AI Foundry DB

Persist a classic Azure AI Inference session in Postgres and test history with tool-free prompts.

This source uses Agno's classic AzureAIFoundry adapter and the azure-ai-inference package, which Microsoft retired on August 26, 2026. Existing endpoint availability is separate from SDK retirement. For a new integration, use the current Foundry API setup with a compatible deployment. The classic setup below applies only to an existing compatible endpoint unless a current adaptation is explicitly provided.

The source enables web search on Phi-4, but Microsoft's partner model catalog lists no tool-calling support for that model. For this history example, remove the toolkit and use the two continuity prompts in the run step.

db.py
"""Run `uv pip install ddgs sqlalchemy anthropic` to install dependencies."""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.azure import AzureAIFoundry
from agno.tools.websearch import WebSearchTools

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

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

agent = Agent(
    model=AzureAIFoundry(id="Phi-4"),
    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?")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

Run the Classic Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno "psycopg[binary]" aiohttp azure-ai-inference ddgs sqlalchemy

Export environment variables

export AZURE_API_KEY="your_azure_api_key_here"
export AZURE_ENDPOINT="your_azure_endpoint_here"

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 the example

Save the code above as db.py. Remove the WebSearchTools import and tools=[WebSearchTools()], argument. Replace the two run calls with:

agent.print_response("Remember this project name: Aurora.")
agent.print_response("What project name did I just give you?")

Keep db=db and add_history_to_context=True, then run:

python db.py

Full source: cookbook/90_models/azure/ai_foundry/db.py