diff --git a/examples/anthropic/csv_scraper_graph_multi_haiku.py b/examples/anthropic/csv_scraper_graph_multi_haiku.py
new file mode 100644
index 00000000..b833af01
--- /dev/null
+++ b/examples/anthropic/csv_scraper_graph_multi_haiku.py
@@ -0,0 +1,55 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "claude-3-haiku-20240307",
+        "max_tokens": 4000},
+}
+
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/anthropic/json_scraper_multi_haiku.py b/examples/anthropic/json_scraper_multi_haiku.py
new file mode 100644
index 00000000..0327673b
--- /dev/null
+++ b/examples/anthropic/json_scraper_multi_haiku.py
@@ -0,0 +1,36 @@
+"""
+Module for showing how JSONScraperMultiGraph multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import JSONScraperMultiGraph
+
+load_dotenv()
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "claude-3-haiku-20240307",
+        "max_tokens": 4000
+        },
+}
+
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/anthropic/pdf_scraper_graph_haiku.py b/examples/anthropic/pdf_scraper_graph_haiku.py
index cf7e8326..10080b0f 100644
--- a/examples/anthropic/pdf_scraper_graph_haiku.py
+++ b/examples/anthropic/pdf_scraper_graph_haiku.py
@@ -1,10 +1,12 @@
+""" 
+Module for showing how PDFScraper multi works
+"""
 import os, json
 from dotenv import load_dotenv
 from scrapegraphai.graphs import PDFScraperGraph
 
 load_dotenv()
 
-
 # ************************************************
 # Define the configuration for the graph
 # ************************************************
diff --git a/examples/anthropic/pdf_scraper_multi_haiku.py b/examples/anthropic/pdf_scraper_multi_haiku.py
new file mode 100644
index 00000000..974dd2f8
--- /dev/null
+++ b/examples/anthropic/pdf_scraper_multi_haiku.py
@@ -0,0 +1,72 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+load_dotenv()
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "claude-3-haiku-20240307",
+        "max_tokens": 4000
+        },
+}
+
+# ***************
+# Covert to list
+# ***************
+
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/anthropic/xml_scraper_graph_multi_haiku.py b/examples/anthropic/xml_scraper_graph_multi_haiku.py
new file mode 100644
index 00000000..6b79f709
--- /dev/null
+++ b/examples/anthropic/xml_scraper_graph_multi_haiku.py
@@ -0,0 +1,55 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "claude-3-haiku-20240307",
+        "max_tokens": 4000},
+}
+
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/bedrock/csv_scraper_graph_multi_bedrock.py b/examples/bedrock/csv_scraper_graph_multi_bedrock.py
new file mode 100644
index 00000000..c776c508
--- /dev/null
+++ b/examples/bedrock/csv_scraper_graph_multi_bedrock.py
@@ -0,0 +1,59 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "client": "client_name",
+        "model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+        "temperature": 0.0
+    },
+    "embeddings": {
+        "model": "bedrock/cohere.embed-multilingual-v3"
+    }
+}
+
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/bedrock/xml_scraper_graph_multi_bedrock.py b/examples/bedrock/xml_scraper_graph_multi_bedrock.py
new file mode 100644
index 00000000..a0ed3560
--- /dev/null
+++ b/examples/bedrock/xml_scraper_graph_multi_bedrock.py
@@ -0,0 +1,59 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "client": "client_name",
+        "model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+        "temperature": 0.0
+    },
+    "embeddings": {
+        "model": "bedrock/cohere.embed-multilingual-v3"
+    }
+}
+
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/deepseek/csv_scraper_graph_multi_deepseek.py b/examples/deepseek/csv_scraper_graph_multi_deepseek.py
new file mode 100644
index 00000000..ea5e9154
--- /dev/null
+++ b/examples/deepseek/csv_scraper_graph_multi_deepseek.py
@@ -0,0 +1,58 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+deepseek_key = os.getenv("DEEPSEEK_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "deepseek-chat",
+        "openai_api_key": deepseek_key,
+        "openai_api_base": 'https://api.deepseek.com/v1',
+    },
+    "verbose": True,
+}
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/deepseek/json_scraper_multi_deepseek.py b/examples/deepseek/json_scraper_multi_deepseek.py
new file mode 100644
index 00000000..b957dde0
--- /dev/null
+++ b/examples/deepseek/json_scraper_multi_deepseek.py
@@ -0,0 +1,38 @@
+"""
+Module for showing how JSONScraperMultiGraph multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import JSONScraperMultiGraph
+
+load_dotenv()
+
+deepseek_key = os.getenv("DEEPSEEK_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "deepseek-chat",
+        "openai_api_key": deepseek_key,
+        "openai_api_base": 'https://api.deepseek.com/v1',
+    },
+    "verbose": True,
+}
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/deepseek/pdf_scraper_multi_deepseek.py b/examples/deepseek/pdf_scraper_multi_deepseek.py
new file mode 100644
index 00000000..211e4635
--- /dev/null
+++ b/examples/deepseek/pdf_scraper_multi_deepseek.py
@@ -0,0 +1,75 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+load_dotenv()
+
+deepseek_key = os.getenv("DEEPSEEK_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "deepseek-chat",
+        "openai_api_key": deepseek_key,
+        "openai_api_base": 'https://api.deepseek.com/v1',
+    },
+    "verbose": True,
+}
+
+# ***************
+# Covert to list
+# ***************
+
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/deepseek/xml_scraper_graph_multi_deepseek.py b/examples/deepseek/xml_scraper_graph_multi_deepseek.py
new file mode 100644
index 00000000..0f53a6b2
--- /dev/null
+++ b/examples/deepseek/xml_scraper_graph_multi_deepseek.py
@@ -0,0 +1,58 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+deepseek_key = os.getenv("DEEPSEEK_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "deepseek-chat",
+        "openai_api_key": deepseek_key,
+        "openai_api_base": 'https://api.deepseek.com/v1',
+    },
+    "verbose": True,
+}
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/gemini/csv_scraper_graph_multi_gemini.py b/examples/gemini/csv_scraper_graph_multi_gemini.py
new file mode 100644
index 00000000..bfe1b19a
--- /dev/null
+++ b/examples/gemini/csv_scraper_graph_multi_gemini.py
@@ -0,0 +1,57 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+gemini_key = os.getenv("GOOGLE_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": gemini_key,
+        "model": "gemini-pro",
+    },
+}
+
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/gemini/json_scraper_multi_gemini.py b/examples/gemini/json_scraper_multi_gemini.py
new file mode 100644
index 00000000..e914109b
--- /dev/null
+++ b/examples/gemini/json_scraper_multi_gemini.py
@@ -0,0 +1,38 @@
+"""
+Module for showing how JSONScraperMultiGraph multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import JSONScraperMultiGraph
+
+load_dotenv()
+
+gemini_key = os.getenv("GOOGLE_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": gemini_key,
+        "model": "gemini-pro",
+    },
+    "library": "beautifulsoup"
+}
+
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/gemini/pdf_scraper_multi_gemini.py b/examples/gemini/pdf_scraper_multi_gemini.py
new file mode 100644
index 00000000..66afbef2
--- /dev/null
+++ b/examples/gemini/pdf_scraper_multi_gemini.py
@@ -0,0 +1,74 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+load_dotenv()
+
+gemini_key = os.getenv("GOOGLE_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": gemini_key,
+        "model": "gemini-pro",
+    },
+    "library": "beautifulsoup"
+}
+
+# ***************
+# Covert to list
+# ***************
+
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/gemini/xml_scraper_graph_multi_gemini.py b/examples/gemini/xml_scraper_graph_multi_gemini.py
new file mode 100644
index 00000000..e0d979b7
--- /dev/null
+++ b/examples/gemini/xml_scraper_graph_multi_gemini.py
@@ -0,0 +1,57 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+gemini_key = os.getenv("GOOGLE_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": gemini_key,
+        "model": "gemini-pro",
+    },
+}
+
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/groq/csv_scraper_graph_multi_groq.py b/examples/groq/csv_scraper_graph_multi_groq.py
new file mode 100644
index 00000000..475b8cac
--- /dev/null
+++ b/examples/groq/csv_scraper_graph_multi_groq.py
@@ -0,0 +1,59 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+groq_key = os.getenv("GROQ_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "groq/gemma-7b-it",
+        "api_key": groq_key,
+        "temperature": 0
+    },
+    "headless": False
+}
+
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/groq/json_scraper_multi_groq.py b/examples/groq/json_scraper_multi_groq.py
new file mode 100644
index 00000000..df3b9276
--- /dev/null
+++ b/examples/groq/json_scraper_multi_groq.py
@@ -0,0 +1,38 @@
+"""
+Module for showing how JSONScraperMultiGraph multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import JSONScraperMultiGraph
+
+load_dotenv()
+
+groq_key = os.getenv("GROQ_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "groq/gemma-7b-it",
+        "api_key": groq_key,
+        "temperature": 0
+    },
+    "library": "beautifulsoup"
+}
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/groq/pdf_scraper_multi_groq.py b/examples/groq/pdf_scraper_multi_groq.py
new file mode 100644
index 00000000..c43a7087
--- /dev/null
+++ b/examples/groq/pdf_scraper_multi_groq.py
@@ -0,0 +1,74 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+load_dotenv()
+groq_key = os.getenv("GROQ_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "groq/gemma-7b-it",
+        "api_key": groq_key,
+        "temperature": 0
+    },
+    "library": "beautifulsoup"
+}
+
+# ***************
+# Covert to list
+# ***************
+
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/groq/xml_scraper_graph_multi_groq.py b/examples/groq/xml_scraper_graph_multi_groq.py
new file mode 100644
index 00000000..62540671
--- /dev/null
+++ b/examples/groq/xml_scraper_graph_multi_groq.py
@@ -0,0 +1,60 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+groq_key = os.getenv("GROQ_APIKEY")
+
+graph_config = {
+    "llm": {
+        "model": "groq/gemma-7b-it",
+        "api_key": groq_key,
+        "temperature": 0
+    },
+    "headless": False
+}
+
+
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/huggingfacehub/json_scraper_multi_huggingfacehub.py b/examples/huggingfacehub/json_scraper_multi_huggingfacehub.py
new file mode 100644
index 00000000..8ca3ba51
--- /dev/null
+++ b/examples/huggingfacehub/json_scraper_multi_huggingfacehub.py
@@ -0,0 +1,46 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import JSONScraperMultiGraph
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+
+load_dotenv()
+
+HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+
+repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
+
+llm_model_instance = HuggingFaceEndpoint(
+    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
+)
+
+embedder_model_instance = HuggingFaceInferenceAPIEmbeddings(
+    api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2"
+)
+
+graph_config = {
+    "llm": {"model_instance": llm_model_instance},
+    "embeddings": {"model_instance": embedder_model_instance}
+}
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/huggingfacehub/pdf_scraper_multi_huggingfacehub.py b/examples/huggingfacehub/pdf_scraper_multi_huggingfacehub.py
new file mode 100644
index 00000000..d24d522c
--- /dev/null
+++ b/examples/huggingfacehub/pdf_scraper_multi_huggingfacehub.py
@@ -0,0 +1,79 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import PdfScraperMultiGraph
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+load_dotenv()
+
+HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+
+repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
+
+llm_model_instance = HuggingFaceEndpoint(
+    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
+)
+
+embedder_model_instance = HuggingFaceInferenceAPIEmbeddings(
+    api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2"
+)
+
+graph_config = {
+    "llm": {"model_instance": llm_model_instance},
+    "embeddings": {"model_instance": embedder_model_instance}
+}
+
+# Covert to list
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/local_models/csv_scraper_graph_multi_ollama.py b/examples/local_models/csv_scraper_graph_multi_ollama.py
new file mode 100644
index 00000000..fb6bce51
--- /dev/null
+++ b/examples/local_models/csv_scraper_graph_multi_ollama.py
@@ -0,0 +1,62 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+
+import os
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        "format": "json",  # Ollama needs the format to be specified explicitly
+        # "model_tokens": 2000, # set context length arbitrarily
+        "base_url": "http://localhost:11434",
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+        "base_url": "http://localhost:11434",
+    },
+    "verbose": True,
+}
+
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/local_models/json_scraper_multi_ollama.py b/examples/local_models/json_scraper_multi_ollama.py
new file mode 100644
index 00000000..91f4fab4
--- /dev/null
+++ b/examples/local_models/json_scraper_multi_ollama.py
@@ -0,0 +1,39 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from scrapegraphai.graphs import JSONScraperMultiGraph
+
+graph_config = {
+    "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        "format": "json",  # Ollama needs the format to be specified explicitly
+        "model_tokens": 4000,
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+    },
+    "verbose": True,
+    "headless": False,
+}
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/local_models/pdf_scraper_multi_ollama.py b/examples/local_models/pdf_scraper_multi_ollama.py
new file mode 100644
index 00000000..c0b65a63
--- /dev/null
+++ b/examples/local_models/pdf_scraper_multi_ollama.py
@@ -0,0 +1,71 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import json
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+graph_config = {
+    "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        "format": "json",  # Ollama needs the format to be specified explicitly
+        "model_tokens": 4000,
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+    },
+    "verbose": True,
+}
+
+# Covert to list
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/local_models/xml_scraper_graph_multi_ollama.py b/examples/local_models/xml_scraper_graph_multi_ollama.py
new file mode 100644
index 00000000..d84c6c9f
--- /dev/null
+++ b/examples/local_models/xml_scraper_graph_multi_ollama.py
@@ -0,0 +1,62 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        "format": "json",  # Ollama needs the format to be specified explicitly
+        # "model_tokens": 2000, # set context length arbitrarily
+        "base_url": "http://localhost:11434",
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+        "base_url": "http://localhost:11434",
+    },
+    "verbose": True,
+}
+
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/oneapi/csv_scraper_graph_multi_oneapi.py b/examples/oneapi/csv_scraper_graph_multi_oneapi.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/oneapi/json_scraper_multi_oneapi..py b/examples/oneapi/json_scraper_multi_oneapi..py
new file mode 100644
index 00000000..5dc365aa
--- /dev/null
+++ b/examples/oneapi/json_scraper_multi_oneapi..py
@@ -0,0 +1,32 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from scrapegraphai.graphs import JSONScraperMultiGraph
+
+graph_config = {
+    "llm": {
+        "api_key": "***************************",
+        "model": "oneapi/qwen-turbo",
+        "base_url": "http://127.0.0.1:3000/v1",  # 设置 OneAPI URL
+    }
+}
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/oneapi/json_scraper_oneapi.py b/examples/oneapi/json_scraper_oneapi.py
index 5f182594..87c7ea3c 100644
--- a/examples/oneapi/json_scraper_oneapi.py
+++ b/examples/oneapi/json_scraper_oneapi.py
@@ -3,10 +3,8 @@ Basic example of scraping pipeline using JSONScraperGraph from JSON documents
 """
 
 import os
-from dotenv import load_dotenv
 from scrapegraphai.graphs import JSONScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
-load_dotenv()
 
 # ************************************************
 # Read the JSON file
@@ -23,8 +21,6 @@ with open(file_path, 'r', encoding="utf-8") as file:
 # Define the configuration for the graph
 # ************************************************
 
-openai_key = os.getenv("OPENAI_APIKEY")
-
 graph_config = {
     "llm": {
         "api_key": "***************************",
diff --git a/examples/oneapi/pdf_scraper_multi_oneapi.py b/examples/oneapi/pdf_scraper_multi_oneapi.py
new file mode 100644
index 00000000..8b6c57a1
--- /dev/null
+++ b/examples/oneapi/pdf_scraper_multi_oneapi.py
@@ -0,0 +1,70 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+load_dotenv()
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-3.5-turbo",
+    },
+}
+
+# Covert to list
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/oneapi/xml_scraper_graph_multi_oneapi.py b/examples/oneapi/xml_scraper_graph_multi_oneapi.py
new file mode 100644
index 00000000..564c2a3a
--- /dev/null
+++ b/examples/oneapi/xml_scraper_graph_multi_oneapi.py
@@ -0,0 +1,57 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-3.5-turbo",
+    },
+}
+
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/oneapi/xml_scraper_oneapi.py b/examples/oneapi/xml_scraper_oneapi.py
index 5be5716e..15862052 100644
--- a/examples/oneapi/xml_scraper_oneapi.py
+++ b/examples/oneapi/xml_scraper_oneapi.py
@@ -23,7 +23,7 @@ with open(file_path, 'r', encoding="utf-8") as file:
 # Define the configuration for the graph
 # ************************************************
 
-openai_key = os.getenv("OPENAI_APIKEY")
+openai_key = os.getenv("ONEAPI_KEY")
 
 graph_config = {
     "llm": {
diff --git a/examples/openai/csv_scraper_graph_multi_openai.py b/examples/openai/csv_scraper_graph_multi_openai.py
new file mode 100644
index 00000000..890765df
--- /dev/null
+++ b/examples/openai/csv_scraper_graph_multi_openai.py
@@ -0,0 +1,56 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "api_key": "***************************",
+        "model": "oneapi/qwen-turbo",
+        "base_url": "http://127.0.0.1:3000/v1",  # 设置 OneAPI URL
+    }
+}
+
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/examples/openai/deep_scraper_openai.py b/examples/openai/deep_scraper_openai.py
index 6a2e1347..4860a31f 100644
--- a/examples/openai/deep_scraper_openai.py
+++ b/examples/openai/deep_scraper_openai.py
@@ -9,7 +9,6 @@ from scrapegraphai.utils import prettify_exec_info
 
 load_dotenv()
 
-
 # ************************************************
 # Define the configuration for the graph
 # ************************************************
diff --git a/examples/openai/json_scraper_multi_openai.py b/examples/openai/json_scraper_multi_openai.py
new file mode 100644
index 00000000..5f3d9fc2
--- /dev/null
+++ b/examples/openai/json_scraper_multi_openai.py
@@ -0,0 +1,37 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import JSONScraperMultiGraph
+
+load_dotenv()
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-3.5-turbo",
+    },
+}
+
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+sources = [text, text]
+
+multiple_search_graph = JSONScraperMultiGraph(
+    prompt= "List me all the authors, title and genres of the books",
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/openai/pdf_scraper_multi_openai.py b/examples/openai/pdf_scraper_multi_openai.py
new file mode 100644
index 00000000..8b6c57a1
--- /dev/null
+++ b/examples/openai/pdf_scraper_multi_openai.py
@@ -0,0 +1,70 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+load_dotenv()
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-3.5-turbo",
+    },
+}
+
+# Covert to list
+sources = [
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+    "This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity – largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.",
+]
+
+prompt = """
+You are an expert in reviewing academic manuscripts. Please analyze the abstracts provided from an academic journal article to extract and clearly identify the following elements:
+
+Independent Variable (IV): The variable that is manipulated or considered as the primary cause affecting other variables.
+Dependent Variable (DV): The variable that is measured or observed, which is expected to change as a result of variations in the Independent Variable.
+Exogenous Shock: Identify any external or unexpected events used in the study that serve as a natural experiment or provide a unique setting for observing the effects on the IV and DV.
+Response Format: For each abstract, present your response in the following structured format:
+
+Independent Variable (IV):
+Dependent Variable (DV):
+Exogenous Shock:
+
+Example Queries and Responses:
+
+Query: This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather the interaction between call center architecture and outdoor weather conditions in order to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity largely through workers converting more calls into sales, and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
+
+Response:
+
+Independent Variable (IV): Employee happiness.
+Dependent Variable (DV): Overall firm productivity.
+Exogenous Shock: Sudden company-wide increase in bonus payments.
+
+Query: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
+
+Response:
+
+Independent Variable (IV): Exposure to social media.
+Dependent Variable (DV): Mental health outcomes.
+Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
+"""
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
+
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))
diff --git a/examples/openai/smart_scraper_multi_openai.py b/examples/openai/smart_scraper_multi_openai.py
index ddfc6239..504e00a8 100644
--- a/examples/openai/smart_scraper_multi_openai.py
+++ b/examples/openai/smart_scraper_multi_openai.py
@@ -2,7 +2,8 @@
 Basic example of scraping pipeline using SmartScraper
 """
 
-import os, json
+import os
+import json
 from dotenv import load_dotenv
 from scrapegraphai.graphs import SmartScraperMultiGraph
 
diff --git a/examples/openai/xml_scraper_graph_multi_ollama.py b/examples/openai/xml_scraper_graph_multi_ollama.py
new file mode 100644
index 00000000..e0edfaef
--- /dev/null
+++ b/examples/openai/xml_scraper_graph_multi_ollama.py
@@ -0,0 +1,57 @@
+"""
+Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import XMLScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the XML file
+# ************************************************
+
+FILE_NAME = "inputs/books.xml"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "api_key": "***************************",
+        "model": "oneapi/qwen-turbo",
+        "base_url": "http://127.0.0.1:3000/v1",  # 设置 OneAPI URL
+    }
+}
+
+
+# ************************************************
+# Create the XMLScraperMultiGraph instance and run it
+# ************************************************
+
+xml_scraper_graph = XMLScraperMultiGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=[text, text],  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = xml_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = xml_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")
diff --git a/scrapegraphai/graphs/__init__.py b/scrapegraphai/graphs/__init__.py
index 994b2e3a..29f001fa 100644
--- a/scrapegraphai/graphs/__init__.py
+++ b/scrapegraphai/graphs/__init__.py
@@ -16,3 +16,7 @@ from .pdf_scraper_graph import PDFScraperGraph
 from .omni_scraper_graph import OmniScraperGraph
 from .omni_search_graph import OmniSearchGraph
 from .smart_scraper_multi_graph import SmartScraperMultiGraph
+from .pdf_scraper_multi import PdfScraperMultiGraph
+from .json_scraper_multi import JSONScraperMultiGraph
+from .csv_scraper_graph_multi import CSVScraperMultiGraph
+from .xml_scraper_graph_multi import XMLScraperMultiGraph
diff --git a/scrapegraphai/graphs/csv_scraper_graph_multi.py b/scrapegraphai/graphs/csv_scraper_graph_multi.py
new file mode 100644
index 00000000..85ed1727
--- /dev/null
+++ b/scrapegraphai/graphs/csv_scraper_graph_multi.py
@@ -0,0 +1,116 @@
+""" 
+CSVScraperMultiGraph Module
+"""
+
+from copy import copy, deepcopy
+from typing import List, Optional
+
+from .base_graph import BaseGraph
+from .abstract_graph import AbstractGraph
+from .csv_scraper_graph import CSVScraperGraph
+
+from ..nodes import (
+    GraphIteratorNode,
+    MergeAnswersNode
+)
+
+
+class CSVScraperMultiGraph(AbstractGraph):
+    """ 
+    CSVScraperMultiGraph is a scraping pipeline that scrapes a list of URLs and generates answers to a given prompt.
+    It only requires a user prompt and a list of URLs.
+
+    Attributes:
+        prompt (str): The user prompt to search the internet.
+        llm_model (dict): The configuration for the language model.
+        embedder_model (dict): The configuration for the embedder model.
+        headless (bool): A flag to run the browser in headless mode.
+        verbose (bool): A flag to display the execution information.
+        model_token (int): The token limit for the language model.
+
+    Args:
+        prompt (str): The user prompt to search the internet.
+        source (List[str]): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (Optional[str]): The schema for the graph output.
+
+    Example:
+        >>> search_graph = MultipleSearchGraph(
+        ...     "What is Chioggia famous for?",
+        ...     {"llm": {"model": "gpt-3.5-turbo"}}
+        ... )
+        >>> result = search_graph.run()
+    """
+
+    def __init__(self, prompt: str, source: List[str], config: dict, schema: Optional[str] = None):
+
+        self.max_results = config.get("max_results", 3)
+
+        if all(isinstance(value, str) for value in config.values()):
+            self.copy_config = copy(config)
+        else:
+            self.copy_config = deepcopy(config)
+
+        super().__init__(prompt, config, source, schema)
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping and searching.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping and searching workflow.
+        """
+
+        # ************************************************
+        # Create a SmartScraperGraph instance
+        # ************************************************
+
+        smart_scraper_instance = CSVScraperGraph(
+            prompt="",
+            source="",
+            config=self.copy_config,
+        )
+
+        # ************************************************
+        # Define the graph nodes
+        # ************************************************
+
+        graph_iterator_node = GraphIteratorNode(
+            input="user_prompt & jsons",
+            output=["results"],
+            node_config={
+                "graph_instance": smart_scraper_instance,
+            }
+        )
+
+        merge_answers_node = MergeAnswersNode(
+            input="user_prompt & results",
+            output=["answer"],
+            node_config={
+                "llm_model": self.llm_model,
+                "schema": self.schema
+            }
+        )
+
+        return BaseGraph(
+            nodes=[
+                graph_iterator_node,
+                merge_answers_node,
+            ],
+            edges=[
+                (graph_iterator_node, merge_answers_node),
+            ],
+            entry_point=graph_iterator_node
+        )
+
+    def run(self) -> str:
+        """
+        Executes the web scraping and searching process.
+
+        Returns:
+            str: The answer to the prompt.
+        """
+        inputs = {"user_prompt": self.prompt, "jsons": self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("answer", "No answer found.")
diff --git a/scrapegraphai/graphs/json_scraper_multi.py b/scrapegraphai/graphs/json_scraper_multi.py
new file mode 100644
index 00000000..2010c856
--- /dev/null
+++ b/scrapegraphai/graphs/json_scraper_multi.py
@@ -0,0 +1,116 @@
+""" 
+JSONScraperMultiGraph Module
+"""
+
+from copy import copy, deepcopy
+from typing import List, Optional
+
+from .base_graph import BaseGraph
+from .abstract_graph import AbstractGraph
+from .json_scraper_graph import JSONScraperGraph
+
+from ..nodes import (
+    GraphIteratorNode,
+    MergeAnswersNode
+)
+
+
+class JSONScraperMultiGraph(AbstractGraph):
+    """ 
+    JSONScraperMultiGraph is a scraping pipeline that scrapes a list of URLs and generates answers to a given prompt.
+    It only requires a user prompt and a list of URLs.
+
+    Attributes:
+        prompt (str): The user prompt to search the internet.
+        llm_model (dict): The configuration for the language model.
+        embedder_model (dict): The configuration for the embedder model.
+        headless (bool): A flag to run the browser in headless mode.
+        verbose (bool): A flag to display the execution information.
+        model_token (int): The token limit for the language model.
+
+    Args:
+        prompt (str): The user prompt to search the internet.
+        source (List[str]): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (Optional[str]): The schema for the graph output.
+
+    Example:
+        >>> search_graph = MultipleSearchGraph(
+        ...     "What is Chioggia famous for?",
+        ...     {"llm": {"model": "gpt-3.5-turbo"}}
+        ... )
+        >>> result = search_graph.run()
+    """
+
+    def __init__(self, prompt: str, source: List[str], config: dict, schema: Optional[str] = None):
+
+        self.max_results = config.get("max_results", 3)
+
+        if all(isinstance(value, str) for value in config.values()):
+            self.copy_config = copy(config)
+        else:
+            self.copy_config = deepcopy(config)
+
+        super().__init__(prompt, config, source, schema)
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping and searching.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping and searching workflow.
+        """
+
+        # ************************************************
+        # Create a SmartScraperGraph instance
+        # ************************************************
+
+        smart_scraper_instance = JSONScraperGraph(
+            prompt="",
+            source="",
+            config=self.copy_config,
+        )
+
+        # ************************************************
+        # Define the graph nodes
+        # ************************************************
+
+        graph_iterator_node = GraphIteratorNode(
+            input="user_prompt & jsons",
+            output=["results"],
+            node_config={
+                "graph_instance": smart_scraper_instance,
+            }
+        )
+
+        merge_answers_node = MergeAnswersNode(
+            input="user_prompt & results",
+            output=["answer"],
+            node_config={
+                "llm_model": self.llm_model,
+                "schema": self.schema
+            }
+        )
+
+        return BaseGraph(
+            nodes=[
+                graph_iterator_node,
+                merge_answers_node,
+            ],
+            edges=[
+                (graph_iterator_node, merge_answers_node),
+            ],
+            entry_point=graph_iterator_node
+        )
+
+    def run(self) -> str:
+        """
+        Executes the web scraping and searching process.
+
+        Returns:
+            str: The answer to the prompt.
+        """
+        inputs = {"user_prompt": self.prompt, "jsons": self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("answer", "No answer found.")
diff --git a/scrapegraphai/graphs/pdf_scraper_graph.py b/scrapegraphai/graphs/pdf_scraper_graph.py
index 10556213..6afa13de 100644
--- a/scrapegraphai/graphs/pdf_scraper_graph.py
+++ b/scrapegraphai/graphs/pdf_scraper_graph.py
@@ -1,3 +1,4 @@
+
 """
 PDFScraperGraph Module
 """
@@ -63,8 +64,9 @@ class PDFScraperGraph(AbstractGraph):
             input='pdf | pdf_dir',
             output=["doc"],
         )
+
         rag_node = RAGNode(
-            input="user_prompt & doc",
+            input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
             node_config={
                 "llm_model": self.llm_model,
diff --git a/scrapegraphai/graphs/pdf_scraper_multi.py b/scrapegraphai/graphs/pdf_scraper_multi.py
new file mode 100644
index 00000000..125d70a0
--- /dev/null
+++ b/scrapegraphai/graphs/pdf_scraper_multi.py
@@ -0,0 +1,117 @@
+""" 
+PdfScraperMultiGraph Module
+"""
+
+from copy import copy, deepcopy
+from typing import List, Optional
+
+from .base_graph import BaseGraph
+from .abstract_graph import AbstractGraph
+from .pdf_scraper_graph import PDFScraperGraph
+
+from ..nodes import (
+    GraphIteratorNode,
+    MergeAnswersNode
+)
+
+
+class PdfScraperMultiGraph(AbstractGraph):
+    """ 
+    PdfScraperMultiGraph is a scraping pipeline that scrapes a 
+    list of URLs and generates answers to a given prompt.
+    It only requires a user prompt and a list of URLs.
+
+    Attributes:
+        prompt (str): The user prompt to search the internet.
+        llm_model (dict): The configuration for the language model.
+        embedder_model (dict): The configuration for the embedder model.
+        headless (bool): A flag to run the browser in headless mode.
+        verbose (bool): A flag to display the execution information.
+        model_token (int): The token limit for the language model.
+
+    Args:
+        prompt (str): The user prompt to search the internet.
+        source (List[str]): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (Optional[str]): The schema for the graph output.
+
+    Example:
+        >>> search_graph = MultipleSearchGraph(
+        ...     "What is Chioggia famous for?",
+        ...     {"llm": {"model": "gpt-3.5-turbo"}}
+        ... )
+        >>> result = search_graph.run()
+    """
+
+    def __init__(self, prompt: str, source: List[str], config: dict, schema: Optional[str] = None):
+
+        self.max_results = config.get("max_results", 3)
+
+        if all(isinstance(value, str) for value in config.values()):
+            self.copy_config = copy(config)
+        else:
+            self.copy_config = deepcopy(config)
+
+        super().__init__(prompt, config, source, schema)
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping and searching.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping and searching workflow.
+        """
+
+        # ************************************************
+        # Create a PDFScraperGraph instance
+        # ************************************************
+
+        pdf_scraper_instance = PDFScraperGraph(
+            prompt="",
+            source="",
+            config=self.copy_config,
+        )
+
+        # ************************************************
+        # Define the graph nodes
+        # ************************************************
+
+        graph_iterator_node = GraphIteratorNode(
+            input="user_prompt & pdfs",
+            output=["results"],
+            node_config={
+                "graph_instance": pdf_scraper_instance,
+            }
+        )
+
+        merge_answers_node = MergeAnswersNode(
+            input="user_prompt & results",
+            output=["answer"],
+            node_config={
+                "llm_model": self.llm_model,
+                "schema": self.schema
+            }
+        )
+
+        return BaseGraph(
+            nodes=[
+                graph_iterator_node,
+                merge_answers_node,
+            ],
+            edges=[
+                (graph_iterator_node, merge_answers_node),
+            ],
+            entry_point=graph_iterator_node
+        )
+
+    def run(self) -> str:
+        """
+        Executes the web scraping and searching process.
+
+        Returns:
+            str: The answer to the prompt.
+        """
+        inputs = {"user_prompt": self.prompt, "pdfs": self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("answer", "No answer found.")
diff --git a/scrapegraphai/graphs/smart_scraper_graph.py b/scrapegraphai/graphs/smart_scraper_graph.py
index ee230695..aadd0887 100644
--- a/scrapegraphai/graphs/smart_scraper_graph.py
+++ b/scrapegraphai/graphs/smart_scraper_graph.py
@@ -117,4 +117,4 @@ class SmartScraperGraph(AbstractGraph):
         inputs = {"user_prompt": self.prompt, self.input_key: self.source}
         self.final_state, self.execution_info = self.graph.execute(inputs)
 
-        return self.final_state.get("answer", "No answer found.")
\ No newline at end of file
+        return self.final_state.get("answer", "No answer found.")
diff --git a/scrapegraphai/graphs/xml_scraper_graph_multi.py b/scrapegraphai/graphs/xml_scraper_graph_multi.py
new file mode 100644
index 00000000..1198f580
--- /dev/null
+++ b/scrapegraphai/graphs/xml_scraper_graph_multi.py
@@ -0,0 +1,117 @@
+""" 
+XMLScraperMultiGraph Module
+"""
+
+from copy import copy, deepcopy
+from typing import List, Optional
+
+from .base_graph import BaseGraph
+from .abstract_graph import AbstractGraph
+from .xml_scraper_graph import XMLScraperGraph
+
+from ..nodes import (
+    GraphIteratorNode,
+    MergeAnswersNode
+)
+
+
+class XMLScraperMultiGraph(AbstractGraph):
+    """ 
+    XMLScraperMultiGraph is a scraping pipeline that scrapes a list of URLs and 
+    generates answers to a given prompt.
+    It only requires a user prompt and a list of URLs.
+
+    Attributes:
+        prompt (str): The user prompt to search the internet.
+        llm_model (dict): The configuration for the language model.
+        embedder_model (dict): The configuration for the embedder model.
+        headless (bool): A flag to run the browser in headless mode.
+        verbose (bool): A flag to display the execution information.
+        model_token (int): The token limit for the language model.
+
+    Args:
+        prompt (str): The user prompt to search the internet.
+        source (List[str]): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (Optional[str]): The schema for the graph output.
+
+    Example:
+        >>> search_graph = MultipleSearchGraph(
+        ...     "What is Chioggia famous for?",
+        ...     {"llm": {"model": "gpt-3.5-turbo"}}
+        ... )
+        >>> result = search_graph.run()
+    """
+
+    def __init__(self, prompt: str, source: List[str], config: dict, schema: Optional[str] = None):
+
+        self.max_results = config.get("max_results", 3)
+
+        if all(isinstance(value, str) for value in config.values()):
+            self.copy_config = copy(config)
+        else:
+            self.copy_config = deepcopy(config)
+
+        super().__init__(prompt, config, source, schema)
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping and searching.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping and searching workflow.
+        """
+
+        # ************************************************
+        # Create a SmartScraperGraph instance
+        # ************************************************
+
+        smart_scraper_instance = XMLScraperGraph(
+            prompt="",
+            source="",
+            config=self.copy_config,
+        )
+
+        # ************************************************
+        # Define the graph nodes
+        # ************************************************
+
+        graph_iterator_node = GraphIteratorNode(
+            input="user_prompt & jsons",
+            output=["results"],
+            node_config={
+                "graph_instance": smart_scraper_instance,
+            }
+        )
+
+        merge_answers_node = MergeAnswersNode(
+            input="user_prompt & results",
+            output=["answer"],
+            node_config={
+                "llm_model": self.llm_model,
+                "schema": self.schema
+            }
+        )
+
+        return BaseGraph(
+            nodes=[
+                graph_iterator_node,
+                merge_answers_node,
+            ],
+            edges=[
+                (graph_iterator_node, merge_answers_node),
+            ],
+            entry_point=graph_iterator_node
+        )
+
+    def run(self) -> str:
+        """
+        Executes the web scraping and searching process.
+
+        Returns:
+            str: The answer to the prompt.
+        """
+        inputs = {"user_prompt": self.prompt, "jsons": self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("answer", "No answer found.")
diff --git a/scrapegraphai/nodes/__init__.py b/scrapegraphai/nodes/__init__.py
index 4577ee86..5c54937c 100644
--- a/scrapegraphai/nodes/__init__.py
+++ b/scrapegraphai/nodes/__init__.py
@@ -19,4 +19,4 @@ from .generate_answer_csv_node import GenerateAnswerCSVNode
 from .generate_answer_pdf_node import GenerateAnswerPDFNode
 from .graph_iterator_node import GraphIteratorNode
 from .merge_answers_node import MergeAnswersNode
-from .generate_answer_omni_node import GenerateAnswerOmniNode
\ No newline at end of file
+from .generate_answer_omni_node import GenerateAnswerOmniNode
diff --git a/scrapegraphai/nodes/generate_answer_csv_node.py b/scrapegraphai/nodes/generate_answer_csv_node.py
index c12e0688..4317e969 100644
--- a/scrapegraphai/nodes/generate_answer_csv_node.py
+++ b/scrapegraphai/nodes/generate_answer_csv_node.py
@@ -49,7 +49,7 @@ class GenerateAnswerCSVNode(BaseNode):
         input: str,
         output: List[str],
         node_config: Optional[dict] = None,
-        node_name: str = "GenerateAnswer",
+        node_name: str = "GenerateAnswerCSV",
     ):
         """
         Initializes the GenerateAnswerNodeCsv with a language model client and a node name.
diff --git a/scrapegraphai/nodes/generate_answer_pdf_node.py b/scrapegraphai/nodes/generate_answer_pdf_node.py
index 40ec1889..64dec983 100644
--- a/scrapegraphai/nodes/generate_answer_pdf_node.py
+++ b/scrapegraphai/nodes/generate_answer_pdf_node.py
@@ -48,7 +48,7 @@ class GenerateAnswerPDFNode(BaseNode):
         input: str,
         output: List[str],
         node_config: Optional[dict] = None,
-        node_name: str = "GenerateAnswer",
+        node_name: str = "GenerateAnswerPDF",
     ):
         """
         Initializes the GenerateAnswerNodePDF with a language model client and a node name.
@@ -96,9 +96,7 @@ class GenerateAnswerPDFNode(BaseNode):
         output_parser = JsonOutputParser()
         format_instructions = output_parser.get_format_instructions()
 
-       
         chains_dict = {}
-
         # Use tqdm to add progress bar
         for i, chunk in enumerate(
             tqdm(doc, desc="Processing chunks", disable=not self.verbose)
@@ -108,7 +106,7 @@ class GenerateAnswerPDFNode(BaseNode):
                     template=template_no_chunks_pdf,
                     input_variables=["question"],
                     partial_variables={
-                        "context": chunk.page_content,
+                        "context":chunk,
                         "format_instructions": format_instructions,
                     },
                 )
@@ -117,7 +115,7 @@ class GenerateAnswerPDFNode(BaseNode):
                     template=template_chunks_pdf,
                     input_variables=["question"],
                     partial_variables={
-                        "context": chunk.page_content,
+                        "context":chunk,
                         "chunk_id": i + 1,
                         "format_instructions": format_instructions,
                     },
diff --git a/scrapegraphai/nodes/generate_scraper_node.py b/scrapegraphai/nodes/generate_scraper_node.py
index 205b057b..99d1516a 100644
--- a/scrapegraphai/nodes/generate_scraper_node.py
+++ b/scrapegraphai/nodes/generate_scraper_node.py
@@ -10,7 +10,6 @@ from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnableParallel
 from tqdm import tqdm
-
 from ..utils.logging import get_logger
 
 # Imports from the library
diff --git a/scrapegraphai/nodes/get_probable_tags_node.py b/scrapegraphai/nodes/get_probable_tags_node.py
index a26ded38..f31633c0 100644
--- a/scrapegraphai/nodes/get_probable_tags_node.py
+++ b/scrapegraphai/nodes/get_probable_tags_node.py
@@ -3,10 +3,8 @@ GetProbableTagsNode Module
 """
 
 from typing import List, Optional
-
 from langchain.output_parsers import CommaSeparatedListOutputParser
 from langchain.prompts import PromptTemplate
-
 from ..utils.logging import get_logger
 from .base_node import BaseNode
 
diff --git a/scrapegraphai/nodes/robots_node.py b/scrapegraphai/nodes/robots_node.py
index 2ed7755f..d77c7a08 100644
--- a/scrapegraphai/nodes/robots_node.py
+++ b/scrapegraphai/nodes/robots_node.py
@@ -47,7 +47,7 @@ class RobotsNode(BaseNode):
         input: str,
         output: List[str],
         node_config: Optional[dict] = None,
-        node_name: str = "Robots",
+        node_name: str = "RobotNode",
 
     ):
         super().__init__(node_name, "node", input, output, 1)