-scrape/just-scrape)\\n\\n![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp-light.webp&w=3840&q=75)![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp.webp&w=3840&q=75)\\n\\n### MCP\\n\\n[View repo](https://github.com/ScrapeGraphAI/scrapegraph-mcp)\\n\\n![LangChain](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Flangchain.webp&w=3840&q=75)\\n\\n### LangChain\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/langchain)\\n\\n![CrewAI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcrewai.webp&w=3840&q=75)\\n\\n### CrewAI\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/crewai)\\n\\n![LlamaIndex](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fllama_index.webp&w=3840&q=75)\\n\\n### LlamaIndex\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/llamaindex)\\n\\n![Agno](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fagno.webp&w=3840&q=75)\\n\\n### Agno\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/agno)\\n\\n![Smithery](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fsmithery.webp&w=3840&q=75)\\n\\n### Smithery\\n\\n[View docs](https://docs.scrapegraphai.com/services/mcp-server/smithery)\\n\\n![n8n](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fn8n.webp&w=3840&q=75)\\n\\n### n8n\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/n8n)\\n\\n![Zapier](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fzapier.webp&w=3840&q=75)\\n\\n### Zapier\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/zapier)\\n\\n![Make](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmake.webp&w=3840&q=75)\\n\\n### Make\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/make)\\n\\n![Python SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fpython-logo.webp&w=3840&q=75)\\n\\n### Python SDK\\n\\n[Install SDK](https://pypi.org/project/scrapegraph-py/)\\n\\n![JavaScript SDK](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fjavascript.webp&w=3840&q=75)\\n\\n### JavaScript SDK\\n\\n[Install SDK](https://www.npmjs.com/package/scrapegraph-js)\\n\\n![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-light.webp&w=3840&q=75)![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-dark.webp&w=3840&q=75)\\n\\n### CLI\\n\\n[Get started](https://skills.sh/scrapegraphai/just-scrape/just-scrape)\\n\\n![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp-light.webp&w=3840&q=75)![MCP](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp.webp&w=3840&q=75)\\n\\n### MCP\\n\\n[View repo](https://github.com/ScrapeGraphAI/scrapegraph-mcp)\\n\\n![LangChain](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Flangchain.webp&w=3840&q=75)\\n\\n### LangChain\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/langchain)\\n\\n![CrewAI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcrewai.webp&w=3840&q=75)\\n\\n### CrewAI\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/crewai)\\n\\n![LlamaIndex](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fllama_index.webp&w=3840&q=75)\\n\\n### LlamaIndex\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/llamaindex)\\n\\n![Agno](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fagno.webp&w=3840&q=75)\\n\\n### Agno\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/agno)\\n\\n![Smithery](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fsmithery.webp&w=3840&q=75)\\n\\n### Smithery\\n\\n[View docs](https://docs.scrapegraphai.com/services/mcp-server/smithery)\\n\\n![n8n](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fn8n.webp&w=3840&q=75)\\n\\n### n8n\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/n8n)\\n\\n![Zapier](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fzapier.webp&w=3840&q=75)\\n\\n### Zapier\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/zapier)\\n\\n![Make](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmake.webp&w=3840&q=75)\\n\\n### Make\\n\\n[View docs](https://docs.scrapegraphai.com/integrations/make)\\n\\n[See all the integrations ->](https://scrapegraphai.com/integrations)\\n\\n// Why ScrapeGraph\\n\\n## Turn any webpage into structured data with one API call\\n\\ncURLPythonJavaScript\\n\\n### Scrape\\n\\nConvert any webpage to clean, well-formatted Markdown. Documentation, content migration, or preparing context for LLMs.\\n\\nrequestresponse\\n    \\n    \\n    curl -X POST \\\\\\n      https://v2-api.scrapegraphai.com/api/scrape \\\\\\n      -H \"Content-Type: application/json\" \\\\\\n      -H \"SGAI-APIKEY: $SGAI_API_KEY\" \\\\\\n      -d \\'{\\n      \"url\": \"https://docs.example.com/getting-started\",\\n      \"formats\": [\\n        {\\n          \"type\": \"markdown\"\\n        }\\n      ]\\n    }\\'\\n\\n### Extract\\n\\nExtract structured data from any webpage using natural language prompts. Define a schema and get back clean JSON -- product details, contact info, or any content.\\n\\nrequestresponse\\n    \\n    \\n    curl -X POST \\\\\\n      https://v2-api.scrapegraphai.com/api/extract \\\\\\n      -H \"Content-Type: application/json\" \\\\\\n      -H \"SGAI-APIKEY: $SGAI_API_KEY\" \\\\\\n      -d \\'{\\n      \"url\": \"https://news.ycombinator.com\",\\n      \"prompt\": \"Extract the top 3 stories with title, points, and author\"\\n    }\\'\\n\\n### Search\\n\\nSearch the web and extract structured data from results in one call. Market research, competitor analysis, and trend tracking.\\n\\nrequestresponse\\n    \\n    \\n    curl -X POST \\\\\\n      https://v2-api.scrapegraphai.com/api/search \\\\\\n      -H \"Content-Type: application/json\" \\\\\\n      -H \"SGAI-APIKEY: $SGAI_API_KEY\" \\\\\\n      -d \\'{\\n      \"query\": \"sustainable packaging trends 2025\",\\n      \"numResults\": 3,\\n      \"prompt\": \"Extract top trends with market size\",\\n      \"locationGeoCode\": \"us\"\\n    }\\'\\n\\n### Crawl\\n\\nCrawl all the pages on a website and get structured data for each link. Site-wide extraction, content audits, and data pipelines.\\n\\nrequestresponse\\n    \\n    \\n    curl -X POST \\\\\\n      https://v2-api.scrapegraphai.com/api/crawl \\\\\\n      -H \"Content-Type: application/json\" \\\\\\n      -H \"SGAI-APIKEY: $SGAI_API_KEY\" \\\\\\n      -d \\'{\\n      \"url\": \"https://docs.example.com\"\\n    }\\'\\n\\n### Monitor\\n\\nWatch webpages for changes and get notified via webhook. Price tracking, content updates, and competitor intelligence.\\n\\nrequestresponse\\n    \\n    \\n    curl -X POST \\\\\\n      https://v2-api.scrapegraphai.com/api/monitor \\\\\\n      -H \"Content-Type: application/json\" \\\\\\n      -H \"SGAI-APIKEY: $SGAI_API_KEY\" \\\\\\n      -d \\'{\\n      \"url\": \"https://example-shop.com/product\",\\n      \"interval\": \"0 */6 * * *\",\\n      \"webhookUrl\": \"https://your-app.com/webhook\",\\n      \"formats\": [\\n        {\\n          \"type\": \"markdown\"\\n        }\\n      ]\\n    }\\'\\n\\n// Built for every workflow\\n\\n## Use Cases\\n\\n![Price Monitoring Bot](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fprice-monitoring-bot-dark.jpeg&w=3840&q=75)![Price Monitoring Bot](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fprice-monitoring-bot-light.jpeg&w=3840&q=75)\\n\\n### Price Monitoring Bot\\n\\nAmazon productseBay listingsShopify stores\\n\\nTrack competitor prices on Amazon, eBay, and other e-commerce sites. Get alerts when prices drop or inventory changes.\\n\\n[Read more ->](https://scrapegraphai.com/blog/price-monitoring-bot)\\n\\n![Lead Generation Tool](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Flead-generation-tool-dark.jpeg&w=3840&q=75)![Lead Generation Tool](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Flead-generation-tool-light.jpeg&w=3840&q=75)\\n\\n### Lead Generation Tool\\n\\nLinkedIn profilesTwitter usersCompany contacts\\n\\nExtract LinkedIn profiles, Twitter accounts, and contact information at scale without getting blocked.\\n\\n[Read more ->](https://scrapegraphai.com/blog/lead-generation-scraping)\\n\\n![Market Research Dashboard](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fmarket-research-dashboard-dark.jpeg&w=3840&q=75)![Market Research Dashboard](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fmarket-research-dashboard-light.jpeg&w=3840&q=75)\\n\\n### Market Research Dashboard\\n\\nProduct reviewsApp ratingsCustomer sentiment\\n\\nAggregate reviews, ratings, and sentiment from multiple sites. Build comprehensive competitor analysis.\\n\\n[Read more ->](https://scrapegraphai.com/blog/market-research-scraping)\\n\\n![Real Estate Tracker](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Freal-estate-tracker-dark.jpeg&w=3840&q=75)![Real Estate Tracker](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Freal-estate-tracker-light.jpeg&w=3840&q=75)\\n\\n### Real Estate Tracker\\n\\nZillow listingsRedfin dataRental properties\\n\\nMonitor property listings on Zillow, Redfin, and local sites. Track price changes and new listings.\\n\\n[Read more ->](https://scrapegraphai.com/blog/real-estate-scraping-guide)\\n\\n![MCP Server](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fmcp-server-dark.jpeg&w=3840&q=75)![MCP Server](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fmcp-server-light.jpeg&w=3840&q=75)\\n\\n### MCP Server\\n\\nClaude DesktopCursor IDEAI Workflows\\n\\nConnect AI assistants directly to the web via Model Context Protocol. Enable Claude, Cursor, and other AI tools to scrape data in real-time.\\n\\n[Read more ->](https://scrapegraphai.com/blog/mcp-server-guide)\\n\\n![AI Agent Tool](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fai-agent-tool-dark.jpeg&w=3840&q=75)![AI Agent Tool](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fmarketing%2Fuse-cases%2Fai-agent-tool-light.jpeg&w=3840&q=75)\\n\\n### AI Agent Tool\\n\\nRAG pipelinesAutonomous agentsReal-time data\\n\\nProvide agents with extremely fast web access. Perfect for RAG pipelines, autonomous research, and real-time data enrichment.\\n\\n[Read more ->](https://scrapegraphai.com/blog/ai-agent-scraping-tool)\\n\\nDon\\'t see your use case? Our AI adapts to any website structure.\\n\\n[Start Building Now ->](https://scrapegraphai.com/dashboard)[View Examples ->](https://docs.scrapegraphai.com/)\\n\\n// Choose the plan that fits your needs\\n\\n## Simple, transparent pricing  \\nwith no hidden fees.\\n\\nMonthlyYearly\\n\\nSave up to 15%\\n\\nFree Plan\\n\\nOpen-source library\\n\\n$0/mo\\n\\nbilled $0/year\\n\\n500 credits/month\\n\\n  * 500 API credits, one-time\\n  * 10 requests/min\\n  * 1 monitor\\n  * 1 concurrent crawl\\n\\n\\n\\n[Start Free](https://scrapegraphai.com/signup)\\n\\nStarter Plan\\n\\nGreat for individuals or small projects\\n\\n$17/mo\\n\\nbilled $204/yearSave $36\\n\\n10,000 credits/month\\n\\n  * 10,000 API credits per month\\n  * 100 requests/min\\n  * 5 monitors\\n  * 3 concurrent crawls\\n\\n\\n\\n[Get Started!](https://scrapegraphai.com/signup)\\n\\nMost Popular\\n\\nGrowth Plan\\n\\nRecommended for growing businesses\\n\\n$85/mo\\n\\nbilled $1020/yearSave $180\\n\\n100,000 credits/month\\n\\n  * 100,000 API credits per month\\n  * 500 requests/min\\n  * 25 monitors\\n  * 15 concurrent crawls\\n  * Basic Proxy Rotation\\n\\n\\n\\n[Get Started!](https://scrapegraphai.com/signup)\\n\\nPro Plan\\n\\nOptimized for data-driven teams\\n\\n$425/mo\\n\\nbilled $5100/yearSave $900\\n\\n750,000 credits/month\\n\\n  * 750,000 API credits per month\\n  * 5,000 requests/min\\n  * 100 monitors\\n  * 50 concurrent crawls\\n  * Advanced Proxy Rotation\\n  * Priority support\\n\\n\\n\\n[Get Started!](https://scrapegraphai.com/signup)\\n\\nEnterprise\\n\\nTailored solutions for large organizations.\\n\\nCustom\\n\\n  * Ad-hoc credits\\n  * Custom rate limits\\n  * Dedicated support\\n  * SLA guarantee\\n\\n\\n\\n[Contact Sales](https://scrapegraphai.com/contacts)\\n\\n// One-time credit top-ups\\n\\n### Need more credits? Top-up anytime.\\n\\nCredit packs never expire and stack on top of your monthly subscription.\\n\\nSmall\\n\\n$5one-time\\n\\n1,000 credits\\n\\n$5.00 per 1k credits\\n\\n  * Never expires\\n  * Stacks on subscription\\n\\n\\n\\n[Get Started!](https://scrapegraphai.com/signup)\\n\\nMedium\\n\\n$40one-time\\n\\n10,000 credits\\n\\n$4.00 per 1k credits\\n\\n  * Never expires\\n  * Stacks on subscription\\n\\n\\n\\n[Get Started!](https://scrapegraphai.com/signup)\\n\\nLarge\\n\\n$150one-time\\n\\n50,000 credits\\n\\n$3.00 per 1k credits\\n\\n  * Never expires\\n  * Stacks on subscription\\n\\n\\n\\n[Get Started!](https://scrapegraphai.com/signup)\\n\\n// How credits work\\n\\n### Pay only for what you use\\n\\n#### Scrape\\n\\nConvert any URL to clean markdown, HTML, screenshots, or branding analysis\\n\\nBase Cost\\n\\n1 (markdown) * 2 (screenshot) * 25 (branding)\\n\\nFormula\\n\\nformat base + stealth modifier\\n\\n#### Extract\\n\\nExtract structured data from any webpage using natural language prompts\\n\\nBase Cost\\n\\n5\\n\\nFormula\\n\\n5 + stealth modifier\\n\\n#### Search\\n\\nSearch the web and extract data from results in one call\\n\\nBase Cost\\n\\n2/result (no prompt) * 5/result (with prompt)\\n\\nFormula\\n\\n(base + stealth modifier) \u00d7 results\\n\\n#### Crawl\\n\\nCrawl entire websites and extract data from every page\\n\\nBase Cost\\n\\n2 startup + per-page scrape cost\\n\\nFormula\\n\\n2 + (format base + stealth modifier) \u00d7 pages\\n\\n#### Monitor\\n\\nWatch webpages for changes and get notified via webhook\\n\\nBase Cost\\n\\n+5 on change detected\\n\\nFormula\\n\\nformat base + stealth modifier + change bonus\\n\\nEndpoint| Base Cost| Formula  \\n---|---|---  \\nScrapeConvert any URL to clean markdown, HTML, screenshots, or branding analysis| 1 (markdown) * 2 (screenshot) * 25 (branding)| format base + stealth modifier  \\nExtractExtract structured data from any webpage using natural language prompts| 5| 5 + stealth modifier  \\nSearchSearch the web and extract data from results in one call| 2/result (no prompt) * 5/result (with prompt)| (base + stealth modifier) \u00d7 results  \\nCrawlCrawl entire websites and extract data from every page| 2 startup + per-page scrape cost| 2 + (format base + stealth modifier) \u00d7 pages  \\nMonitorWatch webpages for changes and get notified via webhook| +5 on change detected| format base + stealth modifier + change bonus  \\n  \\n// Extra features\\n\\n### Optional fetch settings\\n\\n#### Render mode: auto / fast / js\\n\\nChanges the fetch strategy. Render mode does not change the credit cost.\\n\\nCredit Modifier\\n\\n+0\\n\\n#### Stealth toggle\\n\\nAdds anti-bot bypass on top of the selected render mode.\\n\\nCredit Modifier\\n\\n+5\\n\\nFetch Setting| Credit Modifier  \\n---|---  \\nRender mode: auto / fast / jsChanges the fetch strategy. Render mode does not change the credit cost.| +0  \\nStealth toggleAdds anti-bot bypass on top of the selected render mode.| +5  \\n  \\n// Estimate your costs\\n\\n### Credit Calculator\\n\\nScrapeExtractSearchCrawlMonitor\\n\\nURL to markdown, HTML, screenshot\\n\\nRender Mode\\n\\nStealth\\n\\n+5 credits\\n\\nFormat\\n\\nPer Request1Credits\\n\\n// AI assistant integration\\n\\n## Model Context Protocol, Skills & CLI\\n\\n![MCP Logo](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp-light.webp&w=3840&q=75)![MCP Logo](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fmcp.webp&w=3840&q=75)\\n\\n[MCP Documentation](https://docs.scrapegraphai.com/services/mcp-server)[Install Skill](https://skills.sh/scrapegraphai/just-scrape/just-scrape)[CLI Documentation](https://docs.scrapegraphai.com/services/cli/introduction)\\n\\nUse ScrapeGraph in\\n\\n![Claude](https://scrapegraphai.com/images/logos/claude.svg)\\n\\n### Claude\\n\\n[Use Claude with MCP](https://scrapegraphai.com/blog/claude-scraping-beast-machine)\\n\\n![Skill](https://scrapegraphai.com/images/logos/skills-light.svg)![Skill](https://scrapegraphai.com/images/logos/skills-dark.svg)\\n\\n### Skill\\n\\n[Install Skill](https://skills.sh/scrapegraphai/just-scrape/just-scrape)\\n\\n![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-light.webp&w=3840&q=75)![CLI](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fintegrations%2Fcli-dark.webp&w=3840&q=75)\\n\\n### CLI\\n\\n[Install CLI](https://github.com/ScrapeGraphAI/just-scrape)\\n\\n![ChatGPT](https://scrapegraphai.com/_next/image?url=%2Fimages%2Flogos%2Fopenai.png&w=3840&q=75)![ChatGPT](https://scrapegraphai.com/_next/image?url=%2Fimages%2Flogos%2Fopenai-dark.png&w=3840&q=75)\\n\\n### ChatGPT\\n\\n[Use ChatGPT with MCP](https://scrapegraphai.com/blog/claude-scraping-beast-machine)\\n\\n![Gemini](https://scrapegraphai.com/_next/image?url=%2Fimages%2Flogos%2Fgemini.png&w=3840&q=75)\\n\\n### Gemini\\n\\n[Use Gemini with MCP](https://scrapegraphai.com/blog/claude-scraping-beast-machine)\\n\\n// How we compare\\n\\n## Features Comparison\\n\\nFeature| ScrapeGraphAI| Scrapy| BeautifulSoup| Selenium  \\n---|---|---|---|---  \\nAI-Powered Extraction| | | |   \\nZero Maintenance| | | |   \\nAutomatic Proxy Management| | | |   \\nJavaScript Rendering| | | |   \\nAuto-Adapts to Changes| | | |   \\nBuilt-in Rate Limiting| | | |   \\nCloud-Ready API| | | |   \\nAI-Agent Ready| | | |   \\nEnterprise Support| | | |   \\n  \\n// What people say\\n\\n## Testimonials\\n\\n![Jason Touleyrou](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fjason_touley.jpeg&w=96&q=75)\\n\\n### Jason Touleyrou\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Recently, I stumbled upon an intriguing tool--Scrapegraph-ai. It\\'s a fascinating blend of code and user prompts designed to streamline website scraping. My first attempt with Scrapegraph-ai was a breeze. It took less than five minutes to set up, and the results were immediate and impressive.\"\\n\\n![Mehandi Islam](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fmehandi.jpeg&w=96&q=75)\\n\\n### Mehandi Islam\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Web Scraping Made Easy: Automate Data Extraction with ScrapeGraphAI. This tool has revolutionized how we approach data collection.\"\\n\\n![Soham Kshirsagar](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fsoham.jpeg&w=96&q=75)\\n\\n### Soham Kshirsagar\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"ScrapeGraphAI is revolutionizing web scraping by leveraging Large Language Models (LLMs) to simplify the data extraction process.\"\\n\\n![Kuldeep Singh Sidhu](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fkuldeep.jpeg&w=96&q=75)\\n\\n### Kuldeep Singh Sidhu\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Are you tired of writing scripts to scrape data from the web? ScrapeGraphAI is here for you! It\\'s an absolute game-changer.\"\\n\\n![Thomas Janssen](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fthomas.jpeg&w=96&q=75)\\n\\n### Thomas Janssen\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Meet ScrapeGraphAI, a REVOLUTION in Web Scraping. With ScrapeGraphAI you use a Large Language Model to do the heavy lifting. No more hours spent debugging selectors!\"\\n\\n![Nagesh Mustapure](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fnagesh.jpeg&w=96&q=75)\\n\\n### Nagesh Mustapure\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Unlocking the Power of Web Data with ScrapeGraphAI! With data growing at an exponential rate, leveraging web data effectively is crucial. Enter ScrapeGraphAI--a cutting-edge tool revolutionizing web scraping.\"\\n\\n![Jason Touleyrou](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fjason_touley.jpeg&w=96&q=75)\\n\\n### Jason Touleyrou\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Recently, I stumbled upon an intriguing tool--Scrapegraph-ai. It\\'s a fascinating blend of code and user prompts designed to streamline website scraping. My first attempt with Scrapegraph-ai was a breeze. It took less than five minutes to set up, and the results were immediate and impressive.\"\\n\\n![Mehandi Islam](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fmehandi.jpeg&w=96&q=75)\\n\\n### Mehandi Islam\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Web Scraping Made Easy: Automate Data Extraction with ScrapeGraphAI. This tool has revolutionized how we approach data collection.\"\\n\\n![Soham Kshirsagar](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fsoham.jpeg&w=96&q=75)\\n\\n### Soham Kshirsagar\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"ScrapeGraphAI is revolutionizing web scraping by leveraging Large Language Models (LLMs) to simplify the data extraction process.\"\\n\\n![Kuldeep Singh Sidhu](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fkuldeep.jpeg&w=96&q=75)\\n\\n### Kuldeep Singh Sidhu\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Are you tired of writing scripts to scrape data from the web? ScrapeGraphAI is here for you! It\\'s an absolute game-changer.\"\\n\\n![Thomas Janssen](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fthomas.jpeg&w=96&q=75)\\n\\n### Thomas Janssen\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Meet ScrapeGraphAI, a REVOLUTION in Web Scraping. With ScrapeGraphAI you use a Large Language Model to do the heavy lifting. No more hours spent debugging selectors!\"\\n\\n![Nagesh Mustapure](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fnagesh.jpeg&w=96&q=75)\\n\\n### Nagesh Mustapure\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Unlocking the Power of Web Data with ScrapeGraphAI! With data growing at an exponential rate, leveraging web data effectively is crucial. Enter ScrapeGraphAI--a cutting-edge tool revolutionizing web scraping.\"\\n\\n![Jason Touleyrou](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fjason_touley.jpeg&w=96&q=75)\\n\\n### Jason Touleyrou\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Recently, I stumbled upon an intriguing tool--Scrapegraph-ai. It\\'s a fascinating blend of code and user prompts designed to streamline website scraping. My first attempt with Scrapegraph-ai was a breeze. It took less than five minutes to set up, and the results were immediate and impressive.\"\\n\\n![Mehandi Islam](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fmehandi.jpeg&w=96&q=75)\\n\\n### Mehandi Islam\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Web Scraping Made Easy: Automate Data Extraction with ScrapeGraphAI. This tool has revolutionized how we approach data collection.\"\\n\\n![Soham Kshirsagar](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fsoham.jpeg&w=96&q=75)\\n\\n### Soham Kshirsagar\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"ScrapeGraphAI is revolutionizing web scraping by leveraging Large Language Models (LLMs) to simplify the data extraction process.\"\\n\\n![Kuldeep Singh Sidhu](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fkuldeep.jpeg&w=96&q=75)\\n\\n### Kuldeep Singh Sidhu\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Are you tired of writing scripts to scrape data from the web? ScrapeGraphAI is here for you! It\\'s an absolute game-changer.\"\\n\\n![Thomas Janssen](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fthomas.jpeg&w=96&q=75)\\n\\n### Thomas Janssen\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Meet ScrapeGraphAI, a REVOLUTION in Web Scraping. With ScrapeGraphAI you use a Large Language Model to do the heavy lifting. No more hours spent debugging selectors!\"\\n\\n![Nagesh Mustapure](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fnagesh.jpeg&w=96&q=75)\\n\\n### Nagesh Mustapure\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Unlocking the Power of Web Data with ScrapeGraphAI! With data growing at an exponential rate, leveraging web data effectively is crucial. Enter ScrapeGraphAI--a cutting-edge tool revolutionizing web scraping.\"\\n\\n![Jason Touleyrou](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fjason_touley.jpeg&w=96&q=75)\\n\\n### Jason Touleyrou\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Recently, I stumbled upon an intriguing tool--Scrapegraph-ai. It\\'s a fascinating blend of code and user prompts designed to streamline website scraping. My first attempt with Scrapegraph-ai was a breeze. It took less than five minutes to set up, and the results were immediate and impressive.\"\\n\\n![Mehandi Islam](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fmehandi.jpeg&w=96&q=75)\\n\\n### Mehandi Islam\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Web Scraping Made Easy: Automate Data Extraction with ScrapeGraphAI. This tool has revolutionized how we approach data collection.\"\\n\\n![Soham Kshirsagar](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fsoham.jpeg&w=96&q=75)\\n\\n### Soham Kshirsagar\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"ScrapeGraphAI is revolutionizing web scraping by leveraging Large Language Models (LLMs) to simplify the data extraction process.\"\\n\\n![Kuldeep Singh Sidhu](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fkuldeep.jpeg&w=96&q=75)\\n\\n### Kuldeep Singh Sidhu\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Are you tired of writing scripts to scrape data from the web? ScrapeGraphAI is here for you! It\\'s an absolute game-changer.\"\\n\\n![Thomas Janssen](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fthomas.jpeg&w=96&q=75)\\n\\n### Thomas Janssen\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Meet ScrapeGraphAI, a REVOLUTION in Web Scraping. With ScrapeGraphAI you use a Large Language Model to do the heavy lifting. No more hours spent debugging selectors!\"\\n\\n![Nagesh Mustapure](https://scrapegraphai.com/_next/image?url=%2Fimages%2Ftestimonials%2Fnagesh.jpeg&w=96&q=75)\\n\\n### Nagesh Mustapure\\n\\nLinkedIn\\n\\nScrapeGraphAIWeb Scraping API\\n\\n> \"Unlocking the Power of Web Data with ScrapeGraphAI! With data growing at an exponential rate, leveraging web data effectively is crucial. Enter ScrapeGraphAI--a cutting-edge tool revolutionizing web scraping.\"\\n\\n// Latest from the blog\\n\\n## Blogs\\n\\n[NewsWelcome 2026: The Year of the Spider in Web ScrapingScrapeGraphAI![](https://scrapegraphai.com/logos/logo-light.svg)![](https://scrapegraphai.com/logos/logo-dark.svg)[ 01 ]Welcome 2026: The Year of the Spider in Web ScrapingWelcome to 2026, the Year of the Spider. Discover what this means for AI-powered web scraping and data extraction going forward.Read more->](https://scrapegraphai.com/blog/2026-year-of-spider)[ComparisonsWhy AI Web Scraping Beats Search APIs for DataScrapeGraphAI![](https://scrapegraphai.com/logos/logo-light.svg)![](https://scrapegraphai.com/logos/logo-dark.svg)[ 02 ]Why AI Web Scraping Beats Search APIs for DataWhy search APIs like Firecrawl and Exa fall short for structured data. See how an AI-first scraping approach cuts costs and delivers better results.Read more->](https://scrapegraphai.com/blog/why-scraping-is-more-important-than-search)[![100k Dataset for Structured LLM Output Research](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fblog%2Fscrapegrahai-100k%2Fog.webp&w=3840&q=75)![100k Dataset for Structured LLM Output Research](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fblog%2Fscrapegrahai-100k%2Fog.webp&w=3840&q=75)[ 03 ]100k Dataset for Structured LLM Output ResearchScrapeGraphAI 100k: a dataset of 100,000 real-world structured extraction examples from 9M user events. Open on Hugging Face for LLM and AI agent research.Read more->](https://scrapegraphai.com/blog/scrapegrahai-100k)\\n\\n[View all posts ->](https://scrapegraphai.com/blog)\\n\\n// FAQ\\n\\n## Frequently Asked Questions\\n\\n### What is ScrapeGraphAI?\\n\\nScrapeGraphAI is an AI-powered web scraping API that lets you extract structured data from any website using natural language prompts. No complex selectors, no brittle scripts -- just describe what you want and get clean JSON back.\\n\\n### How does AI-powered web scraping work?\\n\\n### Do I need to write CSS selectors or XPath to scrape websites?\\n\\n### What data formats does the web scraping API support?\\n\\n### Is ScrapeGraphAI free to use?\\n\\n### Can ScrapeGraphAI scrape JavaScript-rendered pages and SPAs?\\n\\n### How fast is the ScrapeGraphAI API?\\n\\n### Is there an MCP server for AI agents?\\n\\n// Ready to start\\n\\n## Give your AI Agent superpowers with lightning-fast web data!\\n\\n[Get Started ->](https://scrapegraphai.com/signup)\\n\\n## ScrapeGraphAI\\n\\n![](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fspider-footer-dark.webp&w=1080&q=75)![](https://scrapegraphai.com/_next/image?url=https%3A%2F%2Fpub-308fdc8a1858487d9ab275774108077b.r2.dev%2Fimages%2Fspider-footer-light.webp&w=1080&q=75)\\n\\n* * *\\n\\n### Product\\n\\n  * [API Status](https://scrapegraphai.com/api-status)\\n  * [Compare](https://scrapegraphai.com/compare)\\n  * [Changelog](https://scrapegraphai.com/changelog)\\n  * [Pricing](https://scrapegraphai.com/pricing)\\n\\n\\n\\n### Resources\\n\\n  * [Blog](https://scrapegraphai.com/blog)\\n  * [Glossary](https://scrapegraphai.com/glossary)\\n  * [For Startups](https://scrapegraphai.com/for-startups)\\n  * [Branding](https://scrapegraphai.com/branding)\\n  * [Manifesto](https://github.com/ScrapeGraphAI/ScrapeGraphAI-manifesto)\\n\\n\\n\\n### Social\\n\\n[Github](https://github.com/ScrapeGraphAI)[X](https://x.com/scrapegraphai)[Reddit](https://www.reddit.com/r/scrapegraphai/)[Discord](https://discord.gg/JqvBb7wV8j)[Medium](https://medium.com/@scrapegraphai)\\n\\n![NVIDIA Inception Program](https://scrapegraphai.com/_next/image?url=%2Fimages%2Fnvidia-inception-last.png&w=256&q=75)[![SOC 2 Certified](https://scrapegraphai.com/_next/image?url=%2Fsoc2%2F21972-312_SOC_NonCPA.png&w=128&q=75)](https://www.aicpa.org/soc4so)\\n\\n(C) 2026 ScrapeGraphAI, Inc. All rights reserved.\\n\\n[Privacy Policy](https://scrapegraphai.com/privacy)[Terms of Service](https://scrapegraphai.com/terms)\\n']... (truncated)\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'strip'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Run the application graph\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m action_name, results, state = app.run(\n\u001b[32m      3\u001b[39m     halt_after=[\u001b[33m\"ask_question\"\u001b[39m],\n\u001b[32m      4\u001b[39m     inputs={\n\u001b[32m      5\u001b[39m         \u001b[33m\"webpage_url\"\u001b[39m: \u001b[33m\"https://scrapegraphai.com/\"\u001b[39m,\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/telemetry.py:276\u001b[39m, in \u001b[36mcapture_function_usage.<locals>.wrapped_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    273\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(call_fn)\n\u001b[32m    274\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapped_fn\u001b[39m(*args, **kwargs):\n\u001b[32m    275\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mcall_fn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    277\u001b[39m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    278\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m is_telemetry_enabled():\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:685\u001b[39m, in \u001b[36m_call_execute_method_pre_post.__call__.<locals>.wrapper_sync\u001b[39m\u001b[34m(app_self, *args, **kwargs)\u001b[39m\n\u001b[32m    683\u001b[39m exc = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    684\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m685\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mapp_self\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    686\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    687\u001b[39m     \u001b[38;5;28mself\u001b[39m.call_post(app_self, exc)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:1287\u001b[39m, in \u001b[36mApplication.run\u001b[39m\u001b[34m(self, halt_before, halt_after, inputs)\u001b[39m\n\u001b[32m   1285\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m   1286\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1287\u001b[39m         \u001b[30;43mnext\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mgen\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1288\u001b[39m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m   1289\u001b[39m         result = e.value\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:1225\u001b[39m, in \u001b[36mApplication.iterate\u001b[39m\u001b[34m(self, halt_before, halt_after, inputs)\u001b[39m\n\u001b[32m   1222\u001b[39m prior_action: Optional[Action] = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1223\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m.has_next_action():\n\u001b[32m   1224\u001b[39m     \u001b[38;5;66;03m# self.step will only return None if there is no next action, so we can rely on tuple unpacking\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1225\u001b[39m     prior_action, result, state = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mstep\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43minputs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43minputs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1226\u001b[39m     \u001b[38;5;28;01myield\u001b[39;00m prior_action, result, state\n\u001b[32m   1227\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._should_halt_iterate(halt_before, halt_after, prior_action):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:685\u001b[39m, in \u001b[36m_call_execute_method_pre_post.__call__.<locals>.wrapper_sync\u001b[39m\u001b[34m(app_self, *args, **kwargs)\u001b[39m\n\u001b[32m    683\u001b[39m exc = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    684\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m685\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mapp_self\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    686\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    687\u001b[39m     \u001b[38;5;28mself\u001b[39m.call_post(app_self, exc)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:857\u001b[39m, in \u001b[36mApplication.step\u001b[39m\u001b[34m(self, inputs)\u001b[39m\n\u001b[32m    855\u001b[39m \u001b[38;5;28mself\u001b[39m.validate_correct_async_use()\n\u001b[32m    856\u001b[39m \u001b[38;5;28mself\u001b[39m._increment_sequence_id()\n\u001b[32m--> \u001b[39m\u001b[32m857\u001b[39m out = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_step\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43minputs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43minputs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m_run_hooks\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mTrue\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    858\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:914\u001b[39m, in \u001b[36mApplication._step\u001b[39m\u001b[34m(self, inputs, _run_hooks)\u001b[39m\n\u001b[32m    912\u001b[39m     exc = e\n\u001b[32m    913\u001b[39m     logger.exception(_format_BASE_ERROR_MESSAGE(next_action, \u001b[38;5;28mself\u001b[39m._state, inputs))\n\u001b[32m--> \u001b[39m\u001b[32m914\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[32m    915\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    916\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m _run_hooks:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:900\u001b[39m, in \u001b[36mApplication._step\u001b[39m\u001b[34m(self, inputs, _run_hooks)\u001b[39m\n\u001b[32m    898\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    899\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m next_action.single_step:\n\u001b[32m--> \u001b[39m\u001b[32m900\u001b[39m         result, new_state = \u001b[30;43m_run_single_step_action\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    901\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mnext_action\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_state\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43maction_inputs\u001b[39;49m\n\u001b[32m    902\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    903\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    904\u001b[39m         result = _run_function(\n\u001b[32m    905\u001b[39m             next_action, \u001b[38;5;28mself\u001b[39m._state, action_inputs, name=next_action.name\n\u001b[32m    906\u001b[39m         )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/application.py:298\u001b[39m, in \u001b[36m_run_single_step_action\u001b[39m\u001b[34m(action, state, inputs)\u001b[39m\n\u001b[32m    295\u001b[39m \u001b[38;5;66;03m# TODO -- guard all reads/writes with a subset of the state\u001b[39;00m\n\u001b[32m    296\u001b[39m action.validate_inputs(inputs)\n\u001b[32m    297\u001b[39m result, new_state = _adjust_single_step_output(\n\u001b[32m--> \u001b[39m\u001b[32m298\u001b[39m     \u001b[30;43maction\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mrun_and_update\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstate\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43minputs\u001b[39;49m\u001b[30;43m)\u001b[39;49m, action.name, action.schema\n\u001b[32m    299\u001b[39m )\n\u001b[32m    300\u001b[39m _validate_result(result, action.name, action.schema)\n\u001b[32m    301\u001b[39m out = result, _state_update(state, new_state)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/ScrapeGraphAI/py-sdk/scrapegraph-sdk/.venv/lib/python3.12/site-packages/burr/core/action.py:671\u001b[39m, in \u001b[36mFunctionBasedAction.run_and_update\u001b[39m\u001b[34m(self, state, **run_kwargs)\u001b[39m\n\u001b[32m    670\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mrun_and_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, state: State, **run_kwargs) -> \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mdict\u001b[39m, State]:\n\u001b[32m--> \u001b[39m\u001b[32m671\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_fn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstate\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_bound_params\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mrun_kwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 6\u001b[39m, in \u001b[36membed_and_store\u001b[39m\u001b[34m(state, webpage_url)\u001b[39m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m embed_and_store(state: State, webpage_url: str) -> State:\n\u001b[32m      3\u001b[39m     \u001b[33m\"\"\"Embed and store the Markdown content.\"\"\"\u001b[39m\n\u001b[32m      4\u001b[39m     markdown_content = state[\u001b[33m\"markdown_content\"\u001b[39m]\n\u001b[32m      5\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m     chunks = get_text_chunks(markdown_content)\n\u001b[32m      7\u001b[39m \n\u001b[32m      8\u001b[39m     \u001b[38;5;66;03m# Embed and store the chunks using LanceDB\u001b[39;00m\n\u001b[32m      9\u001b[39m     con = lancedb.connect(\u001b[33m\"./webpages\"\u001b[39m)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 19\u001b[39m, in \u001b[36mget_text_chunks\u001b[39m\u001b[34m(text, chunk_token_size)\u001b[39m\n\u001b[32m     15\u001b[39m \n\u001b[32m     16\u001b[39m     Returns:\n\u001b[32m     17\u001b[39m         A list of text chunks.\n\u001b[32m     18\u001b[39m     \"\"\"\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m text.strip():\n\u001b[32m     20\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m []\n\u001b[32m     21\u001b[39m \n\u001b[32m     22\u001b[39m     tokens = tokenizer.encode(text)\n",
+      "\u001b[31mAttributeError\u001b[39m: 'list' object has no attribute 'strip'"
+     ]
+    }
+   ],
+   "source": [
+    "# Run the application graph\n",
+    "action_name, results, state = app.run(\n",
+    "    halt_after=[\"ask_question\"],\n",
+    "    inputs={\n",
+    "        \"webpage_url\": \"https://scrapegraphai.com/\",\n",
+    "        \"user_query\": \"Who are the founders?\"\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "j1Ht3OCdozIY",
+    "outputId": "fc4ff9c3-1675-447e-e8f8-b228731de9fc"
+   },
+   "outputs": [],
+   "source": [
+    "print(state[\"llm_answer\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2as65QLypwdb"
+   },
+   "source": [
+    "### \ud83d\uddbc\ufe0f Visualize the traces with `Burr UI`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "id": "C_amp8mOwcx3"
+   },
+   "outputs": [],
+   "source": [
+    "# execute to load the Burr extension\n",
+    "%load_ext burr.integrations.notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 421
+    },
+    "id": "6mr_3Mp5wgrL",
+    "outputId": "567a1499-346d-416f-c4ca-9045bb7bb26b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"400\"\n",
+       "            src=\"https://o1dw0h9349d-496ff2e9c6d22116-7241-colab.googleusercontent.com/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x796eafb93ac0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%burr_ui\n",
+    "\n",
+    "# If you want to open the ui in another window\n",
+    "# from google.colab import output\n",
+    "# output.serve_kernel_port_as_window(7241)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JyW5EvKB3XgA"
+   },
+   "source": [
+    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+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-1SZT8VzTZNd"
+   },
+   "source": [
+    "## \ud83d\udd17 Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dUi2LtMLRDDR"
+   },
+   "source": [
+    "\n",
+    "<p align=\"left\">\n",
+    "  <img src=\"https://raw.githubusercontent.com/VinciGit00/Scrapegraph-ai/main/docs/assets/api-banner.png\"\n",
+    "       alt=\"ScrapeGraph API Banner\" width=\"600\">\n",
+    "</p>\n",
+    "\n",
+    "\n",
+    "- \ud83d\ude80 **Get your API Key:** [ScrapeGraphAI Dashboard](https://scrapegraphai.com/dashboard)  \n",
+    "- \ud83d\udc19 **GitHub:** [ScrapeGraphAI GitHub](https://github.com/scrapegraphai)  \n",
+    "- \ud83d\udcbc **LinkedIn:** [ScrapeGraphAI LinkedIn](https://www.linkedin.com/company/scrapegraphai/)  \n",
+    "- \ud83d\udc26 **Twitter:** [ScrapeGraphAI Twitter](https://twitter.com/scrapegraphai)  \n",
+    "- \ud83d\udcac **Discord:** [Join our Discord Community](https://discord.gg/uJN7TYcpNa)  \n",
+    "- \u23e9 **Burr:** [Github](https://github.com/DAGWorks-Inc/burr)\n",
+    "- \ud83d\udee2\ufe0f **LanceDB:** [Github](https://github.com/lancedb/lancedb)\n",
+    "\n",
+    "Made with \u2764\ufe0f by the [ScrapeGraphAI](https://scrapegraphai.com) Team  \n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [
+    "IzsyDXEWwPVt",
+    "apBsL-L2KzM7",
+    "i1Ei6gDTdzYW",
+    "BNXvZkkLsOed",
+    "zbP0km8BtN73",
+    "OOnKVmJTvORy"
+   ],
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/cookbook/company-info/scrapegraph_sdk.ipynb b/cookbook/company-info/scrapegraph_sdk.ipynb
index 50ee223..4e7f5bb 100644
--- a/cookbook/company-info/scrapegraph_sdk.ipynb
+++ b/cookbook/company-info/scrapegraph_sdk.ipynb
@@ -6,7 +6,7 @@
     "id": "jEkuKbcRrPcK"
    },
    "source": [
-    "## 🕷️ Extract Company Info with Official Scrapegraph SDK\n"
+    "## 🕷️ Extract Company Info with Official Scrapegraph SDK"
    ]
   },
   {