Agno

pip install agno supermemory python-dotenv 

# .env SUPERMEMORY_API_KEY=your-supermemory-api-key OPENAI_API_KEY=your-openai-api-key 

from agno.agent import Agent from agno.models.openai import OpenAIChat from supermemory import Supermemory from dotenv import load_dotenv  load_dotenv()  memory = Supermemory()  def get_user_context(user_id: str, query: str) -> str:  """Pull user profile and relevant memories."""  result = memory.profile(container_tag=user_id, q=query)   static = result.profile.static or []  dynamic = result.profile.dynamic or []  memories = result.search_results.results if result.search_results else []   return f""" User background: {chr(10).join(static) if static else 'No profile yet.'}  Recent activity: {chr(10).join(dynamic) if dynamic else 'Nothing recent.'}  Related memories: {chr(10).join([m.memory or m.chunk for m in memories[:5]]) if memories else 'None.'} """  def create_agent(user_id: str, task: str) -> Agent:  """Create an agent with user context."""  context = get_user_context(user_id, task)   return Agent(  name="assistant",  model=OpenAIChat(id="gpt-4o"),  description=f"""You are a helpful assistant.  Here's what you know about this user: {context}  Use this to personalize your responses.""",  markdown=True  )  def chat(user_id: str, message: str) -> str:  """Run the agent and store the interaction."""  agent = create_agent(user_id, message)  response = agent.run(message)   # Save for next time  memory.add(  content=f"User: {message}\nAssistant: {response.content}",  container_tag=user_id  )   return response.content 

result = memory.profile(  container_tag="user_123",  q="cooking help" # Also returns relevant memories )  print(result.profile.static) # ["Vegetarian", "Allergic to nuts"] print(result.profile.dynamic) # ["Learning Italian cuisine", "Meal prepping"] 

def store_chat(user_id: str, user_msg: str, agent_response: str):  memory.add(  content=f"User asked: {user_msg}\nAgent said: {agent_response}",  container_tag=user_id,  metadata={"type": "conversation"}  ) 

results = memory.search.memories(  q="pasta recipes we discussed",  container_tag="user_123",  search_mode="hybrid",  limit=5 )  for r in results.results:  print(r.memory or r.chunk) 

from agno.agent import Agent from agno.models.openai import OpenAIChat from supermemory import Supermemory from dotenv import load_dotenv  load_dotenv()  class PersonalAssistant:  def __init__(self):  self.memory = Supermemory()   def get_context(self, user_id: str, query: str) -> dict:  """Fetch user profile and relevant history."""  result = self.memory.profile(  container_tag=user_id,  q=query,  threshold=0.5  )   return {  "profile": result.profile.static or [],  "recent": result.profile.dynamic or [],  "history": [m.memory for m in (result.search_results.results or [])[:3]]  }   def build_description(self, context: dict) -> str:  """Turn context into agent description."""  parts = ["You are a helpful personal assistant."]   if context["profile"]:  parts.append(f"About this user: {', '.join(context['profile'])}")   if context["recent"]:  parts.append(f"They're currently: {', '.join(context['recent'])}")   if context["history"]:  parts.append(f"Past conversations: {'; '.join(context['history'])}")   parts.append("Reference what you know about them when relevant.")   return "\n\n".join(parts)   def create_agent(self, context: dict) -> Agent:  return Agent(  name="assistant",  model=OpenAIChat(id="gpt-4o"),  description=self.build_description(context),  markdown=True  )   def chat(self, user_id: str, message: str) -> str:  """Handle a message and remember the interaction."""  context = self.get_context(user_id, message)  agent = self.create_agent(context)   response = agent.run(message)   # Store for future sessions  self.memory.add(  content=f"User: {message}\nAssistant: {response.content}",  container_tag=user_id,  metadata={"type": "chat"}  )   return response.content   def teach(self, user_id: str, fact: str):  """Store a preference or fact about the user."""  self.memory.add(  content=fact,  container_tag=user_id,  metadata={"type": "preference"}  )   if __name__ == "__main__":  assistant = PersonalAssistant()   # Teach it some preferences  assistant.teach("user_1", "Prefers concise answers")  assistant.teach("user_1", "Works in software engineering")   # Chat  response = assistant.chat("user_1", "What's a good way to learn Rust?")  print(response) 

from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.tools import tool from supermemory import Supermemory  memory = Supermemory()  @tool def search_memory(query: str, user_id: str) -> str:  """Search for information in the user's memory.   Args:  query: What to look for  user_id: The user's ID  """  results = memory.search.memories(  q=query,  container_tag=user_id,  limit=5  )   if not results.results:  return "Nothing relevant found in memory."   return "\n".join([r.memory or r.chunk for r in results.results])  @tool def remember(content: str, user_id: str) -> str:  """Store something important about the user.   Args:  content: What to remember  user_id: The user's ID  """  memory.add(content=content, container_tag=user_id)  return f"Remembered: {content}"  agent = Agent(  name="memory_agent",  model=OpenAIChat(id="gpt-4o"),  tools=[search_memory, remember],  description="""You are an assistant with memory.  When users share preferences or important info, use the remember tool. When they ask about past conversations, search your memory first.""",  markdown=True ) 

from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.media import Image from pathlib import Path from supermemory import Supermemory  memory = Supermemory()  def analyze_and_remember(user_id: str, image_path: str, question: str) -> str:  """Analyze an image, answer a question, and store the context."""   agent = Agent(  name="vision_agent",  model=OpenAIChat(id="gpt-4o"),  description="You analyze images and answer questions about them.",  markdown=True  )   # Get the agent's analysis  response = agent.run(question, images=[Image(filepath=Path(image_path))])   # Store the interaction with image context  memory.add(  content=f"User shared an image and asked: {question}\nAnalysis: {response.content}",  container_tag=user_id,  metadata={"type": "image_analysis", "image": image_path}  )   return response.content 

# Store with metadata memory.add(  content="User prefers dark mode interfaces",  container_tag="user_123",  metadata={  "type": "preference",  "category": "ui",  "source": "onboarding"  } )  # Search with filters results = memory.search.memories(  q="interface preferences",  container_tag="user_123",  filters={  "AND": [  {"key": "type", "value": "preference"},  {"key": "category", "value": "ui"}  ]  } )