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Code

agent_with_user_memory.py
from textwrap import dedent

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.memory.manager import MemoryManager
from agno.models.google import Gemini
from agno.os.app import AgentOS
from agno.os.interfaces.telegram import Telegram
from agno.tools.websearch import WebSearchTools

agent_db = SqliteDb(db_file="tmp/persistent_memory.db")

memory_manager = MemoryManager(
    memory_capture_instructions=(
        "Collect User's name,"
        "Collect Information about user's passion and hobbies,"
        "Collect Information about the user's likes and dislikes,"
        "Collect information about what the user is doing with their life right now"
    ),
    model=Gemini(id="gemini-2.0-flash"),
)


personal_agent = Agent(
    name="Basic Agent",
    model=Gemini(id="gemini-2.0-flash"),
    tools=[WebSearchTools()],
    add_history_to_context=True,
    num_history_runs=3,
    add_datetime_to_context=True,
    markdown=True,
    db=agent_db,
    memory_manager=memory_manager,
    enable_agentic_memory=True,
    instructions=dedent("""
        You are a personal AI friend of the user, your purpose is to chat with the user about things and make them feel good.
        First introduce yourself and ask for their name then, ask about themselves, their hobbies, what they like to do and what they like to talk about.
        Use web search to find latest information about things in the conversations
    """),
)


agent_os = AgentOS(
    agents=[personal_agent],
    interfaces=[Telegram(agent=personal_agent)],
)
app = agent_os.get_app()

if __name__ == "__main__":
    agent_os.serve(app="agent_with_user_memory:app", reload=True)

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Usage

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
2

Set Environment Variables

export TELEGRAM_TOKEN=your-bot-token-from-botfather
export GOOGLE_API_KEY=your-google-api-key
export APP_ENV=development
3

Install dependencies

uv pip install -U "agno[telegram]"
4

Run Example

python agent_with_user_memory.py

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Key Features

  • Agentic Memory: MemoryManager captures user preferences, hobbies, and personal details
  • Cross-Session Recall: Remembers user information across conversations
  • Web Search: Uses WebSearchTools to find up-to-date information during conversations
  • Persistent Storage: SQLite database for both sessions and memory