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

# Agent with Storage

> Store WatsonX agent sessions and history in Postgres.

## Code

```python db.py theme={null}
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.ibm import WatsonX
from agno.tools.websearch import WebSearchTools

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

agent = Agent(
    model=WatsonX(id="mistralai/mistral-small-3-1-24b-instruct-2503"),
    db=db,
    tools=[WebSearchTools()],
    add_history_to_context=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")
```

## Usage

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

  <Step title="Set your API key">
    ```bash theme={null}
    export IBM_WATSONX_API_KEY=xxx
    export IBM_WATSONX_PROJECT_ID=xxx
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U ddgs psycopg sqlalchemy ibm-watsonx-ai agno
    ```
  </Step>

  <Step title="Set up PostgreSQL">
    Make sure you have a PostgreSQL database running. You can adjust the `db_url` in the code to match your database configuration.
  </Step>

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

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

PostgreSQL storage maintains conversation state across multiple interactions. The example creates an IBM WatsonX agent with a PostgreSQL storage backend and sends multiple messages, preserving the conversation history between them.