> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent With User Memory

> Slack personal assistant that captures names, hobbies, and preferences with MemoryManager and recalls them in later chats.

A personal assistant that remembers users across conversations using `MemoryManager`. The agent captures names, hobbies, and preferences, then uses that context in future chats.

<Warning>
  Anthropic retired the pinned `claude-sonnet-4-20250514` model on June 15, 2026. Replace it with `claude-sonnet-4-6` before running. See [Anthropic model deprecations](https://platform.claude.com/docs/en/about-claude/model-deprecations).
</Warning>

```python agent_with_user_memory.py theme={null}
"""
Agent With User Memory
======================

A personal assistant that remembers users across conversations using
``MemoryManager``. The agent captures names, hobbies, and preferences,
then uses that context in future chats.

Key concepts:
  - ``MemoryManager`` extracts and stores user facts after each run.
  - ``update_memory_on_run=True`` triggers automatic memory capture.
  - ``memory_capture_instructions`` tell the manager what to look for.

Slack scopes: app_mentions:read, assistant:write, chat:write, im:history
"""

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

# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------

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()


# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    """Run your AgentOS.

    You can see the configuration and available apps at:
    http://localhost:7777/config

    """
    agent_os.serve(app="agent_with_user_memory:app", reload=True)
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U "agno[os,slack]" anthropic ddgs openai
    ```
  </Step>

  <Step title="Export environment variables">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
      export OPENAI_API_KEY="your_openai_api_key_here"
      export SLACK_SIGNING_SECRET="your_slack_signing_secret_here"
      export SLACK_TOKEN="your_slack_token_here"
      ```

      ```bash Windows theme={null}
      $Env:ANTHROPIC_API_KEY="your_anthropic_api_key_here"
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      $Env:SLACK_SIGNING_SECRET="your_slack_signing_secret_here"
      $Env:SLACK_TOKEN="your_slack_token_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Update the Claude model">
    Replace `Claude(id="claude-sonnet-4-20250514")` with `Claude(id="claude-sonnet-4-6")` in the saved file.
  </Step>

  <Step title="Configure Slack">
    Complete [Slack setup](/agent-os/interfaces/slack/setup): create and install the app, expose the server through public HTTPS, and add the scopes listed in the example. The default event request URL is `<public-url>/slack/events`; HITL examples also use `<public-url>/slack/interactions` for interactivity. Use any custom prefix shown in the example instead of `/slack`.
  </Step>

  <Step title="Run the example">
    Save the code above as `agent_with_user_memory.py`, then run:

    ```bash theme={null}
    python agent_with_user_memory.py
    ```
  </Step>
</Steps>

Full source: [cookbook/05\_agent\_os/interfaces/slack/agent\_with\_user\_memory.py](https://github.com/agno-agi/agno/blob/main/cookbook/05_agent_os/interfaces/slack/agent_with_user_memory.py)