diff --git a/docs/mint.json b/docs/mint.json
index 00a9b47f9..5ed0f6d6b 100644
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -3,7 +3,7 @@
     "logo": {
       "light": "/logo/logo.png",
       "dark": "/logo/logo.png",
-      "href": "https://pandas-ai.com"
+      "href": "https://getpanda.ai"
     },
     "favicon": "/favicon.svg",
     "colors": {
@@ -72,9 +72,14 @@
         "pages": ["v3/ai-dashboards", "v3/share-dataframes", "v3/permission-management"],
         "version": "v3"
       },
+      {
+        "group": "Advanced Usage",
+        "pages": ["v3/agent"],
+        "version": "v3"
+      },
       {
         "group": "Backwards Compatibility",
-        "pages": ["v3/agent", "v3/smart-dataframes", "v3/smart-datalakes"],
+        "pages": ["v3/smart-dataframes", "v3/smart-datalakes"],
         "version": "v3"
       },
       {
diff --git a/docs/v3/agent.mdx b/docs/v3/agent.mdx
index 0af4635b2..dee329cec 100644
--- a/docs/v3/agent.mdx
+++ b/docs/v3/agent.mdx
@@ -1,18 +1,18 @@
 ---
-title: 'Agent'
-description: 'Add few-shot learning to your PandaAI agent'
+title: "Agent"
+description: "Add few-shot learning to your PandaAI agent"
 ---
 
 <Note title="Beta Notice">
-Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+  Release v3 is currently in beta. This documentation reflects the features and
+  functionality in progress and may change before the final release.
 </Note>
 
 You can train PandaAI to understand your data better and to improve its performance. Training is as easy as calling the `train` method on the `Agent`.
 
-
 ## Prerequisites
 
-Before you start training PandaAI, you need to set your PandaAI API key. 
+Before you start training PandaAI, you need to set your PandaAI API key.
 You can generate your API key by signing up at [https://app.pandabi.ai](https://app.pandabi.ai).
 
 ```python
@@ -134,6 +134,60 @@ print(response)
 # The model will use the information provided in the training to generate a response
 ```
 
+## Using the Sandbox Environment
+
+To enhance security and protect against malicious code through prompt injection, PandaAI provides a sandbox environment for code execution. The sandbox runs your code in an isolated Docker container, ensuring that potentially harmful operations are contained.
+
+### Installation
+
+Before using the sandbox, you need to install Docker on your machine and ensure it is running.
+
+First, install the sandbox package:
+
+```bash
+pip install pandasai-docker
+```
+
+### Basic Usage
+
+Here's how to use the sandbox with your PandaAI agent:
+
+```python
+from pandasai import Agent
+from pandasai_docker import DockerSandbox
+
+# Initialize the sandbox
+sandbox = DockerSandbox()
+sandbox.start()
+
+# Create an agent with the sandbox
+df = pai.read_csv("data.csv")
+agent = Agent([df], sandbox=sandbox)
+
+# Chat with the agent - code will run in the sandbox
+response = agent.chat("Calculate the average sales")
+
+# Don't forget to stop the sandbox when done
+sandbox.stop()
+```
+
+### Customizing the Sandbox
+
+You can customize the sandbox environment by specifying a custom name and Dockerfile:
+
+```python
+sandbox = DockerSandbox(
+    "custom-sandbox-name",
+    "/path/to/custom/Dockerfile"
+)
+```
+
+<Note>
+  The sandbox works offline and provides an additional layer of security for
+  code execution. It's particularly useful when working with untrusted data or
+  when you need to ensure that code execution is isolated from your main system.
+</Note>
+
 ## Troubleshooting
 
 In some cases, you might get an error like this: `No vector store provided. Please provide a vector store to train the agent`. It means no API key has been generated to use the `BambooVectorStore`.
@@ -153,9 +207,11 @@ vector_store = BambooVectorStor(api_key="YOUR_PANDABI_API_KEY")
 # Instantiate the agent with the custom vector store
 agent = Agent(connector, config={...} vectorstore=vector_store)
 ```
+
 ## Custom Head
 
 In some cases, you might want to provide custom data samples to the conversational agent to improve its understanding and responses. For example, you might want to:
+
 - Provide better examples that represent your data patterns
 - Avoid sharing sensitive information
 - Guide the agent with specific data scenarios
diff --git a/examples/docker_sandbox.ipynb b/examples/docker_sandbox.ipynb
new file mode 100644
index 000000000..881a20f8b
--- /dev/null
+++ b/examples/docker_sandbox.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Execute code in a sandbox\n",
+    "\n",
+    "To enhance security and protect yourself from malicious code through prompt injection, \n",
+    "we make it possible to run code in a sandbox environment.\n",
+    "This notebook explains how to do it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install the package\n",
+    "\n",
+    "First of all you need to install the python package. You can use pip to install it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install pandasai-docker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Execute the code in the sandbox with the agent\n",
+    "\n",
+    "Please keep in mind the sandbox works offline. \n",
+    "Once you have installed the package, you can start the sandbox with the following code.\n",
+    "For the purpose of this example, we are going to use bambooLLM as the LLM chosen for the agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandasai as pai\n",
+    "from pandasai import Agent\n",
+    "from pandasai_docker import DockerSandbox\n",
+    "\n",
+    "pai.set_api_key(\"YOUR_API_KEY\")\n",
+    "\n",
+    "# initialize the sandbox\n",
+    "sandbox = DockerSandbox()\n",
+    "sandbox.start()\n",
+    "\n",
+    "# read a csv as df\n",
+    "df = pai.read_csv(\"./data/heart.csv\")\n",
+    "\n",
+    "# pass the csv and the sandbox to the agent\n",
+    "agent = Agent([df], memory_size=10, sandbox=sandbox)\n",
+    "\n",
+    "# Chat with the Agent\n",
+    "response = agent.chat(\"plot top five artists streams\")\n",
+    "\n",
+    "# stop the sandbox (docker container)\n",
+    "sandbox.stop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customize the sandbox\n",
+    "\n",
+    "You can decide the name and path of your sandbox by passing them as positional arguments in the DockerSandbox()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sandbox = DockerSandbox(\"pandaai-sandbox\", \"/path/to/Dockerfile\")\n",
+    "\n",
+    "# read a csv as df\n",
+    "df = pai.read_csv(\"./data/heart.csv\")\n",
+    "\n",
+    "# pass the csv and the sandbox to the agent\n",
+    "agent = Agent([df], memory_size=10, sandbox=sandbox)\n",
+    "\n",
+    "# Chat with the Agent\n",
+    "response = agent.chat(\"plot top five artists streams\")\n",
+    "\n",
+    "sandbox.stop()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/extensions/sandbox/docker/README.md b/extensions/sandbox/docker/README.md
new file mode 100644
index 000000000..ed753229c
--- /dev/null
+++ b/extensions/sandbox/docker/README.md
@@ -0,0 +1,9 @@
+# Docker Sandbox Extension for PandasAI
+
+## Installation
+
+You can install this extension using poetry:
+
+```bash
+poetry add pandasai-docker
+```
diff --git a/extensions/sandbox/docker/pandasai_docker/Dockerfile b/extensions/sandbox/docker/pandasai_docker/Dockerfile
new file mode 100644
index 000000000..2f39e00ac
--- /dev/null
+++ b/extensions/sandbox/docker/pandasai_docker/Dockerfile
@@ -0,0 +1,12 @@
+FROM python:3.9
+
+LABEL image_name="pandasai-sandbox"
+
+# Install required Python packages
+RUN pip install pandas numpy matplotlib
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Default command keeps the container running (useful for testing or debugging)
+CMD ["sleep", "infinity"]
diff --git a/extensions/sandbox/docker/pandasai_docker/__init__.py b/extensions/sandbox/docker/pandasai_docker/__init__.py
new file mode 100644
index 000000000..70529fff0
--- /dev/null
+++ b/extensions/sandbox/docker/pandasai_docker/__init__.py
@@ -0,0 +1,3 @@
+from .docker_sandbox import DockerSandbox
+
+__all__ = ["DockerSandbox"]
diff --git a/extensions/sandbox/docker/pandasai_docker/docker_sandbox.py b/extensions/sandbox/docker/pandasai_docker/docker_sandbox.py
new file mode 100644
index 000000000..c8bf4641c
--- /dev/null
+++ b/extensions/sandbox/docker/pandasai_docker/docker_sandbox.py
@@ -0,0 +1,211 @@
+import io
+import logging
+import os
+import re
+import subprocess
+import tarfile
+import uuid
+from typing import Optional
+
+import docker
+
+from pandasai.sandbox import Sandbox
+
+from .serializer import ResponseSerializer
+
+logger = logging.getLogger(__name__)
+
+
+class DockerSandbox(Sandbox):
+    def __init__(self, image_name="pandasai-sandbox", dockerfile_path=None):
+        super().__init__()
+        self._dockerfile_path: str = dockerfile_path or os.path.join(
+            os.path.dirname(__file__), "Dockerfile"
+        )
+        self._image_name: str = image_name
+        self._client: docker.DockerClient = docker.from_env()
+        self._container: Optional[docker.models.containers.Container] = None
+
+        # Build the image if it does not exist
+        if not self._image_exists():
+            self._build_image()
+
+        self._helper_code: str = self._read_start_code(
+            os.path.join(os.path.dirname(__file__), "serializer.py")
+        )
+
+    def _image_exists(self) -> bool:
+        try:
+            self._client.images.get(self._image_name)
+            return True
+        except docker.errors.ImageNotFound:
+            return False
+
+    def _build_image(self) -> None:
+        logger.info(
+            f"Building Docker image '{self._image_name}' from '{self._dockerfile_path}'..."
+        )
+        try:
+            subprocess.run(
+                [
+                    "docker",
+                    "build",
+                    "-f",
+                    self._dockerfile_path,
+                    "-t",
+                    self._image_name,
+                    ".",
+                ],
+                check=True,
+                capture_output=True,
+                text=True,
+            )
+        except subprocess.CalledProcessError as e:
+            logger.error(
+                f"Failed to build Docker image '{self._image_name}' with error: {e.stderr}"
+            )
+            raise
+
+    def start(self):
+        if not self._started:
+            logger.info(
+                f"Starting a Docker container from the image '{self._image_name}'"
+            )
+            self._container = self._client.containers.run(
+                self._image_name,
+                command="sleep infinity",
+                network_disabled=True,
+                detach=True,
+                tty=True,
+            )
+            logger.info(
+                f"Started a Docker container with id '{self._container.id}' from the image '{self._image_name}'"
+            )
+            self._started = True
+
+    def stop(self) -> None:
+        if self._started and self._container:
+            logger.info(f"Stopping a Docker container with id '{self._container.id}''")
+            self._container.stop()
+            self._container.remove()
+            self._container = None
+            self._started = False
+
+    def _read_start_code(self, file_path: str) -> str:
+        """Read helper start code from a file as a string.
+
+        Args:
+            file_path (str): Path to the file.
+
+        Returns:
+            str: Code as a string.
+        """
+        with open(file_path, "r") as file:
+            return file.read()
+
+    def _exec_code(self, code: str, environment: dict) -> dict:
+        """Execute Python code in a Docker container.
+
+        Args:
+            code (str): Code to execute.
+            environment (dict): Environment variables to pass to the container.
+
+        Returns:
+            dict: Result of the code execution.
+        """
+        if not self._container:
+            raise RuntimeError("Container is not running.")
+
+        sql_queries = self._extract_sql_queries_from_code(code)
+
+        # Temporary chart storage path
+        chart_path = "/tmp/temp_chart.png"
+        # actual chart path
+        original_chart_path = None
+
+        if png_paths := re.findall(r"'([^']+\.png)'", code):
+            original_chart_path = png_paths[0]
+
+        # update chart path
+        code = re.sub(
+            r"""(['"])([^'"]*\.png)\1""",
+            lambda m: f"{m.group(1)}{chart_path}{m.group(1)}",
+            code,
+        )
+
+        # Execute SQL queries, save the query results to CSV files
+        datasets_map = {}
+        for sql_query in sql_queries:
+            execute_sql_query_func = environment.get("execute_sql_query")
+            if execute_sql_query_func is None:
+                raise RuntimeError(
+                    "execute_sql_query function is not defined in the environment."
+                )
+
+            query_df = execute_sql_query_func(sql_query)
+            filename = f"{uuid.uuid4().hex}.csv"
+            # Pass the files to the container for further processing
+            self.transfer_file(query_df, filename=filename)
+            datasets_map[sql_query] = filename
+
+        # Add the datasets_map variable to the code
+        dataset_map = f"""
+datasets_map = {datasets_map}
+
+def execute_sql_query(sql_query):
+    filename = datasets_map[sql_query]
+    filepath = os.path.join("/tmp", filename)
+    return pd.read_csv(filepath)
+
+"""
+        # serialization code to get output from docker
+        end_code = """
+print(parser.serialize(result))
+"""
+        # Concatenate code and helper code
+        code = self._helper_code + dataset_map + code + end_code
+
+        # Compile the code for errors
+        self._compile_code(code)
+
+        # Replace double quotes with escaped double quotes for command line code arguments
+        code = code.replace('"', '\\"')
+
+        logger.info(f"Submitting code to docker container {code}")
+
+        exit_code, output = self._container.exec_run(
+            cmd=f'python -c "{code}"', demux=True
+        )
+
+        if exit_code != 0:
+            raise RuntimeError(f"Error executing code: {output[1].decode()}")
+
+        response = output[0].decode()
+        return ResponseSerializer.deserialize(response, original_chart_path)
+
+    def transfer_file(self, csv_data, filename="file.csv") -> None:
+        if not self._container:
+            raise RuntimeError("Container is not running.")
+
+        # Convert the DataFrame to a CSV string
+        csv_string = csv_data.to_csv(index=False)
+
+        # Create a tar archive in memory
+        tar_stream = io.BytesIO()
+        with tarfile.open(fileobj=tar_stream, mode="w") as tar:
+            # Add the CSV string as a file in the tar archive
+            csv_bytes = csv_string.encode("utf-8")
+            tarinfo = tarfile.TarInfo(name=filename)
+            tarinfo.size = len(csv_bytes)
+            tar.addfile(tarinfo, io.BytesIO(csv_bytes))
+
+        # Seek to the beginning of the stream
+        tar_stream.seek(0)
+
+        # Transfer the tar archive to the container
+        self._container.put_archive("/tmp", tar_stream)
+
+    def __del__(self) -> None:
+        if self._container:
+            self._container.stop()
+            self._container.remove()
diff --git a/extensions/sandbox/docker/pandasai_docker/serializer.py b/extensions/sandbox/docker/pandasai_docker/serializer.py
new file mode 100644
index 000000000..8ebe4292d
--- /dev/null
+++ b/extensions/sandbox/docker/pandasai_docker/serializer.py
@@ -0,0 +1,73 @@
+import base64
+import datetime
+import json
+import os  # important to import
+import tarfile  # important to import
+from json import JSONEncoder
+
+import numpy as np
+import pandas as pd
+
+
+class ResponseSerializer:
+    @staticmethod
+    def serialize_dataframe(df: pd.DataFrame) -> dict:
+        if df.empty:
+            return {"columns": [], "data": [], "index": []}
+        return df.to_dict(orient="split")
+
+    @staticmethod
+    def serialize(result: dict) -> str:
+        if result["type"] == "dataframe":
+            if isinstance(result["value"], pd.Series):
+                result["value"] = result["value"].to_frame()
+            result["value"] = ResponseSerializer.serialize_dataframe(result["value"])
+
+        elif result["type"] == "plot" and isinstance(result["value"], str):
+            with open(result["value"], "rb") as image_file:
+                image_data = image_file.read()
+            result["value"] = base64.b64encode(image_data).decode()
+
+        return json.dumps(result, cls=CustomEncoder)
+
+    @staticmethod
+    def deserialize(response: str, chart_path: str = None) -> dict:
+        result = json.loads(response)
+        if result["type"] == "dataframe":
+            json_data = result["value"]
+            result["value"] = pd.DataFrame(
+                data=json_data["data"],
+                index=json_data["index"],
+                columns=json_data["columns"],
+            )
+
+        elif result["type"] == "plot" and chart_path:
+            image_data = base64.b64decode(result["value"])
+
+            # Write the binary data to a file
+            with open(chart_path, "wb") as image_file:
+                image_file.write(image_data)
+
+            result["value"] = chart_path
+
+        return result
+
+
+class CustomEncoder(JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, (np.integer, np.int64)):
+            return int(obj)
+
+        if isinstance(obj, (np.floating, np.float64)):
+            return float(obj)
+
+        if isinstance(obj, (pd.Timestamp, datetime.datetime, datetime.date)):
+            return obj.isoformat()
+
+        if isinstance(obj, pd.DataFrame):
+            return ResponseSerializer.serialize_dataframe(obj)
+
+        return super().default(obj)
+
+
+parser = ResponseSerializer()
diff --git a/extensions/sandbox/docker/poetry.lock b/extensions/sandbox/docker/poetry.lock
new file mode 100644
index 000000000..1dcfd35d0
--- /dev/null
+++ b/extensions/sandbox/docker/poetry.lock
@@ -0,0 +1,1912 @@
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
+
+[[package]]
+name = "astor"
+version = "0.8.1"
+description = "Read/rewrite/write Python ASTs"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+files = [
+    {file = "astor-0.8.1-py2.py3-none-any.whl", hash = "sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5"},
+    {file = "astor-0.8.1.tar.gz", hash = "sha256:6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e"},
+]
+
+[[package]]
+name = "certifi"
+version = "2024.12.14"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
+    {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.1"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"},
+    {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"},
+    {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e218488cd232553829be0664c2292d3af2eeeb94b32bea483cf79ac6a694e037"},
+    {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:80ed5e856eb7f30115aaf94e4a08114ccc8813e6ed1b5efa74f9f82e8509858f"},
+    {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b010a7a4fd316c3c484d482922d13044979e78d1861f0e0650423144c616a46a"},
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+zstd = ["zstandard (>=0.18.0)"]
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+[[package]]
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+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
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+]
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+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.8,<3.12"
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diff --git a/extensions/sandbox/docker/pyproject.toml b/extensions/sandbox/docker/pyproject.toml
new file mode 100644
index 000000000..4369f54dd
--- /dev/null
+++ b/extensions/sandbox/docker/pyproject.toml
@@ -0,0 +1,16 @@
+[tool.poetry]
+name = "pandasai-docker"
+version = "0.1.0"
+description = ""
+authors = ["ArslanSaleem <khan.arslan38@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.12"
+pandasai = ">=3.0.0-beta.0"
+numpy = ">=1.23.2,<2.0.0"
+docker = "^7.1.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/extensions/sandbox/docker/tests/test_sandbox.py b/extensions/sandbox/docker/tests/test_sandbox.py
new file mode 100644
index 000000000..e4166b529
--- /dev/null
+++ b/extensions/sandbox/docker/tests/test_sandbox.py
@@ -0,0 +1,263 @@
+import unittest
+from io import BytesIO
+from unittest.mock import MagicMock, mock_open, patch
+
+import pandas as pd
+from docker.errors import ImageNotFound
+from pandasai_docker import DockerSandbox
+
+
+class TestDockerSandbox(unittest.TestCase):
+    def setUp(self):
+        self.image_name = "test_image"
+        self.dfs = [MagicMock()]
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    def test_destructor(self, mock_docker):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_container = mock_client.containers.run.return_value
+        sandbox._container = mock_container
+
+        del sandbox
+        mock_container.stop.assert_called_once()
+        mock_container.remove.assert_called_once()
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    def test_image_exists(self, mock_docker):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_client.images.get.return_value = True
+        self.assertTrue(sandbox._image_exists())
+
+        mock_client.images.get.side_effect = ImageNotFound("Image not found")
+        self.assertFalse(sandbox._image_exists())
+
+    @patch("builtins.open")
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    @patch("pandasai_docker.docker_sandbox.subprocess")
+    def test_build_image(self, mock_subprocess, mock_docker, mock_open):
+        # Create a single BytesIO object to mock the file content
+        mock_file = MagicMock(spec=BytesIO)
+        mock_file.__enter__.return_value = BytesIO(b"FROM python:3.9")
+        mock_file.__exit__.return_value = None
+        mock_open.return_value = mock_file
+
+        # Arrange
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        dockerfile_path = sandbox._dockerfile_path
+        image_name = self.image_name
+
+        # Act
+        sandbox._build_image()
+
+        # Create the expected fileobj (using the same object reference)
+        expected_fileobj = mock_file.__enter__.return_value
+
+        # Assert
+        mock_subprocess.run.assert_called_once()
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    def test_start_and_stop_container(self, mock_docker):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_client.containers = MagicMock()
+        mock_client.containers.run = MagicMock(return_value=MagicMock())
+
+        sandbox.start()
+        mock_client.containers.run.assert_called_once_with(
+            self.image_name,
+            command="sleep infinity",
+            network_disabled=True,
+            detach=True,
+            tty=True,
+        )
+
+        sandbox.stop()
+        self.assertIsNone(sandbox._container)
+
+    def test_extract_sql_queries_from_code(self):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        code = """
+sql_query = 'SELECT COUNT(*) FROM table'
+result = execute_sql_query(sql_query)
+        """
+        queries = sandbox._extract_sql_queries_from_code(code)
+        self.assertEqual(queries, ["SELECT COUNT(*) FROM table"])
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    def test_transfer_file(self, mock_docker):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_container = mock_client.containers.run.return_value
+        sandbox._container = mock_container
+
+        df = pd.DataFrame({"col1": [1, 2, 3], "col2": [4, 5, 6]})
+        sandbox.transfer_file(df, filename="test.csv")
+
+        mock_container.put_archive.assert_called()
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    def test_exec_code(self, mock_docker):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_container = mock_client.containers.run.return_value
+        mock_container.exec_run.return_value = (
+            0,
+            (b'{"type": "number", "value": 42}', b""),
+        )
+        sandbox._container = mock_container
+
+        mock_execute_sql_func = MagicMock()
+        env = {"execute_sql_query": mock_execute_sql_func}
+
+        code = 'result = {"type": "number", "value": 42}'
+        result = sandbox._exec_code(code, env)
+        self.assertEqual(result, {"type": "number", "value": 42})
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    @patch("pandasai_docker.docker_sandbox.DockerSandbox.transfer_file")
+    def test_exec_code_with_sql_queries(self, mock_transfer_file, mock_docker):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_container = mock_client.containers.run.return_value
+        mock_container.exec_run.return_value = (
+            0,
+            (b'{"type": "number", "value": 42}', b""),
+        )
+        sandbox._container = mock_container
+
+        # Mock SQL execution
+        mock_execute_sql_func = MagicMock()
+        env = {"execute_sql_query": mock_execute_sql_func}
+
+        code = """
+sql_query = 'SELECT COUNT(DISTINCT Artist) AS total_artists FROM artists'
+total_artists_df = execute_sql_query(sql_query)
+total_artists = total_artists_df['total_artists'].iloc[0]
+result = {'type': 'number', 'value': total_artists}
+        """
+        result = sandbox._exec_code(code, env)
+        self.assertEqual(result, {"type": "number", "value": 42})
+        mock_execute_sql_func.assert_called_once_with(
+            "SELECT COUNT(DISTINCT Artist) AS total_artists FROM artists"
+        )
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    @patch("pandasai_docker.docker_sandbox.DockerSandbox.transfer_file")
+    def test_exec_code_with_sql_queries_raise_no_env(
+        self, mock_transfer_file, mock_docker
+    ):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_container = mock_client.containers.run.return_value
+        mock_container.exec_run.return_value = (
+            0,
+            (b'{"type": "number", "value": 42}', b""),
+        )
+        sandbox._container = mock_container
+
+        # Mock SQL execution
+        env = {}
+
+        code = """
+sql_query = 'SELECT COUNT(DISTINCT Artist) AS total_artists FROM artists'
+total_artists_df = execute_sql_query(sql_query)
+total_artists = total_artists_df['total_artists'].iloc[0]
+result = {'type': 'number', 'value': total_artists}
+        """
+        with self.assertRaises(RuntimeError):
+            sandbox._exec_code(code, env)
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    @patch("pandasai_docker.docker_sandbox.DockerSandbox.transfer_file")
+    @patch("pandasai_docker.docker_sandbox.ResponseSerializer.deserialize")
+    def test_exec_code_with_sql_queries_with_plot(
+        self, mock_deserialize, mock_transfer_file, mock_docker
+    ):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_container = mock_client.containers.run.return_value
+        mock_container.exec_run.return_value = (
+            0,
+            (b'{"type": "plot", "value": "base64img"}', b""),
+        )
+        sandbox._container = mock_container
+
+        # Mock SQL execution
+        mock_execute_sql_func = MagicMock()
+        env = {"execute_sql_query": mock_execute_sql_func}
+
+        code = """
+import pandas as pd
+import matplotlib.pyplot as plt
+sql_query = \"\"\"
+SELECT Artist, Streams
+FROM table_artists
+ORDER BY CAST(REPLACE(Streams, ',', '') AS FLOAT) DESC
+LIMIT 5
+\"\"\"
+top_artists_df = execute_sql_query(sql_query)
+top_artists_df['Streams'] = top_artists_df['Streams'].str.replace(',', '').astype(float)
+plt.figure(figsize=(10, 6))
+plt.barh(top_artists_df['Artist'], top_artists_df['Streams'], color='skyblue')
+plt.xlabel('Streams (in millions)')
+plt.title('Top Five Artists by Streams')
+plt.gca().invert_yaxis()
+plt.tight_layout()
+plt.savefig('/exports/charts/temp_chart.png')
+result = {'type': 'plot', 'value': '/exports/charts/temp_chart.png'}
+        """
+        result = sandbox._exec_code(code, env)
+
+        assert result is not None
+        mock_deserialize.assert_called_once_with(
+            '{"type": "plot", "value": "base64img"}', "/exports/charts/temp_chart.png"
+        )
+
+    @patch("pandasai_docker.docker_sandbox.docker.from_env")
+    @patch("pandasai_docker.docker_sandbox.DockerSandbox.transfer_file")
+    @patch("pandasai_docker.docker_sandbox.ResponseSerializer.deserialize")
+    def test_exec_code_with_sql_queries_with_dataframe(
+        self, mock_deserialize, mock_transfer_file, mock_docker
+    ):
+        sandbox = DockerSandbox(image_name=self.image_name)
+        mock_client = mock_docker.return_value
+        mock_container = mock_client.containers.run.return_value
+        mock_container.exec_run.return_value = (
+            0,
+            (
+                b'{"type": "dataframe", "value": {"columns": [], "data": [], "index": []}}',
+                b"",
+            ),
+        )
+        sandbox._container = mock_container
+
+        # Mock SQL execution
+        mock_execute_sql_func = MagicMock()
+        env = {"execute_sql_query": mock_execute_sql_func}
+
+        code = """
+import pandas as pd
+import matplotlib.pyplot as plt
+sql_query = \"\"\"
+SELECT Artist, Streams
+FROM table_artists
+ORDER BY CAST(REPLACE(Streams, ',', '') AS FLOAT) DESC
+LIMIT 5
+\"\"\"
+top_artists_df = execute_sql_query(sql_query)
+result = {'type': 'dataframe', 'value': top_artists_df}
+        """
+        result = sandbox._exec_code(code, env)
+
+        assert result is not None
+        mock_deserialize.assert_called_once_with(
+            '{"type": "dataframe", "value": {"columns": [], "data": [], "index": []}}',
+            None,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/extensions/sandbox/docker/tests/test_serializer.py b/extensions/sandbox/docker/tests/test_serializer.py
new file mode 100644
index 000000000..8592a1f7f
--- /dev/null
+++ b/extensions/sandbox/docker/tests/test_serializer.py
@@ -0,0 +1,90 @@
+import base64
+import datetime
+import json
+import os
+import unittest
+from unittest.mock import mock_open, patch
+
+import numpy as np
+import pandas as pd
+from pandasai_docker.serializer import CustomEncoder, ResponseSerializer
+
+
+class TestResponseSerializer(unittest.TestCase):
+    def test_serialize_dataframe_empty(self):
+        df = pd.DataFrame()
+        result = ResponseSerializer.serialize_dataframe(df)
+        self.assertEqual(result, {"columns": [], "data": [], "index": []})
+
+    def test_serialize_dataframe_non_empty(self):
+        df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
+        result = ResponseSerializer.serialize_dataframe(df)
+        expected = {"columns": ["A", "B"], "data": [[1, 3], [2, 4]], "index": [0, 1]}
+        self.assertEqual(result, expected)
+
+    @patch("builtins.open", new_callable=mock_open, read_data=b"image_data")
+    @patch("base64.b64encode", return_value=b"encoded_image")
+    def test_serialize_plot(self, mock_b64encode, mock_open_file):
+        result = {"type": "plot", "value": "path/to/image.png"}
+        serialized = ResponseSerializer.serialize(result)
+        expected = {"type": "plot", "value": "encoded_image"}
+        self.assertEqual(json.loads(serialized), expected)
+        mock_open_file.assert_called_once_with("path/to/image.png", "rb")
+        mock_b64encode.assert_called_once_with(b"image_data")
+
+    def test_serialize_dataframe_type(self):
+        df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
+        result = {"type": "dataframe", "value": df}
+        serialized = ResponseSerializer.serialize(result)
+        deserialized = json.loads(serialized)
+        self.assertEqual(deserialized["type"], "dataframe")
+        self.assertEqual(
+            deserialized["value"], ResponseSerializer.serialize_dataframe(df)
+        )
+
+    def test_deserialize_dataframe(self):
+        response = {
+            "type": "dataframe",
+            "value": {"columns": ["A", "B"], "data": [[1, 3], [2, 4]], "index": [0, 1]},
+        }
+        serialized = json.dumps(response)
+        result = ResponseSerializer.deserialize(serialized)
+        expected_df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
+        pd.testing.assert_frame_equal(result["value"], expected_df)
+
+    @patch("builtins.open", new_callable=mock_open)
+    @patch("base64.b64decode", return_value=b"image_data")
+    def test_deserialize_plot(self, mock_b64decode, mock_open_file):
+        response = {"type": "plot", "value": base64.b64encode(b"image_data").decode()}
+        serialized = json.dumps(response)
+        chart_path = "path/to/output.png"
+        result = ResponseSerializer.deserialize(serialized, chart_path=chart_path)
+        self.assertEqual(result["value"], chart_path)
+        mock_b64decode.assert_called_once_with(response["value"])
+        mock_open_file.assert_called_once_with(chart_path, "wb")
+        mock_open_file().write.assert_called_once_with(b"image_data")
+
+
+class TestCustomEncoder(unittest.TestCase):
+    def test_encode_numpy(self):
+        data = {"int": np.int64(42), "float": np.float64(3.14)}
+        encoded = json.dumps(data, cls=CustomEncoder)
+        self.assertEqual(json.loads(encoded), {"int": 42, "float": 3.14})
+
+    def test_encode_datetime(self):
+        now = datetime.datetime.now()
+        data = {"timestamp": now}
+        encoded = json.dumps(data, cls=CustomEncoder)
+        self.assertEqual(json.loads(encoded), {"timestamp": now.isoformat()})
+
+    def test_encode_dataframe(self):
+        df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
+        data = {"df": df}
+        encoded = json.dumps(data, cls=CustomEncoder)
+        self.assertEqual(
+            json.loads(encoded)["df"], ResponseSerializer.serialize_dataframe(df)
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/pandasai/agent/base.py b/pandasai/agent/base.py
index 6042de963..ddbfc1668 100644
--- a/pandasai/agent/base.py
+++ b/pandasai/agent/base.py
@@ -23,6 +23,7 @@
     InvalidLLMOutputType,
     MissingVectorStoreError,
 )
+from pandasai.sandbox import Sandbox
 from pandasai.vectorstores.vectorstore import VectorStore
 
 from ..config import Config
@@ -44,6 +45,7 @@ def __init__(
         memory_size: Optional[int] = 10,
         vectorstore: Optional[VectorStore] = None,
         description: str = None,
+        sandbox: Sandbox = None,
     ):
         """
         Args:
@@ -69,6 +71,7 @@ def __init__(
 
         self._code_generator = CodeGenerator(self._state)
         self._response_parser = ResponseParser()
+        self._sandbox = sandbox
 
     def chat(self, query: str, output_type: Optional[str] = None):
         """
@@ -105,8 +108,13 @@ def generate_code(self, query: Union[UserQuery, str]) -> str:
     def execute_code(self, code: str) -> dict:
         """Execute the generated code."""
         self._state.logger.log(f"Executing code: {code}")
+
         code_executor = CodeExecutor(self._state.config)
         code_executor.add_to_env("execute_sql_query", self._execute_sql_query)
+
+        if self._sandbox:
+            return self._sandbox.execute(code, code_executor.environment)
+
         return code_executor.execute_and_return_result(code)
 
     def _execute_local_sql_query(self, query: str) -> pd.DataFrame:
diff --git a/pandasai/core/code_execution/code_executor.py b/pandasai/core/code_execution/code_executor.py
index a72545479..b158ea8be 100644
--- a/pandasai/core/code_execution/code_executor.py
+++ b/pandasai/core/code_execution/code_executor.py
@@ -42,3 +42,7 @@ def execute_and_return_result(self, code: str) -> Any:
             raise NoResultFoundError("No result returned")
 
         return self._environment.get("result", None)
+
+    @property
+    def environment(self) -> dict:
+        return self._environment
diff --git a/pandasai/sandbox/__init__.py b/pandasai/sandbox/__init__.py
new file mode 100644
index 000000000..05ec2cc0a
--- /dev/null
+++ b/pandasai/sandbox/__init__.py
@@ -0,0 +1,3 @@
+from .sandbox import Sandbox
+
+__all__ = ["Sandbox"]
diff --git a/pandasai/sandbox/sandbox.py b/pandasai/sandbox/sandbox.py
new file mode 100644
index 000000000..1672c265b
--- /dev/null
+++ b/pandasai/sandbox/sandbox.py
@@ -0,0 +1,82 @@
+import ast
+
+
+class Sandbox:
+    def __init__(self):
+        self._started: bool = False
+
+    def start(self):
+        raise NotImplementedError("The start method must be implemented by subclasses.")
+
+    def stop(self):
+        raise NotImplementedError("The stop method must be implemented by subclasses.")
+
+    def execute(self, code: str, environment: dict) -> dict:
+        if not self._started:
+            self.start()
+            return self._exec_code(code, environment)
+
+        return self._exec_code(code, environment)
+
+    def _exec_code(self, code: str, environment: dict) -> dict:
+        raise NotImplementedError("Subclasses must implement the _exec_code method.")
+
+    def transfer_file(self, csv_data, filename="file.csv"):
+        raise NotImplementedError(
+            "The transfer_file method must be implemented by subclasses."
+        )
+
+    def _extract_sql_queries_from_code(self, code) -> list[str]:
+        """
+        Extract SQL query strings from Python code
+
+        Args:
+            code (str): Python code as a string.
+
+        Returns:
+            list: List of SQL query strings found in the code.
+        """
+        sql_queries = []
+
+        class SQLQueryExtractor(ast.NodeVisitor):
+            def visit_Assign(self, node):
+                # Look for assignments where SQL queries might be defined
+                if isinstance(
+                    node.value, (ast.Str, ast.Constant)
+                ):  # Python 3.8+: ast.Constant
+                    if "SELECT" in node.value.s.upper():
+                        sql_queries.append(node.value.s)
+                self.generic_visit(node)
+
+            def visit_Call(self, node):
+                # Look for function calls where SQL queries might be passed
+                for arg in node.args:
+                    if isinstance(
+                        arg, (ast.Str, ast.Constant)
+                    ):  # Python 3.8+: ast.Constant
+                        if "SELECT" in arg.s.upper():
+                            sql_queries.append(arg.s)
+                self.generic_visit(node)
+
+        # Parse the code into an AST and visit all nodes
+        tree = ast.parse(code)
+        SQLQueryExtractor().visit(tree)
+
+        return sql_queries
+
+    def _compile_code(self, code: str) -> str:
+        """Compile code as a Python module
+
+        Args:
+            code (str): Code as a string to compile.
+
+        Raises:
+            SyntaxError: If the code contains syntax errors.
+
+        Returns:
+            str: Compiled code as a string.
+        """
+        try:
+            return compile(code, "<string>", "exec")
+        except SyntaxError as e:
+            raise SyntaxError(f"Syntax error in code: {e}") from e
diff --git a/tests/unit_tests/sandbox/test_sandbox.py b/tests/unit_tests/sandbox/test_sandbox.py
new file mode 100644
index 000000000..6c3bf65f6
--- /dev/null
+++ b/tests/unit_tests/sandbox/test_sandbox.py
@@ -0,0 +1,103 @@
+import unittest
+from unittest.mock import MagicMock, patch
+
+from pandasai.sandbox import Sandbox
+
+
+class TestSandbox(unittest.TestCase):
+    def setUp(self):
+        class SandboxImpl(Sandbox):
+            def start(self):
+                self._started = True
+
+            def stop(self):
+                self._started = False
+
+            def _exec_code(self, code: str, environment: dict) -> dict:
+                exec_globals = environment.copy()
+                exec(code, exec_globals)
+                return exec_globals
+
+            def transfer_file(self, csv_data, filename):
+                return f"Processed CSV: {csv_data}"
+
+        self.sandbox = SandboxImpl()
+
+    def test_start(self):
+        self.assertFalse(self.sandbox._started)
+        self.sandbox.start()
+        self.assertTrue(self.sandbox._started)
+
+    def test_stop(self):
+        self.sandbox.start()
+        self.assertTrue(self.sandbox._started)
+        self.sandbox.stop()
+        self.assertFalse(self.sandbox._started)
+
+    def test_execute_calls_start_if_not_started(self):
+        code = "a = 10"
+        environment = {}
+        result = self.sandbox.execute(code, environment)
+        self.assertIn("a", result)
+        self.assertEqual(result["a"], 10)
+        self.assertTrue(self.sandbox._started)
+
+    def test_execute_does_not_call_start_if_already_started(self):
+        code = "a = 20"
+        environment = {}
+        self.sandbox.start()
+        with patch.object(
+            self.sandbox, "start", wraps=self.sandbox.start
+        ) as mock_start:
+            result = self.sandbox.execute(code, environment)
+            mock_start.assert_not_called()
+        self.assertIn("a", result)
+        self.assertEqual(result["a"], 20)
+
+    def test_transfer_file(self):
+        result = self.sandbox.transfer_file("sample_data", None)
+        self.assertEqual(result, "Processed CSV: sample_data")
+
+    def test_extract_sql_queries(self):
+        code = """
+query = "SELECT * FROM users"
+def execute_sql_query(sql):
+    return sql
+execute_sql_query("SELECT id FROM orders")
+        """
+        queries = self.sandbox._extract_sql_queries_from_code(code)
+        self.assertEqual(queries, ["SELECT * FROM users", "SELECT id FROM orders"])
+
+    def test_extract_single_sql_queries(self):
+        code = """
+query = "SELECT * FROM users"
+execute_sql_query(query)
+        """
+        queries = self.sandbox._extract_sql_queries_from_code(code)
+        self.assertEqual(queries, ["SELECT * FROM users"])
+
+    def test_compile_code_valid(self):
+        code = "x = 5\ny = 10\nresult = x + y"
+        compiled = self.sandbox._compile_code(code)
+        self.assertIsNotNone(compiled)
+
+    def test_compile_code_invalid(self):
+        code = "x = 5\ny ="
+        with self.assertRaises(SyntaxError) as context:
+            self.sandbox._compile_code(code)
+        self.assertIn("Syntax error in code", str(context.exception))
+
+    def test_not_implemented_methods(self):
+        sandbox_base = Sandbox()
+        with self.assertRaises(NotImplementedError):
+            sandbox_base.start()
+        with self.assertRaises(NotImplementedError):
+            sandbox_base.stop()
+        with self.assertRaises(NotImplementedError):
+            sandbox_base._exec_code("", {})
+        with self.assertRaises(NotImplementedError):
+            sandbox_base.transfer_file("data")
+
+
+if __name__ == "__main__":
+    unittest.main()