agent_with_storage.py
"""
Agent with Storage - Finance Agent with Storage
====================================================
Building on the Finance Agent from 01, this example adds persistent storage.
Your agent now remembers conversations across runs.
Ask about NVDA, close the script, come back later — pick up where you left off.
The conversation history is saved to SQLite and restored automatically.
Key concepts:
- Run: Each time you run the agent (via agent.print_response() or agent.run())
- Session: A conversation thread, identified by session_id
- Same session_id = continuous conversation, even across runs
Example prompts to try:
- "What's the current price of AAPL?"
- "Compare that to Microsoft" (it remembers AAPL)
- "Based on our discussion, which looks better?"
- "What stocks have we analyzed so far?"
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.google import Gemini
from agno.tools.yfinance import YFinanceTools
# ---------------------------------------------------------------------------
# Storage Configuration
# ---------------------------------------------------------------------------
agent_db = SqliteDb(db_file="tmp/agents.db")
# ---------------------------------------------------------------------------
# Agent Instructions
# ---------------------------------------------------------------------------
instructions = """\
You are a Finance Agent — a data-driven analyst who retrieves market data,
computes key ratios, and produces concise, decision-ready insights.
## Workflow
1. Clarify
- Identify tickers from company names (e.g., Apple → AAPL)
- If ambiguous, ask
2. Retrieve
- Fetch: price, change %, market cap, P/E, EPS, 52-week range
- For comparisons, pull the same fields for each ticker
3. Analyze
- Compute ratios (P/E, P/S, margins) when not already provided
- Key drivers and risks — 2-3 bullets max
- Facts only, no speculation
4. Present
- Lead with a one-line summary
- Use tables for multi-stock comparisons
- Keep it tight
## Rules
- Source: Yahoo Finance. Always note the timestamp.
- Missing data? Say "N/A" and move on.
- No personalized advice — add disclaimer when relevant.
- No emojis.
- Reference previous analyses when relevant.\
"""
# ---------------------------------------------------------------------------
# Create the Agent
# ---------------------------------------------------------------------------
agent_with_storage = Agent(
name="Agent with Storage",
model=Gemini(id="gemini-3.5-flash"),
instructions=instructions,
tools=[YFinanceTools(all=True)],
db=agent_db,
add_datetime_to_context=True,
add_history_to_context=True,
num_history_runs=5,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run the Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# Use a consistent session_id to persist conversation across runs
# Note: session_id is auto-generated if not set
session_id = "finance-agent-session"
# Turn 1: Analyze a stock
agent_with_storage.print_response(
"Give me a quick investment brief on NVIDIA",
session_id=session_id,
stream=True,
)
# Turn 2: Compare — the agent remembers NVDA from turn 1
agent_with_storage.print_response(
"Compare that to Tesla",
session_id=session_id,
stream=True,
)
# Turn 3: Ask for a recommendation based on the full conversation
agent_with_storage.print_response(
"Based on our discussion, which looks like the better investment?",
session_id=session_id,
stream=True,
)
# ---------------------------------------------------------------------------
# More Examples
# ---------------------------------------------------------------------------
"""
Try this flow:
1. Run the script — it analyzes NVDA, compares to TSLA, then recommends
2. Comment out all three prompts above
3. Add: agent.print_response("What about AMD?", session_id=session_id, stream=True)
4. Run again — it remembers the full NVDA vs TSLA conversation
The storage layer persists your conversation history to SQLite.
Restart the script anytime and pick up where you left off.
"""
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno google-genai sqlalchemy yfinance
3
Export your Google API key
export GOOGLE_API_KEY="your_google_api_key_here"
$Env:GOOGLE_API_KEY="your_google_api_key_here"
4
Run the example
Save the code above as
agent_with_storage.py, then run:python agent_with_storage.py