diff --git a/camel/toolkits/__init__.py b/camel/toolkits/__init__.py
index 30909f9f4d..af7e00ff47 100644
--- a/camel/toolkits/__init__.py
+++ b/camel/toolkits/__init__.py
@@ -45,6 +45,7 @@
 from .stripe_toolkit import StripeToolkit
 from .video_toolkit import VideoDownloaderToolkit
 from .dappier_toolkit import DappierToolkit
+from .semanticscholar_toolkit import SemanticScholarToolkit
 
 __all__ = [
     'BaseToolkit',
@@ -77,4 +78,5 @@
     'MeshyToolkit',
     'OpenBBToolkit',
     'DappierToolkit',
+    'SemanticScholarToolkit',
 ]
diff --git a/camel/toolkits/semanticscholar_toolkit.py b/camel/toolkits/semanticscholar_toolkit.py
new file mode 100644
index 0000000000..627f9393a2
--- /dev/null
+++ b/camel/toolkits/semanticscholar_toolkit.py
@@ -0,0 +1,265 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+import json
+from typing import List
+
+import requests
+
+from camel.toolkits import FunctionTool
+from camel.toolkits.base import BaseToolkit
+
+
+class SemanticScholarToolkit(BaseToolkit):
+    """A toolkit for interacting with the Semantic Scholar
+    API to fetch paper and author data."""
+
+    def __init__(self):
+        """Initializes the SemanticScholarToolkit."""
+        self.base_url = "https://api.semanticscholar.org/graph/v1"
+
+    def fetch_paper_data_title(
+        self,
+        paperTitle: str,
+        fields: str = """title,abstract,authors,year,citationCount,
+        publicationTypes,publicationDate,openAccessPdf""",
+    ) -> dict:
+        r"""Fetches a SINGLE paper from the Semantic Scholar
+        API based on a paper title.
+
+        Args:
+            paperTitle (str): The title of the paper to fetch.
+            fields (str): A comma-separated list of fields to include
+            in the response (default includes title, abstract, authors, year,
+            citation count, publicationTypes,publicationDate,openAccessPdf).
+
+        Returns:
+            dict: The response data from the API or error
+            information if the request fails.
+        """
+        url = f"{self.base_url}/paper/search"
+        query_params = {"query": paperTitle, "fields": fields}
+        try:
+            response = requests.get(url, params=query_params)
+            response.raise_for_status()
+            return response.json()
+        except requests.exceptions.RequestException as e:
+            return {
+                "error": f"Request failed: {e!s}",
+                "message": str(e),
+            }
+        except ValueError:
+            return {
+                "error": "Response is not valid JSON",
+                "message": response.text,
+            }
+
+    def fetch_paper_data_id(
+        self,
+        paperID: str,
+        fields: str = """title,abstract,authors,year,citationCount,
+        publicationTypes,publicationDate,openAccessPdf""",
+    ) -> dict:
+        r"""Fetches a SINGLE paper from the Semantic Scholar
+         API based on a paper ID.
+
+        Args:
+            paperID (str): The ID of the paper to fetch.
+            fields (str): A comma-separated list of fields to
+            include in the response (default includes title, abstract,
+            authors, year, citation count, publicationTypes,
+            publicationDate, openAccessPdf).
+
+        Returns:
+            dict: The response data from the API or error information
+            if the request fails.
+        """
+        url = f"{self.base_url}/paper/{paperID}"
+        query_params = {"fields": fields}
+        try:
+            response = requests.get(url, params=query_params)
+            response.raise_for_status()
+            return response.json()
+        except requests.exceptions.RequestException as e:
+            return {
+                "error": f"Request failed: {e!s}",
+                "message": str(e),
+            }
+        except ValueError:
+            return {
+                "error": "Response is not valid JSON",
+                "message": response.text,
+            }
+
+    def fetch_bulk_paper_data(
+        self,
+        query: str,
+        year: str = "2023-",
+        fields: str = """title,url,publicationTypes,
+        publicationDate,openAccessPdf""",
+    ) -> dict:
+        r"""Fetches MULTIPLE papers at once from the Semantic Scholar
+         API based on a related topic.
+        Args:
+            query (str):
+                The text query to match against the paper's title
+                and abstract.
+                For example, you can use the following operators
+                and techniques to construct your query:
+                Example 1:
+                    ((cloud computing) | virtualization)
+                    +security -privacy This will match papers
+                    whose title or abstract contains "cloud"
+                    and "computing", or contains the word
+                    "virtualization". The papers must also
+                    include the term "security" but exclude
+                    papers that contain the word "privacy".
+            year (str): The year filter for papers (default is "2023-").
+            fields (str): The fields to include in the response
+            (e.g., 'title,url,publicationTypes,publicationDate,
+            openAccessPdf').
+        Returns:
+            dict: The response data from the API or
+            error information if the request fails.
+        """
+        url = f"{self.base_url}/paper/search/bulk"
+        query_params = {"query": query, "fields": fields, "year": year}
+        try:
+            response = requests.get(url, params=query_params)
+            response.raise_for_status()
+            return response.json()
+        except requests.exceptions.RequestException as e:
+            return {
+                "error": f"Request failed: {e!s}",
+                "message": str(e),
+            }
+        except ValueError:
+            return {
+                "error": "Response is not valid JSON",
+                "message": response.text,
+            }
+
+    def fetch_recommended_papers(
+        self,
+        positive_paper_ids: List[str],
+        negative_paper_ids: List[str],
+        fields: str = """title,url,citationCount,authors,
+        publicationTypes,publicationDate,openAccessPdf""",
+        limit: int = 500,
+        save_to_file: bool = False,
+    ) -> dict:
+        r"""Fetches recommended papers from the Semantic Scholar
+        API based on the positive and negative paper IDs.
+
+        Args:
+            positive_paper_ids (list): A list of paper IDs (as strings)
+            that are positively correlated to the recommendation.
+
+            negative_paper_ids (list): A list of paper IDs (as strings)
+            that are negatively correlated to the recommendation.
+
+            fields (str): The fields to include in the response
+            (e.g., 'title,url,citationCount,authors,publicationTypes,
+            publicationDate,openAccessPdf').
+
+            limit (int): The maximum number of recommended papers to return.
+            Default is 500.
+
+            save_to_file (bool): If True, saves the response data to a file
+            (default is False).
+
+        Returns:
+            dict: A dictionary containing recommended papers sorted by
+            citation count.
+        """
+        url = "https://api.semanticscholar.org/recommendations/v1/papers"
+        query_params = {"fields": fields, "limit": str(limit)}
+        data = {
+            "positivePaperIds": positive_paper_ids,
+            "negativePaperIds": negative_paper_ids,
+        }
+        try:
+            response = requests.post(url, params=query_params, json=data)
+            response.raise_for_status()
+            papers = response.json()
+            if save_to_file:
+                with open('recommended_papers.json', 'w') as output:
+                    json.dump(papers, output)
+            return papers
+        except requests.exceptions.RequestException as e:
+            return {"error": str(e)}
+        except ValueError:
+            return {
+                "error": "Response is not valid JSON",
+                "message": response.text,
+            }
+
+    def fetch_author_data(
+        self,
+        ids: List[str],
+        fields: str = "name,url,paperCount,hIndex,papers",
+        save_to_file: bool = False,
+    ) -> dict:
+        r"""Fetches author information from the Semantic Scholar
+        API based on author IDs.
+
+        Args:
+            ids (list): A list of author IDs (as strings) to fetch
+            data for.
+
+            fields (str): A comma-separated list of fields to include
+            in the response (default includes name, URL, paper count,
+            hIndex, and papers).
+
+            save_to_file (bool): If True, saves the response data to
+            a file (default is False).
+
+        Returns:
+            dict: The response data from the API or error information if
+            the request fails.
+        """
+        url = f"{self.base_url}/author/batch"
+        query_params = {"fields": fields}
+        data = {"ids": ids}
+        try:
+            response = requests.post(url, params=query_params, json=data)
+            response.raise_for_status()
+            response_data = response.json()
+            if save_to_file:
+                with open('author_information.json', 'w') as output:
+                    json.dump(response_data, output)
+            return response_data
+        except requests.exceptions.RequestException as e:
+            return {"error": str(e)}
+        except ValueError:
+            return {
+                "error": "Response is not valid JSON",
+                "message": response.text,
+            }
+
+    def get_tools(self) -> List[FunctionTool]:
+        r"""Returns a list of FunctionTool objects representing the
+        functions in the toolkit.
+
+        Returns:
+            List[FunctionTool]: A list of FunctionTool objects
+                representing the functions in the toolkit.
+        """
+        return [
+            FunctionTool(self.fetch_paper_data_title),
+            FunctionTool(self.fetch_paper_data_id),
+            FunctionTool(self.fetch_bulk_paper_data),
+            FunctionTool(self.fetch_recommended_papers),
+            FunctionTool(self.fetch_author_data),
+        ]
diff --git a/examples/toolkits/semanticscholar_toolkit.py b/examples/toolkits/semanticscholar_toolkit.py
new file mode 100644
index 0000000000..94df729827
--- /dev/null
+++ b/examples/toolkits/semanticscholar_toolkit.py
@@ -0,0 +1,353 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from camel.agents import ChatAgent
+from camel.configs import ChatGPTConfig
+from camel.messages import BaseMessage
+from camel.models import ModelFactory
+from camel.toolkits import SemanticScholarToolkit
+from camel.types import ModelPlatformType, ModelType
+
+# Define the model, here in this case we use gpt-4o
+model = ModelFactory.create(
+    model_platform=ModelPlatformType.DEFAULT,
+    model_type=ModelType.DEFAULT,
+    model_config_dict=ChatGPTConfig(temperature=0.0).as_dict(),
+)
+
+
+sys_msg = BaseMessage.make_assistant_message(
+    role_name='Tools calling operator', content='You are a helpful assistant'
+)
+
+
+# Initialize a toolkit
+toolkit = SemanticScholarToolkit()
+# Get list of tools
+tools = toolkit.get_tools()
+
+# Initialize a ChatAgent with your custom tools
+camel_agent = ChatAgent(
+    system_message=sys_msg,
+    model=model,
+    tools=tools,
+)
+
+camel_agent.reset()
+
+
+# Description of the added tools
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User", content="""Describe the tools you've added"""
+)
+
+response = camel_agent.step(usr_msg)
+print(response.msgs[0].content)
+
+'''
+================================================================
+1. **fetch_paper_data_title**: This tool fetches a single paper
+ based on its title. You can specify which fields to include in
+ the response, such as the abstract, authors, year, citation
+ count, and more.
+
+2. **fetch_paper_data_id**: Similar to the previous tool,
+ this one retrieves a single paper but uses a paper ID instead
+ of the title. It also allows for specifying the fields to
+ include in the response.
+
+3. **fetch_bulk_paper_data**: This tool allows you to fetch
+ multiple papers at once based on a query that can include
+ various operators (like AND, OR, NOT). You can filter by
+ year and specify which fields to return.
+
+4. **fetch_recommended_papers**: This tool provides
+ recommendations for papers based on a list of positively
+ and negatively correlated paper IDs. You can specify the
+ fields to include in the response and limit the number
+ of papers returned.
+
+5. **fetch_author_data**: This tool retrieves information
+ about authors based on their IDs. You can specify which
+ fields to include in the response, such as the author's name,
+ URL, paper count, h-index, and their papers.
+
+These tools can be used individually or in combination to
+ gather comprehensive information about academic literature
+ and authors.
+================================================================
+'''
+
+# Search a paper through its id
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""search the paper 'Construction of the Literature
+    Graph in Semantic Scholar' for me including its paperid""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_paper_data_title', 
+args={'paperTitle': 'Construction of the Literature Graph in
+Semantic Scholar', 'fields': 'title,abstract,authors,year,
+citationCount,paperId'}, result={'total': 1, 'offset': 0,
+'data': [{'paperId': '649def34f8be52c8b66281af98ae884c09aef38b',
+'title': 'Construction of the Literature Graph in Semantic
+ Scholar', 'abstract': 'We describe a deployed scalable system
+for organizing published scientific literature into a 
+heterogeneous graph to facilitate algorithmic manipulation and 
+discovery. The resulting literature graph consists of more than
+ 280M nodes, representing papers, authors, entities and various
+ interactions between them (e.g., authorships, citations, 
+ entity mentions). We reduce literature graph construction into
+ familiar NLP tasks (e.g., entity extraction and linking),
+ point out research challenges due to differences from standard
+ formulations of these tasks, and report empirical results for
+ each task. The methods describe
+================================================================
+'''
+
+# Search a paper through its title
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""search the paper with paper id of 
+    '649def34f8be52c8b66281af98ae884c09aef38b' for me""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_paper_data_id', args=
+{'paperID': '649def34f8be52c8b66281af98ae884c09aef38b', 
+'fields': 'title,abstract,authors,year,citationCount,
+publicationTypes,publicationDate,openAccessPdf'}, 
+result={'paperId': '649def34f8be52c8b66281af98ae884c09aef38b',
+'title': 'Construction of the Literature Graph in Semantic
+ Scholar', 'abstract': 'We describe a deployed scalable system
+ for organizing published scientific literature into a
+ heterogeneous graph to facilitate algorithmic manipulation
+ and discovery. The resulting literature graph consists of 
+ more than 280M nodes, representing papers, authors, entities
+ and various interactions between them (e.g., authorships,
+ citations, entity mentions). We reduce literature graph
+ construction into familiar NLP tasks (e.g., entity extraction
+ and linking), point out research challenges due to differences
+ from standard formulations of these tasks, and report
+ empirical results for each task. The methods described
+ in this paper ar
+================================================================
+'''
+
+# Search papers thourgh related topic
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""search 10 papers with topic related to
+    'generative ai' from 2024 for me""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_bulk_paper_data', 
+args={'query': 'generative ai', 'year': '2024-', 'fields':
+'title,url,publicationTypes,publicationDate,openAccessPdf'},
+result={'total': 9849, 'token': 'PCOA3RZZB2ADADAEYCX2BLJJRDEGL
+PUCFA3I5XJAKEAB3YXPGDOTY2GU3WHI4ZMALUMAPUDPHP724CEUVEFKTYRZY5K
+LUU53Y5MWWEINIKYZZRC3YT3H4AF7CTSQ', 'data': [{'paperId': 
+'0008cd09c0449451b9e6e6de35c29009f0883cd9', 'url': 'https://www
+.semanticscholar.org/paper/0008cd09c0449451b9e6e6de35c29009
+f0883cd9', 'title': 'A Chitchat on Using ChatGPT for Cheating',
+ 'openAccessPdf': {'url': 'https://doi.org/10.34074/proc.240106'
+ , 'status': 'BRONZE'}, 'publicationTypes': ['Conference'], 
+ 'publicationDate': '2024-07-24'}, {'paperId': '0013aecf813400
+ 174158e4f012918c5408f90962', 'url': 'https://www.semanticsc
+ holar.org/paper/0013aecf813400174158e4f012918c5408f90962', 
+ 'title': 'Can novice teachers detect AI-generated texts in EFL
+ writing?', 'openAccessPdf': None, 'publicationTypes':
+ ['JournalArticle'], 'publicationDate'
+================================================================
+'''
+
+# Search papers through realted topic and operator
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""search 20 papers with topic related to
+    'ai and bio' from 2024 for me""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_bulk_paper_data', 
+args={'query': 'ai and bio', 'year': '2024-', 'fields': 'title,
+url,publicationTypes,publicationDate,openAccessPdf'}, result=
+{'total': 207, 'token': None, 'data': [{'paperId': '00c8477a9c
+c28b85e4f6da13d2a889c94a955291', 'url': 'https://www.semantics
+cholar.org/paper/00c8477a9cc28b85e4f6da13d2a889c94a955291', 
+'title': 'Explaining Enterprise Knowledge Graphs with Large
+ Language Models and Ontological Reasoning', 'openAccessPdf': 
+ None, 'publicationTypes': ['JournalArticle'], 'publicationDate
+ ': None}, {'paperId': '01726fbfc8ee716c82b9c4cd70696906d3a4
+ 46d0', 'url': 'https://www.semanticscholar.org/paper/01726fbfc
+ 8ee716c82b9c4cd70696906d3a446d0', 'title': 'Study Research 
+ Protocol for Phenome India-CSIR Health Cohort Knowledgebase
+ (PI-CHeCK): A Prospective multi-modal follow-up study on a 
+ nationwide employee cohort.', 'openAccessPdf': {'url': 
+ 'https://www.medrxiv.org/content/medrxiv/early/2024/10/19/2024
+ .10.17.24315252.full.pdf', 'status'
+================================================================
+'''
+
+# Recommand papers thorugh positive and negative paper id
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""recommend 20 papers with positive paper id
+    of "02138d6d094d1e7511c157f0b1a3dd4e5b20ebee",
+    "018f58247a20ec6b3256fd3119f57980a6f37748" and negative
+    paper id of "0045ad0c1e14a4d1f4b011c92eb36b8df63d65bc"
+    for me""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_recommended_papers',
+args={'positive_paper_ids': ['02138d6d094d1e7511c157f0b1a3dd4e
+5b20ebee', '018f58247a20ec6b3256fd3119f57980a6f37748'], 'negati
+ve_paper_ids': ['0045ad0c1e14a4d1f4b011c92eb36b8df63d65bc'], 
+'fields': 'title,url,citationCount,authors,publicationTypes,
+publicationDate,openAccessPdf', 'limit': 20, 'save_to_file': F
+alse}, result={'recommendedPapers': [{'paperId': '9cb202a72171
+dc954f8180b42e08da7ab31e16a1', 'url': 'https://www.semanticsc
+holar.org/paper/9cb202a72171dc954f8180b42e08da7ab31e16a1', 'tit
+le': 'Embrace, Don't Avoid: Reimagining Higher Education with
+ Generative Artificial Intelligence', 'citationCount': 0, 'op
+ enAccessPdf': {'url': 'https://heca-analitika.com/jeml/arti
+ cle/download/233/157', 'status': 'HYBRID'}, 'publicationT
+ ypes': ['JournalArticle'], 'publicationDate': '2024-11-2
+ 8', 'authors': [{'authorId': '1659371967', 'name': 'T. R. N
+ oviandy'}, {'authorId': '1657989613', 'name': 'A. Maulan
+ a'}, {'authorId': '146805414', 'name
+================================================================
+'''
+
+# Recommend papers and save the result in a file
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""search the authors of author ids of "2281351310",
+    "2281342663","2300302076","2300141520" for me""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_recommended_papers', 
+args={'positive_paper_ids': ['02138d6d094d1e7511c157f0b1a3dd4e5
+b20ebee', '018f58247a20ec6b3256fd3119f57980a6f37748'], 'negativ
+e_paper_ids': ['0045ad0c1e14a4d1f4b011c92eb36b8df63d65bc'],
+ 'fields': 'title,url,citationCount,authors,publicationTypes,
+ publicationDate,openAccessPdf', 'limit': 20, 'save_to_file': T
+ rue}, result={'recommendedPapers': [{'paperId': '9cb202a7217
+ 1dc954f8180b42e08da7ab31e16a1', 'url': 'https://www.semantics
+ cholar.org/paper/9cb202a72171dc954f8180b42e08da7ab31e16a1', 
+ 'title': 'Embrace, Don't Avoid: Reimagining Higher Education
+ with Generative Artificial Intelligence', 'citationCount':
+ 0, 'openAccessPdf': {'url': 'https://heca-analitika.com/jeml
+ /article/download/233/157', 'status': 'HYBRID'}, 'publication
+ Types': ['JournalArticle'], 'publicationDate': '2024-11-28',
+ 'authors': [{'authorId': '1659371967', 'name': 'T. R. Novia
+ ndy'}, {'authorId': '1657989613', 'name': 'A. Maulana'}, 
+ {'authorId': '146805414', 'name'
+================================================================
+'''
+
+# Search author information through author id
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""recommend 20 papers with positive paper id
+    of "02138d6d094d1e7511c157f0b1a3dd4e5b20ebee", "018f5
+    8247a20ec6b3256fd3119f57980a6f37748" and negative paper
+    id of "0045ad0c1e14a4d1f4b011c92eb36b8df63d65bc" for me,
+    and please save the result in a file.""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_recommended_papers', 
+args={'positive_paper_ids': ['02138d6d094d1e7511c157f0b1a3dd4e5
+b20ebee', '018f58247a20ec6b3256fd3119f57980a6f37748'], 'negat
+ive_paper_ids': ['0045ad0c1e14a4d1f4b011c92eb36b8df63d65bc'],
+ 'fields': 'title,url,citationCount,authors,publicationTypes
+ ,publicationDate,openAccessPdf', 'limit': 20, 'save_to_file
+ ': True}, result={'recommendedPapers': [{'paperId': '9cb20
+ 2a72171dc954f8180b42e08da7ab31e16a1', 'url': 'https://www.se
+ manticscholar.org/paper/9cb202a72171dc954f8180b42e08da7ab31e
+ 16a1', 'title': 'Embrace, Don't Avoid: Reimagining Higher 
+ Education with Generative Artificial Intelligence', 'citat
+ ionCount': 0, 'openAccessPdf': {'url': 'https://heca-anali
+ tika.com/jeml/article/download/233/157', 'status': 'HYBR
+ ID'}, 'publicationTypes': ['JournalArticle'], 'publicatio
+ nDate': '2024-11-28', 'authors': [{'authorId': '165937196
+ 7', 'name': 'T. R. Noviandy'}, {'authorId': '1657989613',
+ 'name': 'A. Maulana'}, {'authorId': '146805414', 'name'
+================================================================
+'''
+
+# Search author information and save the result in a file
+usr_msg = BaseMessage.make_user_message(
+    role_name="CAMEL User",
+    content="""search the authors of author ids of "2281351310"
+    ,"2281342663","2300302076","2300141520" for me, and please
+    save the record in a file.""",
+)
+
+response = camel_agent.step(usr_msg)
+print(str(response.info['tool_calls'])[:1000])
+
+'''
+================================================================
+[FunctionCallingRecord(func_name='fetch_author_data', args=
+{'ids': ['2281351310', '2281342663', '2300302076', '230014152
+0'], 'fields': 'name,url,paperCount,hIndex,papers', 'save_to_
+file': True}, result=[{'authorId': '2281351310', 'url': 'ht
+tps://www.semanticscholar.org/author/2281351310', 'name': 'Tho
+mas K. F. Chiu', 'paperCount': 3, 'hIndex': 1, 'papers': [{'p
+aperId': '218b2e3d3418edff705336a6e0c7f2125be7c562', 'title': N
+one}, {'paperId': '630642b7040a0c396967e4dab93cf73094fa4f8f
+', 'title': None}, {'paperId': '833ff07d2d1be9be7b12e88487d5631
+c141a2e95', 'title': None}]}, {'authorId': '2281342663', 'ur
+l': 'https://www.semanticscholar.org/author/2281342663', 'nam
+e': 'C. Chai', 'paperCount': 6, 'hIndex': 2, 'papers': [{'pape
+rId': '0c70ca68c0239895b0d36abf7f11302cdcf01855', 'title': Non
+e}, {'paperId': '218b2e3d3418edff705336a6e0c7f2125be7c562', 't
+itle': None}, {'paperId': '7ce699e1cfb81cecf298df6be8eaac8f50
+2e0fcc', 'title': None}, {'paperId': '4521b51a8465e69d20a3ae4
+b770cf164a180f67b', 'ti
+================================================================
+'''
diff --git a/test/toolkits/test_semantic_functions.py b/test/toolkits/test_semantic_functions.py
new file mode 100644
index 0000000000..8af11c5efc
--- /dev/null
+++ b/test/toolkits/test_semantic_functions.py
@@ -0,0 +1,276 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import unittest
+from unittest.mock import MagicMock, patch
+
+from camel.toolkits.semanticscholar_toolkit import SemanticScholarToolkit
+
+
+class TestSemanticScholarToolkit(unittest.TestCase):
+    def setUp(self):
+        """
+        Executed before each test, initialize the toolkit instance.
+        """
+        self.toolkit = SemanticScholarToolkit()
+
+    @patch("requests.get")
+    def test_fetch_paper_data_title_success(self, mock_get):
+        """
+        Test fetch_paper_data_title returning 200 successfully.
+        """
+        mock_response_data = {"data": "some paper data"}
+        # Configure the mock to return the response object
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = mock_response_data
+        mock_get.return_value = mock_response
+
+        paper_title = "A Test Paper"
+        response = self.toolkit.fetch_paper_data_title(paper_title)
+
+        # Verify the call details of requests.get
+        mock_get.assert_called_once()
+        called_url = mock_get.call_args[0][0]  # The first argument is the URL
+        called_params = mock_get.call_args[1]["params"]
+        self.assertIn("paper/search", called_url)
+        self.assertEqual(called_params["query"], paper_title)
+
+        # Verify the returned result
+        self.assertEqual(response, mock_response_data)
+
+    @patch("requests.get")
+    def test_fetch_paper_data_title_error(self, mock_get):
+        """
+        Test fetch_paper_data_title returning a non-200 status.
+        """
+        mock_response = MagicMock()
+        mock_response.status_code = 404
+        mock_response.text = "Not Found"
+        mock_response.json.return_value = {
+            "error": "Request failed with status code 404",
+            "message": "Not Found",
+        }
+        mock_get.return_value = mock_response
+
+        paper_title = "Nonexistent Paper"
+        response = self.toolkit.fetch_paper_data_title(paper_title)
+
+        self.assertIn("error", response)
+        self.assertIn("Request failed", response["error"])
+        self.assertEqual(response["message"], "Not Found")
+
+    @patch("requests.get")
+    def test_fetch_paper_data_id_success(self, mock_get):
+        """
+        Test fetch_paper_data_id returning 200 successfully.
+        """
+        mock_response_data = {"title": "Paper Title by ID"}
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = mock_response_data
+        mock_get.return_value = mock_response
+
+        paper_id = "abcdef123456"
+        response = self.toolkit.fetch_paper_data_id(paper_id)
+        mock_get.assert_called_once()
+        self.assertEqual(response, mock_response_data)
+
+        # Get the URL used in the call for confirmation
+        called_url = mock_get.call_args[0][0]
+        self.assertIn(paper_id, called_url)
+
+    @patch("requests.get")
+    def test_fetch_paper_data_id_failure(self, mock_get):
+        """
+        Test fetch_paper_data_id returning a non-200 status.
+        """
+        mock_response = MagicMock()
+        mock_response.status_code = 500
+        mock_response.text = "Internal Server Error"
+        mock_response.json.return_value = {
+            "error": "Request failed with status code 500",
+            "message": "Internal Server Error",
+        }
+        mock_get.return_value = mock_response
+
+        paper_id = "xyz789"
+        response = self.toolkit.fetch_paper_data_id(paper_id)
+
+        self.assertIn("error", response)
+        self.assertIn("500", response["error"])
+        self.assertEqual(response["message"], "Internal Server Error")
+
+    @patch("requests.get")
+    def test_fetch_bulk_paper_data_success(self, mock_get):
+        """
+        Test fetch_bulk_paper_data returning 200 successfully.
+        """
+        mock_response_data = {"data": ["paper1", "paper2"]}
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = mock_response_data
+        mock_get.return_value = mock_response
+
+        query_str = "cloud computing"
+        response = self.toolkit.fetch_bulk_paper_data(query_str)
+
+        mock_get.assert_called_once()
+        self.assertEqual(response, mock_response_data)
+
+        # Check the parameters
+        called_url = mock_get.call_args[0][0]
+        called_params = mock_get.call_args[1]["params"]
+        self.assertIn("bulk", called_url)
+        self.assertEqual(called_params["query"], query_str)
+
+    @patch("requests.get")
+    def test_fetch_bulk_paper_data_failure(self, mock_get):
+        """
+        Test fetch_bulk_paper_data returning a non-200 status.
+        """
+        mock_response = MagicMock()
+        mock_response.status_code = 403
+        mock_response.text = "Forbidden"
+        mock_response.json.return_value = {
+            "error": "Request failed with status code 403",
+            "message": "Forbidden",
+        }
+        mock_get.return_value = mock_response
+
+        query_str = "quantum computing"
+        response = self.toolkit.fetch_bulk_paper_data(query_str)
+
+        self.assertIn("error", response)
+        self.assertIn("403", response["error"])
+        self.assertEqual(response["message"], "Forbidden")
+
+    @patch("requests.post")
+    def test_fetch_recommended_papers_success(self, mock_post):
+        """
+        Test fetch_recommended_papers returning 200 successfully.
+        """
+        mock_response_data = {"papers": [{"id": "123"}, {"id": "456"}]}
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = mock_response_data
+        mock_post.return_value = mock_response
+
+        pos_ids = ["111", "222"]
+        neg_ids = ["333"]
+        result = self.toolkit.fetch_recommended_papers(
+            positive_paper_ids=pos_ids,
+            negative_paper_ids=neg_ids,
+            save_to_file=False,
+        )
+
+        mock_post.assert_called_once()
+        # Validate the request body, URL, and parameters
+        called_url = mock_post.call_args[0][0]
+        self.assertIn("recommendations/v1/papers", called_url)
+
+        called_json = mock_post.call_args[1]["json"]
+        self.assertEqual(called_json["positivePaperIds"], pos_ids)
+        self.assertEqual(called_json["negativePaperIds"], neg_ids)
+
+        self.assertEqual(result, mock_response_data)
+
+    @patch("requests.post")
+    def test_fetch_recommended_papers_failure(self, mock_post):
+        """
+        Test fetch_recommended_papers returning a non-200 status.
+        """
+        mock_response = MagicMock()
+        mock_response.status_code = 400
+        mock_response.text = "Bad Request"
+        mock_response.json.return_value = {
+            "error": "Request failed with status code 400",
+            "message": "Bad Request",
+        }
+        mock_post.return_value = mock_response
+
+        result = self.toolkit.fetch_recommended_papers(
+            positive_paper_ids=["p1"], negative_paper_ids=["p2"]
+        )
+
+        self.assertIn("error", result)
+        self.assertIn("400", result["error"])
+
+    @patch("requests.post")
+    def test_fetch_author_data_success(self, mock_post):
+        """
+        Test fetch_author_data returning 200 successfully.
+        """
+        mock_response_data = {
+            "data": [
+                {"authorId": "A1", "name": "Author One"},
+                {"authorId": "A2", "name": "Author Two"},
+            ]
+        }
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = mock_response_data
+        mock_post.return_value = mock_response
+
+        author_ids = ["A1", "A2"]
+        result = self.toolkit.fetch_author_data(author_ids, save_to_file=False)
+        mock_post.assert_called_once()
+        self.assertEqual(result, mock_response_data)
+
+        # Check if JSON body includes the correct IDs
+        called_json = mock_post.call_args[1]["json"]
+        self.assertEqual(called_json["ids"], author_ids)
+
+    @patch("requests.post")
+    def test_fetch_author_data_failure(self, mock_post):
+        """
+        Test fetch_author_data returning a non-200 status.
+        """
+        mock_response = MagicMock()
+        mock_response.status_code = 404
+        mock_response.text = "Not Found"
+        mock_response.json.return_value = {
+            "error": "Request failed with status code 404",
+            "message": "Not Found",
+        }
+
+        # Let raise_for_status throw an HTTPError to trigger the except branch
+        from requests.exceptions import HTTPError
+
+        mock_response.raise_for_status.side_effect = HTTPError(
+            "404 Client Error"
+        )
+
+        mock_post.return_value = mock_response
+        result = self.toolkit.fetch_author_data(["A999"])
+
+        self.assertIn("error", result)
+        self.assertIn("404 Client Error", result["error"])
+
+    def test_get_tools(self):
+        """
+        Test whether get_tools returns the correct number of tool functions
+        and references.
+        """
+        tools = self.toolkit.get_tools()
+        self.assertEqual(len(tools), 5)
+        # Simply assert whether the callable of each tool matches our methods
+        self.assertEqual(tools[0].func, self.toolkit.fetch_paper_data_title)
+        self.assertEqual(tools[1].func, self.toolkit.fetch_paper_data_id)
+        self.assertEqual(tools[2].func, self.toolkit.fetch_bulk_paper_data)
+        self.assertEqual(tools[3].func, self.toolkit.fetch_recommended_papers)
+        self.assertEqual(tools[4].func, self.toolkit.fetch_author_data)
+
+
+if __name__ == "__main__":
+    unittest.main()