diff --git a/crewai_tools/tools/__init__.py b/crewai_tools/tools/__init__.py
index a72fda2..6164efb 100644
--- a/crewai_tools/tools/__init__.py
+++ b/crewai_tools/tools/__init__.py
@@ -37,3 +37,4 @@
 from .youtube_channel_search_tool.youtube_channel_search_tool import YoutubeChannelSearchTool
 from .youtube_video_search_tool.youtube_video_search_tool import YoutubeVideoSearchTool
 from .spider_tool.spider_tool import SpiderTool
+from .apify_tool.apify_tool import ApifyTool
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diff --git a/crewai_tools/tools/apify_tool/README.md b/crewai_tools/tools/apify_tool/README.md
new file mode 100644
index 0000000..96601b4
--- /dev/null
+++ b/crewai_tools/tools/apify_tool/README.md
@@ -0,0 +1,14 @@
+
+The ApifyTool class provides the following methods:
+
+- `run_actor`: Run an Apify actor with the given input.
+- `wait_for_actor_run`: Wait for an Apify actor run to complete.
+- `get_dataset`: Get the dataset associated with the given actor run.
+- `get_dataset_items`: Get all items from the given dataset.
+- `get_key_value_store`: Get the key-value store associated with the given actor run.
+- `download_file`: Download a file from the key-value store of the given actor run.
+- `store_dataset`: Store data in an Apify dataset.
+
+You can use these methods to interact with various Apify services, such as running actors, waiting for actor runs to complete, fetching datasets, accessing the key-value store, downloading files, and storing data in datasets.
+
+Make sure to replace `"your_apify_token"` and `"your_actor_id"` with your actual Apify token and the ID of the actor you want to run, respectively.
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diff --git a/crewai_tools/tools/apify_tool/apify_tool.py b/crewai_tools/tools/apify_tool/apify_tool.py
new file mode 100644
index 0000000..3cdffa4
--- /dev/null
+++ b/crewai_tools/tools/apify_tool/apify_tool.py
@@ -0,0 +1,71 @@
+import typing as t
+from crewai_tools.tools.base_tool import BaseTool
+from apify_client import ApifyClient, ActorRun, Dataset
+
+class ApifyTool(BaseTool):
+    def __init__(self, apify_token: str):
+        self.client = ApifyClient(apify_token)
+
+    def _run(self, query: str) -> str:
+        """
+        Run the Apify tool with the given query.
+        """
+        # Parse the query and determine the appropriate Apify action
+        # For example, you could use natural language processing to extract the intent
+        # and map it to an Apify action
+
+        # Execute the Apify action using the client
+        # For example, to run an actor:
+        actor_run = self.run_actor(actor_id="your_actor_id", run_input={"query": query})
+
+        # Wait for the actor to complete
+        actor_run = self.wait_for_actor_run(actor_run)
+
+        # Return the result
+        return actor_run.output
+
+    def run_actor(self, actor_id: str, run_input: dict) -> ActorRun:
+        """
+        Run an Apify actor with the given input.
+        """
+        return self.client.actor.call_run(actor_id=actor_id, run_input=run_input)
+
+    def wait_for_actor_run(self, actor_run: ActorRun) -> ActorRun:
+        """
+        Wait for an Apify actor run to complete.
+        """
+        while not actor_run.is_finished():
+            actor_run = self.client.actor.get_run(actor_run.id)
+
+        return actor_run
+
+    def get_dataset(self, actor_run: ActorRun, dataset_id: str = None) -> Dataset:
+        """
+        Get the dataset associated with the given actor run.
+        """
+        if dataset_id:
+            return self.client.dataset.get(dataset_id)
+        else:
+            return actor_run.dataset
+
+    def get_dataset_items(self, dataset: Dataset) -> List[Dict[str, Any]]:
+        """
+        Get all items from the given dataset.
+        """
+        return list(dataset.iterate_items())
+
+    def get_key_value_store(self, actor_run: ActorRun) -> Dict[str, Any]:
+        """
+        Get the key-value store associated with the given actor run.
+        """
+        return actor_run.key_value_store
+
+    def download_file(self, actor_run: ActorRun, key: str, file_path: str):
+        """
+        Download a file from the key-value store of the given actor run.
+        """
+        with open(file_path, "wb") as file:
+            file.write(actor_run.key_value_store.get_file(key).read())
+
+    async def _arun(self, query: str) -> str:
+        raise NotImplementedError("This tool does not support async mode.")
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