Formatting (#2750)

examples/misc/fitness_checker.py

@@ -20,19 +20,19 @@ agent = Agent(
     name="Fitness Agent",
     model=OpenAIChat(id="gpt-4o"),
     description="You are a helpful fitness assistant who remembers past logs and gives personalized suggestions for Anish's training and diet.",
-    markdown=True
+    markdown=True,
 )
 
 
 # Store user preferences as memory
 def store_user_preferences(conversation: list, user_id: str = USER_ID):
     """Store user preferences from conversation history"""
-    memory_client.add(conversation, user_id=user_id, output_format='v1.1')
+    memory_client.add(conversation, user_id=user_id, output_format="v1.1")
 
 
 # Memory-aware assistant function
 def fitness_coach(user_input: str, user_id: str = USER_ID):
-    memories = memory_client.search(user_input, user_id=user_id)   # Search relevant memories bases on user query
+    memories = memory_client.search(user_input, user_id=user_id)  # Search relevant memories bases on user query
     memory_context = "\n".join(f"- {m['memory']}" for m in memories)
 
     prompt = f"""You are a fitness assistant who helps Anish with his training, recovery, and diet. You have long-term memory of his health, routines, preferences, and past conversations.
@@ -48,113 +48,66 @@ User query:
     memory_client.add(f"User: {user_input}\nAssistant: {response.content}", user_id=user_id)
     return response.content
 
+
 # --------------------------------------------------
 # Store user preferences and memories
 messages = [
     {
         "role": "user",
-        "content": "Hi, I’m Anish. I'm 26 years old, 5'10\", and weigh 72kg. I started working out 6 months ago with the goal of building lean muscle."
+        "content": "Hi, I’m Anish. I'm 26 years old, 5'10\", and weigh 72kg. I started working out 6 months ago with the goal of building lean muscle.",
     },
     {
         "role": "assistant",
-        "content": "Got it — you're 26, 5'10\", 72kg, and on a lean muscle journey. Started gym 6 months ago."
+        "content": "Got it — you're 26, 5'10\", 72kg, and on a lean muscle journey. Started gym 6 months ago.",
     },
     {
         "role": "user",
-        "content": "I follow a push-pull-legs routine and train 5 times a week. My rest days are Wednesday and Sunday."
+        "content": "I follow a push-pull-legs routine and train 5 times a week. My rest days are Wednesday and Sunday.",
     },
     {
         "role": "assistant",
-        "content": "Understood — push-pull-legs split, training 5x/week with rest on Wednesdays and Sundays."
+        "content": "Understood — push-pull-legs split, training 5x/week with rest on Wednesdays and Sundays.",
     },
+    {"role": "user", "content": "After push days, I usually eat high-protein and moderate-carb meals to recover."},
+    {"role": "assistant", "content": "Noted — high-protein, moderate-carb meals after push workouts."},
+    {"role": "user", "content": "For pull days, I take whey protein and eat a banana after training."},
+    {"role": "assistant", "content": "Logged — whey protein and banana post pull workouts."},
+    {"role": "user", "content": "On leg days, I make sure to have complex carbs like rice or oats."},
+    {"role": "assistant", "content": "Noted — complex carbs like rice and oats are part of your leg day meals."},
     {
         "role": "user",
-        "content": "After push days, I usually eat high-protein and moderate-carb meals to recover."
-    },
-    {
-        "role": "assistant",
-        "content": "Noted — high-protein, moderate-carb meals after push workouts."
+        "content": "I often feel sore after leg days, so I use turmeric milk and magnesium to help with recovery.",
     },
+    {"role": "assistant", "content": "I'll remember turmeric milk and magnesium as part of your leg day recovery."},
     {
         "role": "user",
-        "content": "For pull days, I take whey protein and eat a banana after training."
+        "content": "Last push day, I did 3x8 bench press at 60kg, 4x12 overhead press, and dips. Felt fatigued after.",
     },
     {
         "role": "assistant",
-        "content": "Logged — whey protein and banana post pull workouts."
+        "content": "Push day logged — 60kg bench, overhead press, dips. You felt fatigued afterward.",
     },
+    {"role": "user", "content": "I prefer light dinners post-workout like tofu, soup, and vegetables."},
+    {"role": "assistant", "content": "Got it — light dinners post-workout: tofu, soup, and veggies."},
     {
         "role": "user",
-        "content": "On leg days, I make sure to have complex carbs like rice or oats."
-    },
-    {
-        "role": "assistant",
-        "content": "Noted — complex carbs like rice and oats are part of your leg day meals."
+        "content": "I have mild lactose intolerance, so I avoid dairy. I use almond milk or lactose-free whey.",
     },
+    {"role": "assistant", "content": "Understood — avoiding regular dairy, using almond milk and lactose-free whey."},
     {
         "role": "user",
-        "content": "I often feel sore after leg days, so I use turmeric milk and magnesium to help with recovery."
+        "content": "I get occasional knee pain, so I avoid deep squats and do more hamstring curls and glute bridges on leg days.",
     },
     {
         "role": "assistant",
-        "content": "I'll remember turmeric milk and magnesium as part of your leg day recovery."
-    },
-    {
-        "role": "user",
-        "content": "Last push day, I did 3x8 bench press at 60kg, 4x12 overhead press, and dips. Felt fatigued after."
-    },
-    {
-        "role": "assistant",
-        "content": "Push day logged — 60kg bench, overhead press, dips. You felt fatigued afterward."
-    },
-    {
-        "role": "user",
-        "content": "I prefer light dinners post-workout like tofu, soup, and vegetables."
-    },
-    {
-        "role": "assistant",
-        "content": "Got it — light dinners post-workout: tofu, soup, and veggies."
-    },
-    {
-        "role": "user",
-        "content": "I have mild lactose intolerance, so I avoid dairy. I use almond milk or lactose-free whey."
-    },
-    {
-        "role": "assistant",
-        "content": "Understood — avoiding regular dairy, using almond milk and lactose-free whey."
-    },
-    {
-        "role": "user",
-        "content": "I get occasional knee pain, so I avoid deep squats and do more hamstring curls and glute bridges on leg days."
-    },
-    {
-        "role": "assistant",
-        "content": "Noted — due to knee discomfort, you substitute deep squats with curls and glute bridges."
-    },
-    {
-        "role": "user",
-        "content": "I track sleep and notice poor performance when I sleep less than 6 hours."
-    },
-    {
-        "role": "assistant",
-        "content": "Logged — performance drops when you get under 6 hours of sleep."
-    },
-    {
-        "role": "user",
-        "content": "I take magnesium supplements to help with muscle recovery and sleep quality."
-    },
-    {
-        "role": "assistant",
-        "content": "Remembered — magnesium helps you with recovery and sleep."
-    },
-    {
-        "role": "user",
-        "content": "I avoid caffeine after 4 PM because it affects my sleep."
-    },
-    {
-        "role": "assistant",
-        "content": "Got it — you avoid caffeine post-4 PM to protect your sleep."
+        "content": "Noted — due to knee discomfort, you substitute deep squats with curls and glute bridges.",
     },
+    {"role": "user", "content": "I track sleep and notice poor performance when I sleep less than 6 hours."},
+    {"role": "assistant", "content": "Logged — performance drops when you get under 6 hours of sleep."},
+    {"role": "user", "content": "I take magnesium supplements to help with muscle recovery and sleep quality."},
+    {"role": "assistant", "content": "Remembered — magnesium helps you with recovery and sleep."},
+    {"role": "user", "content": "I avoid caffeine after 4 PM because it affects my sleep."},
+    {"role": "assistant", "content": "Got it — you avoid caffeine post-4 PM to protect your sleep."},
 ]
 store_user_preferences(messages)
 

examples/misc/healthcare_assistant_google_adk.py

@@ -1,9 +1,11 @@
 import asyncio
 import warnings
+
 from google.adk.agents import Agent
-from google.adk.sessions import InMemorySessionService
 from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
 from google.genai import types
+
 from mem0 import MemoryClient
 
 warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -19,14 +21,14 @@ def save_patient_info(information: str) -> dict:
     print(f"Storing patient information: {information[:30]}...")
 
     # Get user_id from session state or use default
-    user_id = getattr(save_patient_info, 'user_id', 'default_user')
+    user_id = getattr(save_patient_info, "user_id", "default_user")
 
     # Store in Mem0
-    response = mem0_client.add(
+    mem0_client.add(
         [{"role": "user", "content": information}],
         user_id=user_id,
         run_id="healthcare_session",
-        metadata={"type": "patient_information"}
+        metadata={"type": "patient_information"},
     )
 
     return {"status": "success", "message": "Information saved"}
@@ -37,7 +39,7 @@ def retrieve_patient_info(query: str) -> str:
     print(f"Searching for patient information: {query}")
 
     # Get user_id from session state or use default
-    user_id = getattr(retrieve_patient_info, 'user_id', 'default_user')
+    user_id = getattr(retrieve_patient_info, "user_id", "default_user")
 
     # Search Mem0
     results = mem0_client.search(
@@ -45,7 +47,7 @@ def retrieve_patient_info(query: str) -> str:
         user_id=user_id,
         run_id="healthcare_session",
         limit=5,
-        threshold=0.7  # Higher threshold for more relevant results
+        threshold=0.7,  # Higher threshold for more relevant results
     )
 
     if not results:
@@ -65,7 +67,7 @@ def schedule_appointment(date: str, time: str, reason: str) -> dict:
         "status": "success",
         "appointment_id": appointment_id,
         "confirmation": f"Appointment scheduled for {date} at {time} for {reason}",
-        "message": "Please arrive 15 minutes early to complete paperwork."
+        "message": "Please arrive 15 minutes early to complete paperwork.",
     }
 
 
@@ -89,7 +91,7 @@ IMPORTANT GUIDELINES:
 - For serious symptoms, always recommend consulting a healthcare professional.
 - Keep all patient information confidential.
 """,
-    tools=[save_patient_info, retrieve_patient_info, schedule_appointment]
+    tools=[save_patient_info, retrieve_patient_info, schedule_appointment],
 )
 
 # Set Up Session and Runner
@@ -101,18 +103,10 @@ USER_ID = "Alex"
 SESSION_ID = "session_001"
 
 # Create a session
-session = session_service.create_session(
-    app_name=APP_NAME,
-    user_id=USER_ID,
-    session_id=SESSION_ID
-)
+session = session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)
 
 # Create the runner
-runner = Runner(
-    agent=healthcare_agent,
-    app_name=APP_NAME,
-    session_service=session_service
-)
+runner = Runner(agent=healthcare_agent, app_name=APP_NAME, session_service=session_service)
 
 
 # Interact with the Healthcare Assistant
@@ -121,21 +115,14 @@ async def call_agent_async(query, runner, user_id, session_id):
     print(f"\n>>> Patient: {query}")
 
     # Format the user's message
-    content = types.Content(
-        role='user',
-        parts=[types.Part(text=query)]
-    )
+    content = types.Content(role="user", parts=[types.Part(text=query)])
 
     # Set user_id for tools to access
     save_patient_info.user_id = user_id
     retrieve_patient_info.user_id = user_id
 
     # Run the agent
-    async for event in runner.run_async(
-            user_id=user_id,
-            session_id=session_id,
-            new_message=content
-    ):
+    async for event in runner.run_async(user_id=user_id, session_id=session_id, new_message=content):
         if event.is_final_response():
             if event.content and event.content.parts:
                 response = event.content.parts[0].text
@@ -152,7 +139,7 @@ async def run_conversation():
         "Hi, I'm Alex. I've been having headaches for the past week, and I have a penicillin allergy.",
         runner=runner,
         user_id=USER_ID,
-        session_id=SESSION_ID
+        session_id=SESSION_ID,
     )
 
     # Request for health information
@@ -160,7 +147,7 @@ async def run_conversation():
         "Can you tell me more about what might be causing my headaches?",
         runner=runner,
         user_id=USER_ID,
-        session_id=SESSION_ID
+        session_id=SESSION_ID,
     )
 
     # Schedule an appointment
@@ -168,15 +155,12 @@ async def run_conversation():
         "I think I should see a doctor. Can you help me schedule an appointment for next Monday at 2pm?",
         runner=runner,
         user_id=USER_ID,
-        session_id=SESSION_ID
+        session_id=SESSION_ID,
     )
 
     # Test memory - should remember patient name, symptoms, and allergy
     await call_agent_async(
-        "What medications should I avoid for my headaches?",
-        runner=runner,
-        user_id=USER_ID,
-        session_id=SESSION_ID
+        "What medications should I avoid for my headaches?", runner=runner, user_id=USER_ID, session_id=SESSION_ID
     )
 
 
@@ -191,37 +175,28 @@ async def interactive_mode():
     session_id = f"session_{hash(patient_id) % 1000:03d}"
 
     # Create session for this user
-    session = session_service.create_session(
-        app_name=APP_NAME,
-        user_id=patient_id,
-        session_id=session_id
-    )
+    session_service.create_session(app_name=APP_NAME, user_id=patient_id, session_id=session_id)
 
     print(f"\nStarting conversation with patient ID: {patient_id}")
     print("Type your message and press Enter.")
 
     while True:
         user_input = input("\n>>> Patient: ").strip()
-        if user_input.lower() in ['exit', 'quit', 'bye']:
+        if user_input.lower() in ["exit", "quit", "bye"]:
             print("Ending conversation. Thank you!")
             break
 
-        await call_agent_async(
-            user_input,
-            runner=runner,
-            user_id=patient_id,
-            session_id=session_id
-        )
+        await call_agent_async(user_input, runner=runner, user_id=patient_id, session_id=session_id)
 
 
 # Main execution
 if __name__ == "__main__":
     import argparse
 
-    parser = argparse.ArgumentParser(description='Healthcare Assistant with Memory')
-    parser.add_argument('--demo', action='store_true', help='Run the demo conversation')
-    parser.add_argument('--interactive', action='store_true', help='Run in interactive mode')
-    parser.add_argument('--patient-id', type=str, default=USER_ID, help='Patient ID for the conversation')
+    parser = argparse.ArgumentParser(description="Healthcare Assistant with Memory")
+    parser.add_argument("--demo", action="store_true", help="Run the demo conversation")
+    parser.add_argument("--interactive", action="store_true", help="Run in interactive mode")
+    parser.add_argument("--patient-id", type=str, default=USER_ID, help="Patient ID for the conversation")
     args = parser.parse_args()
 
     if args.demo:
@@ -231,5 +206,3 @@ if __name__ == "__main__":
     else:
         # Default to demo mode if no arguments provided
         asyncio.run(run_conversation())
-
-

examples/misc/movie_recommendation_grok3.py

@@ -16,26 +16,21 @@ from mem0 import Memory
 
 # Configure Mem0 with Grok 3 and Qdrant
 config = {
-    "vector_store": {
-        "provider": "qdrant",
-        "config": {
-            "embedding_model_dims": 384
-        }
-    },
+    "vector_store": {"provider": "qdrant", "config": {"embedding_model_dims": 384}},
     "llm": {
         "provider": "xai",
         "config": {
             "model": "grok-3-beta",
             "temperature": 0.1,
             "max_tokens": 2000,
-        }
+        },
     },
     "embedder": {
         "provider": "huggingface",
         "config": {
             "model": "all-MiniLM-L6-v2"  # open embedding model
-        }
-    }
+        },
+    },
 }
 
 # Instantiate memory layer
@@ -57,20 +52,14 @@ def recommend_movie_with_memory(user_id: str, user_query: str):
         prompt += f"\nPreviously, the user mentioned: {past_memories}"
 
     # Generate movie recommendation using Grok 3
-    response = grok_client.chat.completions.create(
-        model="grok-3-beta",
-        messages=[
-            {"role": "user", "content": prompt}
-        ]
-    )
+    response = grok_client.chat.completions.create(model="grok-3-beta", messages=[{"role": "user", "content": prompt}])
     recommendation = response.choices[0].message.content
 
     # Store conversation in memory
     memory.add(
-        [{"role": "user", "content": user_query},
-         {"role": "assistant", "content": recommendation}],
+        [{"role": "user", "content": user_query}, {"role": "assistant", "content": recommendation}],
         user_id=user_id,
-        metadata={"category": "movie"}
+        metadata={"category": "movie"},
     )
 
     return recommendation
@@ -81,10 +70,11 @@ if __name__ == "__main__":
     user_id = "arshi"
     recommend_movie_with_memory(user_id, "I'm looking for a movie to watch tonight. Any suggestions?")
     # OUTPUT: You have watched Intersteller last weekend and you don't like horror movies, maybe you can watch "Purple Hearts" today.
-    recommend_movie_with_memory(user_id, "Can we skip the tearjerkers? I really enjoyed Notting Hill and Crazy Rich Asians.")
+    recommend_movie_with_memory(
+        user_id, "Can we skip the tearjerkers? I really enjoyed Notting Hill and Crazy Rich Asians."
+    )
     # OUTPUT: Got it — no sad endings! You might enjoy "The Proposal" or "Love, Rosie". They’re both light-hearted romcoms with happy vibes.
     recommend_movie_with_memory(user_id, "Any light-hearted movie I can watch after work today?")
     # OUTPUT: Since you liked Crazy Rich Asians and The Proposal, how about "The Intern" or "Isn’t It Romantic"? Both are upbeat, funny, and perfect for relaxing.
     recommend_movie_with_memory(user_id, "I’ve already watched The Intern. Something new maybe?")
     # OUTPUT: No problem! Try "Your Place or Mine" - romcoms that match your taste and are tear-free!
-

examples/misc/personal_assistant_agno.py

@@ -23,8 +23,8 @@ agent = Agent(
     name="Personal Agent",
     model=OpenAIChat(id="gpt-4o"),
     description="You are a helpful personal agent that helps me with day to day activities."
-                "You can process both text and images.",
-    markdown=True
+    "You can process both text and images.",
+    markdown=True,
 )
 
 
@@ -35,24 +35,16 @@ def chat_user(user_input: str = None, user_id: str = "user_123", image_path: str
             base64_image = base64.b64encode(image_file.read()).decode("utf-8")
 
         # First: the text message
-        text_msg = {
-            "role": "user",
-            "content": user_input
-        }
+        text_msg = {"role": "user", "content": user_input}
 
         # Second: the image message
         image_msg = {
             "role": "user",
-            "content": {
-                "type": "image_url",
-                "image_url": {
-                    "url": f"data:image/jpeg;base64,{base64_image}"
-                }
-            }
+            "content": {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}},
         }
 
         # Send both as separate message objects
-        client.add([text_msg, image_msg], user_id=user_id, output_format='v1.1')
+        client.add([text_msg, image_msg], user_id=user_id, output_format="v1.1")
         print("✅ Image uploaded and stored in memory.")
 
     if user_input:
@@ -92,10 +84,13 @@ print(chat_user("When is my test?", user_id=user_id))
 # OUTPUT: Your pilot's test is on your birthday, which is in five days. You're turning 25!
 # Good luck with your preparations, and remember to take some time to relax amidst the studying.
 
-print(chat_user("This is the picture of what I brought with me in the trip to Bahamas",
-                image_path="travel_items.jpeg",  # this will be added to Mem0 memory
-                user_id=user_id))
-print(chat_user("hey can you quickly tell me if brought my sunglasses to my trip, not able to find",
-                user_id=user_id))
+print(
+    chat_user(
+        "This is the picture of what I brought with me in the trip to Bahamas",
+        image_path="travel_items.jpeg",  # this will be added to Mem0 memory
+        user_id=user_id,
+    )
+)
+print(chat_user("hey can you quickly tell me if brought my sunglasses to my trip, not able to find", user_id=user_id))
 # OUTPUT: Yes, you did bring your sunglasses on your trip to the Bahamas along with your laptop, face masks and other items..
 # Since you can't find them now, perhaps check the pockets of jackets you wore or in your luggage compartments.

examples/misc/study_buddy.py

@@ -7,6 +7,7 @@ In order to run this file, you need to set up your Mem0 API at Mem0 platform and
 export OPENAI_API_KEY="your_openai_api_key"
 export MEM0_API_KEY="your_mem0_api_key"
 """
+
 import asyncio
 
 from agents import Agent, Runner
@@ -23,25 +24,19 @@ study_agent = Agent(
 - Identify topics the user has struggled with (e.g., "I'm confused", "this is hard")
 - Help with spaced repetition by suggesting topics to revisit based on last review time
 - Personalize answers using stored memories
-- Summarize PDFs or notes the user uploads""")
+- Summarize PDFs or notes the user uploads""",
+)
 
 
 # Upload and store PDF to Mem0
 def upload_pdf(pdf_url: str, user_id: str):
-    pdf_message = {
-        "role": "user",
-        "content": {
-            "type": "pdf_url",
-            "pdf_url": {"url": pdf_url}
-        }
-    }
+    pdf_message = {"role": "user", "content": {"type": "pdf_url", "pdf_url": {"url": pdf_url}}}
     client.add([pdf_message], user_id=user_id)
     print("✅ PDF uploaded and processed into memory.")
 
 
 # Main interaction loop with your personal study buddy
 async def study_buddy(user_id: str, topic: str, user_input: str):
-
     memories = client.search(f"{topic}", user_id=user_id)
     memory_context = "n".join(f"- {m['memory']}" for m in memories)
 
@@ -56,9 +51,11 @@ Now respond to the user's new question or comment:
     result = await Runner.run(study_agent, prompt)
     response = result.final_output
 
-    client.add([
-        {"role": "user", "content": f'''Topic: {topic}nUser: {user_input}nnStudy Assistant: {response}'''}
-    ], user_id=user_id, metadata={"topic": topic})
+    client.add(
+        [{"role": "user", "content": f"""Topic: {topic}nUser: {user_input}nnStudy Assistant: {response}"""}],
+        user_id=user_id,
+        metadata={"topic": topic},
+    )
 
     return response
 
@@ -78,7 +75,12 @@ async def main():
 
     # Demonstrate spaced repetition prompting
     topic = "Momentum Conservation"
-    print(await study_buddy(user_id, topic, "I think we covered this last week. Is it time to review momentum conservation again?"))
+    print(
+        await study_buddy(
+            user_id, topic, "I think we covered this last week. Is it time to review momentum conservation again?"
+        )
+    )
+
 
 if __name__ == "__main__":
     asyncio.run(main())

examples/misc/voice_assistant_elevenlabs.py

@@ -57,7 +57,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "I prefer brief and concise responses without unnecessary explanations. I get frustrated when assistants are too wordy or repeat information I already know."
+            "content": "I prefer brief and concise responses without unnecessary explanations. I get frustrated when assistants are too wordy or repeat information I already know.",
         },
         {
             "role": "assistant",
@@ -65,7 +65,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "I like to listen to jazz music when I'm working, especially artists like Miles Davis and John Coltrane. I find it helps me focus and be more productive."
+            "content": "I like to listen to jazz music when I'm working, especially artists like Miles Davis and John Coltrane. I find it helps me focus and be more productive.",
         },
         {
             "role": "assistant",
@@ -73,7 +73,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "I usually wake up at 7 AM and prefer reminders for meetings 30 minutes in advance. My most productive hours are between 9 AM and noon, so I try to schedule important tasks during that time."
+            "content": "I usually wake up at 7 AM and prefer reminders for meetings 30 minutes in advance. My most productive hours are between 9 AM and noon, so I try to schedule important tasks during that time.",
         },
         {
             "role": "assistant",
@@ -81,7 +81,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "My favorite color is navy blue, and I prefer dark mode in all my apps. I'm allergic to peanuts, so please remind me to check ingredients when I ask about recipes or restaurants."
+            "content": "My favorite color is navy blue, and I prefer dark mode in all my apps. I'm allergic to peanuts, so please remind me to check ingredients when I ask about recipes or restaurants.",
         },
         {
             "role": "assistant",
@@ -89,7 +89,7 @@ def initialize_memory():
         },
         {
             "role": "user",
-            "content": "My partner's name is Jamie, and we have a golden retriever named Max who is 3 years old. My parents live in Chicago, and I try to visit them once every two months."
+            "content": "My partner's name is Jamie, and we have a golden retriever named Max who is 3 years old. My parents live in Chicago, and I try to visit them once every two months.",
         },
         {
             "role": "assistant",
@@ -135,11 +135,11 @@ def record_audio(filename="input.wav", record_seconds=5):
     stream.close()
     p.terminate()
 
-    with wave.open(filename, 'wb') as wf:
+    with wave.open(filename, "wb") as wf:
         wf.setnchannels(channels)
         wf.setsampwidth(p.get_sample_size(fmt))
         wf.setframerate(rate)
-        wf.writeframes(b''.join(frames))
+        wf.writeframes(b"".join(frames))
 
 
 # ------------------ STT USING WHISPER ------------------
@@ -147,10 +147,7 @@ def transcribe_whisper(audio_path):
     print("🔎 Transcribing with Whisper...")
     try:
         with open(audio_path, "rb") as audio_file:
-            transcript = openai_client.audio.transcriptions.create(
-                model="whisper-1",
-                file=audio_file
-            )
+            transcript = openai_client.audio.transcriptions.create(model="whisper-1", file=audio_file)
         print(f"🗣️ You said: {transcript.text}")
         return transcript.text
     except Exception as e:
@@ -165,9 +162,7 @@ def get_agent_response(user_input):
 
     try:
         task = Task(
-            description=f"Respond to: {user_input}",
-            expected_output="A short and relevant reply.",
-            agent=voice_agent
+            description=f"Respond to: {user_input}", expected_output="A short and relevant reply.", agent=voice_agent
         )
         crew = Crew(
             agents=[voice_agent],
@@ -175,22 +170,19 @@ def get_agent_response(user_input):
             process=Process.sequential,
             verbose=True,
             memory=True,
-            memory_config={
-                "provider": "mem0",
-                "config": {"user_id": USER_ID}
-            }
+            memory_config={"provider": "mem0", "config": {"user_id": USER_ID}},
         )
         result = crew.kickoff()
 
         # Extract the text response from the complex result object
-        if hasattr(result, 'raw'):
+        if hasattr(result, "raw"):
             return result.raw
-        elif isinstance(result, dict) and 'raw' in result:
-            return result['raw']
-        elif isinstance(result, dict) and 'tasks_output' in result:
-            outputs = result['tasks_output']
+        elif isinstance(result, dict) and "raw" in result:
+            return result["raw"]
+        elif isinstance(result, dict) and "tasks_output" in result:
+            outputs = result["tasks_output"]
             if outputs and isinstance(outputs, list) and len(outputs) > 0:
-                return outputs[0].get('raw', str(result))
+                return outputs[0].get("raw", str(result))
 
         # Fallback to string representation if we can't extract the raw response
         return str(result)
@@ -204,10 +196,7 @@ def get_agent_response(user_input):
 def speak_response(text):
     print(f"🤖 Agent: {text}")
     audio = tts_client.text_to_speech.convert(
-        text=text,
-        voice_id="JBFqnCBsd6RMkjVDRZzb",
-        model_id="eleven_multilingual_v2",
-        output_format="mp3_44100_128"
+        text=text, voice_id="JBFqnCBsd6RMkjVDRZzb", model_id="eleven_multilingual_v2", output_format="mp3_44100_128"
     )
     play(audio)
 
@@ -220,7 +209,7 @@ def run_voice_agent():
             record_audio(tmp_audio.name)
             try:
                 user_text = transcribe_whisper(tmp_audio.name)
-                if user_text.lower() in ['exit', 'quit', 'stop']:
+                if user_text.lower() in ["exit", "quit", "stop"]:
                     print("👋 Exiting.")
                     break
                 response = get_agent_response(user_text)