"""
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-flash-preview"),
instructions=instructions,
tools=[YFinanceTools()],
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.
"""
