diff --git a/example.py b/example.py
index 3d78a20c..4d60edf8 100644
--- a/example.py
+++ b/example.py
@@ -9,6 +9,7 @@ agent = Agent(
     streaming_on=True,
     verbose=True,
     stopping_token="<DONE>",
+    interactive=True,
 )
 
 # Run the workflow on a task
diff --git a/playground/agents/devin.py b/playground/agents/devin.py
new file mode 100644
index 00000000..accb4f65
--- /dev/null
+++ b/playground/agents/devin.py
@@ -0,0 +1,9 @@
+"""
+Plan -> act in a loop until observation is met
+
+
+# Tools
+- Terminal
+- Text Editor
+- Browser
+"""
diff --git a/playground/demos/ai_acceleerated_learning/Podgraph .py b/playground/demos/ai_acceleerated_learning/Podgraph .py
new file mode 100644
index 00000000..70944b31
--- /dev/null
+++ b/playground/demos/ai_acceleerated_learning/Podgraph .py	
@@ -0,0 +1,59 @@
+def test_create_graph():
+    """
+    Tests that a graph can be created.
+    """
+    graph = create_graph()
+    assert isinstance(graph, dict)
+
+
+def test_weight_edges():
+    """
+    Tests that the edges of a graph can be weighted.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    for edge in graph.edges:
+        assert isinstance(edge.weight, int)
+
+
+def test_create_user_list():
+    """
+    Tests that a list of all the podcasts that the user has listened to can be created.
+    """
+    user_list = create_user_list()
+    assert isinstance(user_list, list)
+
+
+def test_find_most_similar_podcasts():
+    """
+    Tests that the most similar podcasts to a given podcast can be found.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    user_list = create_user_list()
+    most_similar_podcasts = find_most_similar_podcasts(
+        graph, user_list
+    )
+    assert isinstance(most_similar_podcasts, list)
+
+
+def test_add_most_similar_podcasts():
+    """
+    Tests that the most similar podcasts to a given podcast can be added to the user's list.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    user_list = create_user_list()
+    add_most_similar_podcasts(graph, user_list)
+    assert len(user_list) > 0
+
+
+def test_repeat_steps():
+    """
+    Tests that steps 5-6 can be repeated until the user's list contains the desired number of podcasts.
+    """
+    graph = create_graph()
+    weight_edges(graph)
+    user_list = create_user_list()
+    repeat_steps(graph, user_list)
+    assert len(user_list) == 10
diff --git a/playground/demos/ai_acceleerated_learning/Vocal.py b/playground/demos/ai_acceleerated_learning/Vocal.py
new file mode 100644
index 00000000..85470156
--- /dev/null
+++ b/playground/demos/ai_acceleerated_learning/Vocal.py
@@ -0,0 +1,46 @@
+import pytest
+
+def test_create_youtube_account():
+    # Arrange
+    # Act
+    # Assert
+
+def test_install_video_editing_software():
+    # Arrange
+    # Act
+    # Assert
+
+def test_write_script():
+    # Arrange
+    # Act
+    # Assert
+
+def test_gather_footage():
+    # Arrange
+    # Act
+    # Assert
+
+def test_edit_video():
+    # Arrange
+    # Act
+    # Assert
+
+def test_export_video():
+    # Arrange
+    # Act
+    # Assert
+
+def test_upload_video_to_youtube():
+    # Arrange
+    # Act
+    # Assert
+
+def test_optimize_video_for_search():
+    # Arrange
+    # Act
+    # Assert
+
+def test_share_video():
+    # Arrange
+    # Act
+    # Assert
\ No newline at end of file
diff --git a/playground/demos/ai_acceleerated_learning/main.py b/playground/demos/ai_acceleerated_learning/main.py
new file mode 100644
index 00000000..44eba542
--- /dev/null
+++ b/playground/demos/ai_acceleerated_learning/main.py
@@ -0,0 +1,253 @@
+import concurrent
+import csv
+from swarms import Agent, OpenAIChat
+from swarms.memory import ChromaDB
+from dotenv import load_dotenv
+from swarms.utils.parse_code import extract_code_from_markdown
+from swarms.utils.file_processing import create_file
+from swarms.utils.loguru_logger import logger
+
+
+# Load ENV
+load_dotenv()
+
+# Gemini
+gemini = OpenAIChat()
+
+# memory
+memory = ChromaDB(output_dir="swarm_hackathon")
+
+
+def execute_concurrently(callable_functions: callable, max_workers=5):
+    """
+    Executes callable functions concurrently using multithreading.
+
+    Parameters:
+    - callable_functions: A list of tuples, each containing the callable function and its arguments.
+      For example: [(function1, (arg1, arg2), {'kwarg1': val1}), (function2, (), {})]
+    - max_workers: The maximum number of threads to use.
+
+    Returns:
+    - results: A list of results returned by the callable functions. If an error occurs in any function,
+      the exception object will be placed at the corresponding index in the list.
+    """
+    results = [None] * len(callable_functions)
+
+    def worker(fn, args, kwargs, index):
+        try:
+            result = fn(*args, **kwargs)
+            results[index] = result
+        except Exception as e:
+            results[index] = e
+
+    with concurrent.futures.ThreadPoolExecutor(
+        max_workers=max_workers
+    ) as executor:
+        futures = []
+        for i, (fn, args, kwargs) in enumerate(callable_functions):
+            futures.append(
+                executor.submit(worker, fn, args, kwargs, i)
+            )
+
+        # Wait for all threads to complete
+        concurrent.futures.wait(futures)
+
+    return results
+
+
+# Adjusting the function to extract specific column values
+def extract_and_create_agents(
+    csv_file_path: str, target_columns: list
+):
+    """
+    Reads a CSV file, extracts "Project Name" and "Lightning Proposal" for each row,
+    creates an Agent for each, and adds it to the swarm network.
+
+    Parameters:
+    - csv_file_path: The path to the CSV file.
+    - target_columns: A list of column names to extract values from.
+    """
+    try:
+        agents = []
+        with open(csv_file_path, mode="r", encoding="utf-8") as file:
+            reader = csv.DictReader(file)
+            for row in reader:
+                project_name = row[target_columns[0]]
+                lightning_proposal = row[target_columns[1]]
+
+                # Example of creating and adding an agent based on the project name and lightning proposal
+                agent_name = f"{project_name} agent"
+                print(agent_name)  # For demonstration
+
+                # Create the agent
+                logger.info("Creating agent...")
+
+                # Design agent
+                logger.info("Creating design agent...")
+                design_agent = Agent(
+                    llm=gemini,
+                    agent_name="Design Agent",
+                    max_loops=1,
+                    stopping_token="<DONE?>",
+                    sop=None,
+                    system_prompt=(
+                        "Transform an app idea into step by step very"
+                        " simple algorithmic psuedocode so it can be"
+                        " implemented simply."
+                    ),
+                    long_term_memory=memory,
+                )
+
+                # Log the agent
+                logger.info(
+                    f"Code Agent created: {agent_name} with long term"
+                    " memory"
+                )
+                agent = Agent(
+                    llm=gemini,
+                    agent_name=agent_name,
+                    max_loops=1,
+                    code_interpreter=True,
+                    stopping_token="<DONE?>",
+                    sop=None,
+                    system_prompt=(
+                        "Transform an app idea into a very simple"
+                        " python app in markdown. Return all the"
+                        " python code in a single markdown file."
+                        " Return only code and nothing else."
+                    ),
+                    long_term_memory=memory,
+                )
+
+                # Testing agent
+                logger.info(f"Testing_agent agent: {agent_name}")
+                agent = Agent(
+                    llm=gemini,
+                    agent_name=agent_name + " testing",
+                    max_loops=1,
+                    stopping_token="<DONE?>",
+                    sop=None,
+                    system_prompt=(
+                        "Create unit tests using pytest based on the"
+                        " code you see, only return unit test code in"
+                        " python using markdown, only return the code"
+                        " and nothing else."
+                    ),
+                    long_term_memory=memory,
+                )
+
+                # Log the agent
+                logger.info(
+                    f"Agent created: {agent_name} with long term"
+                    " memory"
+                )
+                agents.append(agent)
+
+                # Design agent
+                design_agent_output = design_agent.run(
+                    (
+                        "Create the algorithmic psuedocode for the"
+                        f" {lightning_proposal} in markdown and"
+                        " return it"
+                    ),
+                    None,
+                )
+
+                logger.info(
+                    "Algorithmic psuedocode created:"
+                    f" {design_agent_output}"
+                )
+
+                # Create the code for each project
+                output = agent.run(
+                    (
+                        "Create the code for the"
+                        f" {lightning_proposal} in python using the"
+                        " algorithmic psuedocode"
+                        f" {design_agent_output} and wrap it in"
+                        " markdown and return it"
+                    ),
+                    None,
+                )
+                print(output)
+                # Parse the output
+                output = extract_code_from_markdown(output)
+                # Create the file
+                output = create_file(output, f"{project_name}.py")
+
+                # Testing agent
+                testing_agent_output = agent.run(
+                    (
+                        "Create the unit tests for the"
+                        f" {lightning_proposal} in python using the"
+                        f" code {output} and wrap it in markdown and"
+                        " return it"
+                    ),
+                    None,
+                )
+                print(testing_agent_output)
+
+                # Parse the output
+                testing_agent_output = extract_code_from_markdown(
+                    testing_agent_output
+                )
+                # Create the file
+                testing_agent_output = create_file(
+                    testing_agent_output, f"test_{project_name}.py"
+                )
+
+                # Log the project created
+                logger.info(
+                    f"Project {project_name} created: {output} at"
+                    f" file path {project_name}.py"
+                )
+                print(output)
+
+                # Log the unit tests created
+                logger.info(
+                    f"Unit tests for {project_name} created:"
+                    f" {testing_agent_output} at file path"
+                    f" test_{project_name}.py"
+                )
+
+                print(
+                    f"Agent {agent_name} created and added to the"
+                    " swarm network"
+                )
+
+        return agents
+
+    except Exception as e:
+        logger.error(
+            "An error occurred while extracting and creating"
+            f" agents: {e}"
+        )
+        return None
+
+
+# CSV
+csv_file = "presentation.csv"
+
+# Specific columns to extract
+target_columns = ["Project Name", "Project Description"]
+
+# Use the adjusted function
+specific_column_values = extract_and_create_agents(
+    csv_file, target_columns
+)
+
+# Display the extracted column values
+print(specific_column_values)
+
+
+# Concurrently execute the function
+logger.info(
+    "Concurrently executing the swarm for each hackathon project..."
+)
+output = execute_concurrently(
+    [
+        (extract_and_create_agents, (csv_file, target_columns), {}),
+    ],
+    max_workers=5,
+)
+print(output)
diff --git a/playground/demos/ai_acceleerated_learning/presentation assistant.py b/playground/demos/ai_acceleerated_learning/presentation assistant.py
new file mode 100644
index 00000000..fb03c814
--- /dev/null
+++ b/playground/demos/ai_acceleerated_learning/presentation assistant.py	
@@ -0,0 +1,86 @@
+class MockApp:
+    def __init__(self):
+        self.running = True
+        self.session = None
+        self.slides = []
+
+    def main_menu(self):
+        return input("Choose option: 1. Start, 2. Load, 3. Exit ")
+
+    def start_new_talk(self, title):
+        self.session = title
+        self.slides = []
+
+    def add_slide(self, content):
+        self.slides.append(content)
+
+    def edit_slide(self, index, content):
+        self.slides[index] = content
+
+    def delete_slide(self, index):
+        del self.slides[index]
+
+    def reorder_slides(self, new_order):
+        self.slides = [self.slides[i] for i in new_order]
+
+    def get_number_of_slides(self):
+        return len(self.slides)
+
+    # Function to simulate user actions
+    def simulate_user_action(self, action):
+        # Placeholder function to simulate user interaction, not part of the actual app code
+        pass
+
+
+# Testing starting a new talk
+def test_start_new_talk():
+    app = MockApp()
+    app.start_new_talk("My New Talk")
+    assert app.session == "My New Talk"
+    assert app.slides == []
+
+
+# Testing adding a slide
+def test_add_slide():
+    app = MockApp()
+    app.start_new_talk("Talk 1")
+    app.add_slide("Slide Content 1")
+    assert app.slides == ["Slide Content 1"]
+
+
+# Testing editing a slide
+def test_edit_slide():
+    app = MockApp()
+    app.start_new_talk("Talk 1")
+    app.add_slide("Slide Content 1")
+    app.edit_slide(0, "Updated Slide Content 1")
+    assert app.slides == ["Updated Slide Content 1"]
+
+
+# Testing deleting a slide
+def test_delete_slide():
+    app = MockApp()
+    app.start_new_talk("Talk 1")
+    app.add_slide("Slide Content 1")
+    app.add_slide("Slide Content 2")
+    app.delete_slide(0)
+    assert app.slides == ["Slide Content 2"]
+
+
+# Testing reordering slides
+def test_reorder_slides():
+    app = MockApp()
+    app.start_new_talk("Talk 1")
+    app.add_slide("Slide Content 1")
+    app.add_slide("Slide Content 2")
+    app.reorder_slides([1, 0])
+    assert app.slides == ["Slide Content 2", "Slide Content 1"]
+
+
+# Testing the number of slides
+def test_slide_count():
+    app = MockApp()
+    app.start_new_talk("Talk 1")
+    app.add_slide("Slide Content 1")
+    app.add_slide("Slide Content 2")
+    assert app.get_number_of_slides() == 2
diff --git a/playground/demos/ai_acceleerated_learning/presentation.csv b/playground/demos/ai_acceleerated_learning/presentation.csv
new file mode 100644
index 00000000..66894008
--- /dev/null
+++ b/playground/demos/ai_acceleerated_learning/presentation.csv
@@ -0,0 +1,15 @@
+Project Name,Team Members,Project Description,Project Link / Code,Team Twitter Handles
+presentation assistant,robert nowell,live visual aid for talks,loom,@robertnowell1
+Vocal,"Jeremy Nixon, Amir Gamil, Eliott Hoffenberg, Trina Chatterjee, Ruby Yeh","Educational Video Generation, Prompt -> Youtube Video",,"@jvnixon, @amirbolous, @Eliotthoff, @trina_chatt"
+Podgraph ,"DC, Leo, Anupam",Graph based podcast learning,https://github.com/dcsan/kbxt ; https://www.figma.com/file/sui06ZgDGXrHOVlrJDiOD7/Untitled?type=design&node-id=0%3A1&mode=design&t=LnQCl13XroVHVbxD-1,@anupambatra_  |  @dcsan
+"Listen, chat and learn!!!",James,Chat with a podcast to learn things,https://react.gitwit.dev/run/zfGVjrjsa6ZKaEU1PldW,@jamesmurdza
+Recall,Liam & Caden,conversation information retrieval,https://recall-97b8b27a6a92.herokuapp.com/,
+VoiceStudyBot,Konrad,Personal tutor to test your knowledge of a book,,@konrad_gnat
+Short Form Upskill,"Margarita, Aditya, Johnny",TikTok Scrape and Transcribe ,margro2000/Learn (github.com),https://twitter.com/Marg_Groisman
+Rohan,Rohan,Rohan,,
+Envision: diagram dataset,Steve,An API to translate any technical concept into diagrams,https://github.com/stephenkfrey/diagrammatic,twitter.com/stevekfrey
+Arxiv2Video,Lily Su,Converts an Arxiv web url to a short video,https://github.com/LilySu/Arxiv2Video,@excelsiorpred
+Dir Chat,Andy Li,Combine to power of SQL and RAG to serach courses,,@xdotli
+Empathy Coach,Ji Young Lim,A chatbot that coches people to make more empathetic conversations,,@jyl1030
+Aimor,Brach Burdick,Platform for assessing and monitoring the psychological wellbeing of a body of students based on conversations with an AI therapist,https://aimor-git-staging-aimor.vercel.app/admin,https://twitter.com/__brach__
+Structured TA bot Generation,Wenxi,Generate structured tutorial chatbot based on video transcript and potentially videos,https://github.com/wenxichen/video2ta ,
\ No newline at end of file
diff --git a/playground/demos/ai_acceleerated_learning/test_Vocal.py b/playground/demos/ai_acceleerated_learning/test_Vocal.py
new file mode 100644
index 00000000..b8e1e14f
--- /dev/null
+++ b/playground/demos/ai_acceleerated_learning/test_Vocal.py
@@ -0,0 +1,38 @@
+from ai_acceleerated_learning.Vocal import Vocal
+
+vocal = Vocal()
+
+
+def test_pass():
+    assert (
+        vocal.generate_video(
+            "I love to play basketball, and I am a very good player.",
+            "basketball",
+        )
+        == "Successfully generated a YouTube video for your prompt: I"
+        " love to play basketball, and I am a very good player."
+    )
+
+
+def test_invalid_sports():
+    assert (
+        vocal.generate_video(
+            "I just ate some delicious tacos", "tacos"
+        )
+        == "Invalid sports entered!! Please enter a valid sport."
+    )
+
+
+def test_invalid_prompt():
+    assert (
+        vocal.generate_video(987, "basketball")
+        == "Invalid prompt entered!! Please enter a valid prompt."
+    )
+
+
+def test_not_string():
+    assert (
+        vocal.generate_video(789, 234)
+        == "Invalid prompt and sports entered!! Please enter valid"
+        " prompt and sport."
+    )
diff --git a/playground/demos/ai_acceleerated_learning/test_presentation assistant.py b/playground/demos/ai_acceleerated_learning/test_presentation assistant.py
new file mode 100644
index 00000000..5a27eebd
--- /dev/null
+++ b/playground/demos/ai_acceleerated_learning/test_presentation assistant.py	
@@ -0,0 +1,86 @@
+# test_presentation_assistant.py
+
+import pytest
+from presentation_assistant import (
+    PresentationAssistant,
+    SlideNotFoundError,
+)
+
+
+@pytest.fixture
+def assistant():
+    slides = [
+        "Welcome to our presentation!",
+        "Here is the agenda for today.",
+        "Let's dive into the first topic.",
+        "Thank you for attending.",
+    ]
+    return PresentationAssistant(slides)
+
+
+def test_init():
+    slides = ["Slide 1", "Slide 2"]
+    pa = PresentationAssistant(slides)
+    assert pa.slides == slides
+    assert pa.current_slide == 0
+
+
+def test_next_slide(assistant):
+    assistant.next_slide()
+    assert assistant.current_slide == 1
+    assistant.next_slide()
+    assert assistant.current_slide == 2
+
+
+def test_previous_slide(assistant):
+    assistant.current_slide = 2
+    assistant.previous_slide()
+    assert assistant.current_slide == 1
+    assistant.previous_slide()
+    assert assistant.current_slide == 0
+
+
+def test_next_slide_at_end(assistant):
+    assistant.current_slide = len(assistant.slides) - 1
+    with pytest.raises(SlideNotFoundError):
+        assistant.next_slide()
+
+
+def test_previous_slide_at_start(assistant):
+    with pytest.raises(SlideNotFoundError):
+        assistant.previous_slide()
+
+
+def test_go_to_slide(assistant):
+    assistant.go_to_slide(2)
+    assert assistant.current_slide == 2
+
+
+def test_go_to_slide_out_of_range(assistant):
+    with pytest.raises(SlideNotFoundError):
+        assistant.go_to_slide(len(assistant.slides))
+
+
+def test_go_to_slide_negative(assistant):
+    with pytest.raises(SlideNotFoundError):
+        assistant.go_to_slide(-1)
+
+
+def test_current_slide_content(assistant):
+    content = assistant.current_slide_content()
+    assert content == assistant.slides[0]
+    assistant.next_slide()
+    content = assistant.current_slide_content()
+    assert content == assistant.slides[1]
+
+
+def test_show_slide(
+    assistant, capsys
+):  # capsys is a pytest fixture to capture stdout and stderr
+    assistant.show_slide()
+    captured = capsys.readouterr()
+    assert captured.out.strip() == assistant.slides[0]
+    assistant.next_slide()
+    assistant.show_slide()
+    captured = capsys.readouterr()
+    assert captured.out.strip() == assistant.slides[1]
diff --git a/playground/structs/custom_model_with_agent.py b/playground/structs/custom_model_with_agent.py
new file mode 100644
index 00000000..8849fc41
--- /dev/null
+++ b/playground/structs/custom_model_with_agent.py
@@ -0,0 +1,29 @@
+from swarms import Agent
+from swarms.models.base_llm import AbstractLLM
+
+
+class ExampleLLM(AbstractLLM):
+    def __init__():
+        pass
+
+    def run(self, task: str, *args, **kwargs):
+        pass
+
+
+## Initialize the workflow
+agent = Agent(
+    llm=ExampleLLM(),
+    max_loops="auto",
+    autosave=True,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    stopping_token="<DONE>",
+    interactive=True,
+)
+
+# Run the workflow on a task
+agent(
+    "Generate a transcript for a youtube video on what swarms are!"
+    " Output a <DONE> token when done."
+)
diff --git a/pyproject.toml b/pyproject.toml
index a9611796..329f4ba5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,6 +59,7 @@ rich = "13.5.2"
 sqlalchemy = "*"
 bitsandbytes = "*"
 pgvector = "*"
+cohere = "*"
 sentence-transformers = "*"
 peft = "*"
 psutil = "*"
diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py
index de6c2856..44f38f5d 100644
--- a/swarms/structs/agent.py
+++ b/swarms/structs/agent.py
@@ -28,6 +28,7 @@ from swarms.utils.data_to_text import data_to_text
 from swarms.utils.parse_code import extract_code_from_markdown
 from swarms.utils.pdf_to_text import pdf_to_text
 from swarms.utils.token_count_tiktoken import limit_tokens_from_string
+from swarms.utils.execution_sandbox import execute_code_in_sandbox
 
 
 # Utils
@@ -625,6 +626,20 @@ class Agent:
                                 response
                             )
 
+                        # Code interpreter
+                        if self.code_interpreter:
+                            response = extract_code_from_markdown(
+                                response
+                            )
+                            # Execute the code in the sandbox
+                            response = execute_code_in_sandbox(
+                                response
+                            )
+                            response = task + response
+                            response = self.llm(
+                                response, *args, **kwargs
+                            )
+
                         # Add the response to the history
                         history.append(response)
 
@@ -641,7 +656,6 @@ class Agent:
                             evaluated_response = self.evaluator(
                                 response
                             )
-
                             out = (
                                 f"Response: {response}\nEvaluated"
                                 f" Response: {evaluated_response}"
@@ -674,10 +688,6 @@ class Agent:
                         if self.parser:
                             response = self.parser(response)
 
-                        # If code interpreter is enabled then run the code
-                        if self.code_interpreter:
-                            self.run_code(response)
-
                         # If tools are enabled then execute the tools
                         if self.tools:
                             execute_tool_by_name(
diff --git a/swarms/utils/execution_sandbox.py b/swarms/utils/execution_sandbox.py
index 6c65e25a..af6c3840 100644
--- a/swarms/utils/execution_sandbox.py
+++ b/swarms/utils/execution_sandbox.py
@@ -1,5 +1,7 @@
-import asyncio
 import logging
+import os
+import subprocess
+import tempfile
 import traceback
 from typing import Tuple
 
@@ -51,21 +53,62 @@ async def execute_code_async(code: str) -> Tuple[str, str]:
     return out, error_message
 
 
-def execute_code_sandbox(
-    code: str, async_on: bool = False
-) -> Tuple[str, str]:
+def execute_code_in_sandbox(code: str, language: str = "python"):
     """
-    Executes the given code in a sandbox environment.
+    Execute code in a specified language using subprocess and return the results or errors.
 
     Args:
         code (str): The code to be executed.
-        async_on (bool, optional): Indicates whether to execute the code asynchronously.
-                                   Defaults to False.
+        language (str): The programming language of the code. Currently supports 'python' only.
 
     Returns:
-        Tuple[str, str]: A tuple containing the stdout and stderr outputs of the code execution.
+        dict: A dictionary containing either the result or any errors.
     """
-    if async_on:
-        return asyncio.run(execute_code_async(code))
-    else:
-        return execute_code_async(code)
+    result = {"output": None, "errors": None}
+
+    try:
+        if language == "python":
+            # Write the code to a temporary file
+            with tempfile.NamedTemporaryFile(
+                delete=False, suffix=".py", mode="w"
+            ) as tmp:
+                tmp.write(code)
+                tmp_path = tmp.name
+
+            # Execute the code in a separate process
+            process = subprocess.run(
+                ["python", tmp_path],
+                capture_output=True,
+                text=True,
+                timeout=10,
+            )
+
+            # Capture the output and errors
+            result["output"] = process.stdout
+            result["errors"] = process.stderr
+
+        else:
+            # Placeholder for other languages; each would need its own implementation
+            raise NotImplementedError(
+                f"Execution for {language} not implemented."
+            )
+
+    except subprocess.TimeoutExpired:
+        result["errors"] = "Execution timed out."
+    except Exception as e:
+        result["errors"] = str(e)
+    finally:
+        # Ensure the temporary file is removed after execution
+        if "tmp_path" in locals():
+            os.remove(tmp_path)
+
+    return result
+
+
+# # Example usage
+# code_to_execute = """
+# print("Hello, world!")
+# """
+
+# execution_result = execute_code(code_to_execute)
+# print(json.dumps(execution_result, indent=4))