Agent with Memory

Code

cookbook/11_models/meta/llama/memory.py

from agno.agent import Agent
from agno.db.base import SessionType
from agno.db.postgres import PostgresDb
from agno.models.meta import Llama
from rich.pretty import pprint

# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)

agent = Agent(
    model=Llama(id="Llama-4-Maverick-17B-128E-Instruct-FP8"),
    user_id="test_user",
    session_id="test_session",
    # Pass the database to the Agent
    db=db,
    # Enable user memories
    update_memory_on_run=True,
    # Enable session summaries
    enable_session_summaries=True,
    # Show debug logs so, you can see the memory being created
    debug_mode=True,
)

# -*- Share personal information
agent.print_response("My name is John Billings", stream=True)

# -*- Print memories and session summary
if agent.db:
    pprint(agent.get_user_memories(user_id="test_user"))
    pprint(
        agent.db.get_session(
            session_id="test_session", session_type=SessionType.AGENT
        ).summary  # type: ignore
    )

# -*- Share personal information
agent.print_response("I live in NYC", stream=True)
# -*- Print memories and session summary
if agent.db:
    pprint(agent.db.get_user_memories(user_id="test_user"))
    pprint(
        agent.db.get_session(
            session_id="test_session", session_type=SessionType.AGENT
        ).summary  # type: ignore
    )

# Ask about the conversation
agent.print_response(
    "What have we been talking about, do you know my name?", stream=True
)

Usage

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   
2. Set your LLAMA API key

   export LLAMA_API_KEY=YOUR_API_KEY
   
3. Install dependencies

   uv pip install openai sqlalchemy psycopg pgvector llama-api-client
   
4. Run Agent

   python python cookbook/11_models/meta/llama/memory.py