> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Chat Memory

> Store user memories and session summaries in Postgres and print them after each turn.

<Warning>
  This example docstring points to the nonexistent `cookbook/agents/personalized_memories_and_summaries.py` path. Save this fence as `memory.py` and use the generated run step below.
</Warning>

```python memory.py theme={null}
"""
This recipe shows how to use personalized memories and summaries in an agent.
Steps:
1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
2. Run: `uv pip install openai sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
3. Run: `python cookbook/agents/personalized_memories_and_summaries.py` to run the agent
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from rich.pretty import pprint

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    user_id="test_user",
    session_id="test_session",
    # Pass the database to the Agent
    db=db,
    # Enable user memories
    update_memory_on_run=True,
    # Enable session summaries
    enable_session_summaries=True,
    # Show debug logs so, you can see the memory being created
)

# -*- Share personal information
agent.print_response("My name is john billings", stream=True)
# -*- Print memories and summary
if agent.db:
    pprint(agent.get_user_memories(user_id="test_user"))
    pprint(
        agent.get_session(session_id="test_session").summary  # type: ignore
    )

# -*- Share personal information
agent.print_response("I live in nyc", stream=True)
# -*- Print memories and summary
if agent.db:
    pprint(agent.get_user_memories(user_id="test_user"))
    pprint(
        agent.get_session(session_id="test_session").summary  # type: ignore
    )

# -*- Share personal information
agent.print_response("I'm going to a concert tomorrow", stream=True)
# -*- Print memories and summary
if agent.db:
    pprint(agent.get_user_memories(user_id="test_user"))
    pprint(
        agent.get_session(session_id="test_session").summary  # type: ignore
    )

# Ask about the conversation
agent.print_response(
    "What have we been talking about, do you know my name?", stream=True
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno "psycopg[binary]" openai sqlalchemy
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

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

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

Full source: [cookbook/90\_models/openai/chat/memory.py](https://github.com/agno-agi/agno/blob/v2.7.4/cookbook/90_models/openai/chat/memory.py)