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

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(host="http://localhost:11434", api_key=None, 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

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Start the local Ollama service

Install Ollama and start its desktop app or service on http://localhost:11434. If you start it manually with ollama serve, keep that process running in a separate terminal.

In the terminal where you will pull models and run Python, select that server and clear the direct-cloud key. The native Ollama client also reads this key independently of Agno.

export OLLAMA_HOST=http://localhost:11434
unset OLLAMA_API_KEY

Pull the model

ollama pull llama3.1:8b

Install dependencies

uv pip install -U ddgs sqlalchemy "psycopg[binary]" ollama agno

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18

Run Agent

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

python db.py