Agno supports using PostgreSQL as a storage backend for Teams using the PostgresDb class.

​
Usage

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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
postgres_for_team.py
"""
Run: `pip install openai ddgs newspaper4k lxml_html_clean agno` to install the dependencies
"""

from typing import List

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.hackernews import HackerNewsTools
from pydantic import BaseModel

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


class Article(BaseModel):
    title: str
    summary: str
    reference_links: List[str]


hn_researcher = Agent(
    name="HackerNews Researcher",
    model=OpenAIChat("gpt-5-mini"),
    role="Gets top stories from hackernews.",
    tools=[HackerNewsTools()],
)

web_searcher = Agent(
    name="Web Searcher",
    model=OpenAIChat("gpt-5-mini"),
    role="Searches the web for information on a topic",
    tools=[DuckDuckGoTools()],
    add_datetime_to_context=True,
)


hn_team = Team(
    name="HackerNews Team",
    model=OpenAIChat("gpt-5-mini"),
    members=[hn_researcher, web_searcher],
    db=db,
    instructions=[
        "First, search hackernews for what the user is asking about.",
        "Then, ask the web searcher to search for each story to get more information.",
        "Finally, provide a thoughtful and engaging summary.",
    ],
    output_schema=Article,
    markdown=True,
    show_members_responses=True,
)

hn_team.print_response("Write an article about the top 2 stories on hackernews")

​
Params

ParameterTypeDefaultDescription
db_urlOptional[str]-The database URL to connect to.
db_engineOptional[Engine]-The SQLAlchemy database engine to use.
db_schemaOptional[str]-The database schema to use.
session_tableOptional[str]-Name of the table to store Agent, Team and Workflow sessions.
memory_tableOptional[str]-Name of the table to store memories.
metrics_tableOptional[str]-Name of the table to store metrics.
eval_tableOptional[str]-Name of the table to store evaluation runs data.
knowledge_tableOptional[str]-Name of the table to store knowledge content.

​
Developer Resources