from agno.agent import Agent from agno.memory.v2.db.sqlite import SqliteMemoryDb from agno.memory.v2.memory import Memory from agno.models.nebius import Nebius from agno.storage.sqlite import SqliteStorage from rich.pretty import pprint import os from dotenv import load_dotenv load_dotenv() # UserId for the memories user_id = "arindam" # Database file for memory and storage db_file = "tmp/agent.db" # Initialize memory.v2 memory = Memory( # Use any model for creating memories model=Nebius( id="deepseek-ai/DeepSeek-V3-0324", api_key=os.getenv("NEBIUS_API_KEY") ), db=SqliteMemoryDb(table_name="user_memories", db_file=db_file), ) # Initialize storage storage = SqliteStorage(table_name="agent_sessions", db_file=db_file) # Initialize Agent memory_agent = Agent( model=Nebius( id="deepseek-ai/DeepSeek-V3-0324", api_key=os.getenv("NEBIUS_API_KEY") ), # Store memories in a database memory=memory, # Give the Agent the ability to update memories enable_agentic_memory=True, # OR - Run the MemoryManager after each response enable_user_memories=True, # Store the chat history in the database storage=storage, # Add the chat history to the messages add_history_to_messages=True, # Number of history runs num_history_runs=3, markdown=True, ) memory.clear() memory_agent.print_response( "My name is Arindam and I support Mohun Bagan.", user_id=user_id, stream=True, stream_intermediate_steps=True, ) print("Memories about Arindam:") pprint(memory.get_user_memories(user_id=user_id)) memory_agent.print_response( "I live in Kolkata, where should i move within a 4 hour drive?", user_id=user_id, stream=True, stream_intermediate_steps=True, ) print("Memories about Arindam:") pprint(memory.get_user_memories(user_id=user_id)) memory_agent.print_response( "Tell me about Arindam", user_id=user_id, stream=True, stream_intermediate_steps=True, ) print("Memories about Arindam:") pprint(memory.get_user_memories(user_id=user_id))