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.

1. 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()],
       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,
   )
   
2. Set up your virtual environment

   Mac

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

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U agno openai yfinance sqlalchemy
   
4. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. 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