> ## 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 an Ollama llama3.1:8b agent's history in Postgres with WebSearchTools and add_history_to_context for multi-turn context.

## Code

```python db.py theme={null}
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.ollama import Ollama
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=Ollama(id="llama3.1:8b"),
    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. Install Ollama**

Follow the [installation guide](https://github.com/ollama/ollama?tab=readme-ov-file#macos) and run:

```bash theme={null}
ollama pull llama3.1:8b
```

**2. Install dependencies**

```bash theme={null}
uv pip install -U ddgs sqlalchemy psycopg ollama agno
```

**3. Run PgVector**

```bash theme={null}
docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18
```

**4. Run Agent**

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

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


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