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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.anthropic.claude import Claude
from agno.models.openai import OpenAIChat
from agno.os.app import AgentOS
from agno.os.interfaces.slack import Slack
from agno.tools.websearch import WebSearchTools

agent_db = SqliteDb(session_table="agent_sessions", 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 users likes and dislikes,
                    Collect information about what the user is doing with their life right now
                """,
    model=OpenAIChat(id="gpt-4o-mini"),
)


personal_agent = Agent(
    name="Basic Agent",
    model=Claude(id="claude-sonnet-4-20250514"),
    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,
    update_memory_on_run=True,
    instructions=dedent("""
        You are a personal AI friend in a Slack chat. Your purpose is to chat with the user 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 the web search tool to find the latest information about things in the conversation.
        You may sometimes receive messages prepended with "group message" — when that happens, reply to the whole group instead of treating them as from a single user.
                        """),
)


# Setup our AgentOS app
agent_os = AgentOS(
    agents=[personal_agent],
    interfaces=[Slack(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 SLACK_TOKEN=***
export SLACK_SIGNING_SECRET=***
export ANTHROPIC_API_KEY=***
3

Install Dependencies

uv pip install 'agno[slack]'
4

Run Example

python agent_with_user_memory.py

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Key Features

  • Memory Management: Captures user names, hobbies, preferences, and activities across conversations
  • Web Search: Fetches current information during conversations via WebSearchTools
  • Personalized Responses: Uses stored memories for contextualized replies