Async Postgres for Agent

Agno supports using PostgreSQL asynchronously, with the AsyncPostgresDb class.

Usage

Run PgVector

Install docker desktop and run PgVector on port 5532 using:

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:16

async_postgres_for_agent.py

import asyncio

from agno.agent import Agent
from agno.db.async_postgres import AsyncPostgresDb
from agno.tools.duckduckgo import DuckDuckGoTools

db_url = "postgresql+psycopg_async://ai:ai@localhost:5532/ai"
db = AsyncPostgresDb(db_url=db_url)

agent = Agent(
    db=db,
    tools=[DuckDuckGoTools()],
    add_history_to_context=True,
    add_datetime_to_context=True,
)


asyncio.run(agent.aprint_response("How many people live in Canada?"))
asyncio.run(agent.aprint_response("What is their national anthem called?"))

Params

Parameter	Type	Default	Description
db_id	Optional[str]	-	The ID of the database instance. UUID by default.
db_url	Optional[str]	-	The database URL to connect to.
db_engine	Optional[AsyncEngine]	-	The SQLAlchemy asyncdatabase engine to use.
db_schema	Optional[str]	-	The database schema to use.
session_table	Optional[str]	-	Name of the table to store Agent, Team and Workflow sessions.
memory_table	Optional[str]	-	Name of the table to store memories.
metrics_table	Optional[str]	-	Name of the table to store metrics.
eval_table	Optional[str]	-	Name of the table to store evaluation runs data.
knowledge_table	Optional[str]	-	Name of the table to store knowledge content.

Developer Resources

• View Cookbook