Code
cookbook/models/ibm/watsonx/storage.py
from agno.agent import Agent
from agno.models.ibm import WatsonX
from agno.storage.postgres import PostgresStorage
from agno.tools.duckduckgo import DuckDuckGoTools
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
agent = Agent(
model=WatsonX(id="ibm/granite-20b-code-instruct"),
storage=PostgresStorage(table_name="agent_sessions", db_url=db_url),
tools=[DuckDuckGoTools()],
add_history_to_messages=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")
Usage
Create a virtual environment
Open the Terminal
and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
Set your API key
export IBM_WATSONX_API_KEY=xxx
export IBM_WATSONX_PROJECT_ID=xxx
Install libraries
pip install -U ibm-watsonx-ai sqlalchemy psycopg duckduckgo-search agno
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.
Run Agent
python cookbook/models/ibm/watsonx/storage.py
This example shows how to use PostgreSQL storage with IBM WatsonX to maintain conversation state across multiple interactions. It creates an agent with a PostgreSQL storage backend and sends multiple messages, with the conversation history being preserved between them.
Note: You need to install the sqlalchemy
package and have a PostgreSQL database available.