> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/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

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

**1. Set your API key**

```bash theme={null}
export IBM_WATSONX_API_KEY=xxx
export IBM_WATSONX_PROJECT_ID=xxx
```

**2. Install dependencies**

```bash theme={null}
uv pip install -U ddgs "psycopg[binary]" sqlalchemy ibm-watsonx-ai agno
```

**3. 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.

**4. Run Agent**

Save the code above as `db.py`, then run:

```bash theme={null}
python db.py
```

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.


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