Agent with Storage

Persist conversation history across runs.

Storage lets your agent remember conversations. With the same session_id, it picks up where you left off, even after restarting.

Create a Python file

agent_with_storage.py
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.tools.yfinance import YFinanceTools

db = SqliteDb(db_file="tmp/agents.db")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools(all=True)],
    db=db,
    add_history_to_context=True,
    num_history_runs=5,
    markdown=True,
)

session_id = "finance-session"

# Turn 1: Analyze a stock
agent.print_response(
    "Give me a quick analysis of NVIDIA",
    session_id=session_id,
    stream=True,
)

# Turn 2: The agent remembers NVDA from turn 1
agent.print_response(
    "Compare that to AMD",
    session_id=session_id,
    stream=True,
)

# Turn 3: Ask based on full conversation
agent.print_response(
    "Which looks like the better investment?",
    session_id=session_id,
    stream=True,
)

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno openai yfinance sqlalchemy

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Run Agent

python agent_with_storage.py

Key Concepts

  • Session: A conversation thread identified by session_id
  • Same session_id = continuous conversation, even across script runs
  • add_history_to_context=True: Includes previous messages in context
  • num_history_runs=5: Number of previous runs to include