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

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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_workflow.py
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 agno.workflow.step import Step
from agno.workflow.workflow import Workflow

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

# Define agents
hackernews_agent = Agent(
    name="Hackernews Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from Hackernews posts",
)
web_agent = Agent(
    name="Web Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools()],
    role="Search the web for the latest news and trends",
)

# Define research team for complex analysis
research_team = Team(
    name="Research Team",
    members=[hackernews_agent, web_agent],
    instructions="Research tech topics from Hackernews and the web",
)

content_planner = Agent(
    name="Content Planner",
    model=OpenAIChat(id="gpt-5-mini"),
    instructions=[
        "Plan a content schedule over 4 weeks for the provided topic and research content",
        "Ensure that I have posts for 3 posts per week",
    ],
)

# Define steps
research_step = Step(
    name="Research Step",
    team=research_team,
)

content_planning_step = Step(
    name="Content Planning Step",
    agent=content_planner,
)

# Create and use workflow
if __name__ == "__main__":
    content_creation_workflow = Workflow(
        name="Content Creation Workflow",
        description="Automated content creation from blog posts to social media",
        db=PostgresDb(
            session_table="workflow_session",
            db_url=db_url,
        ),
        steps=[research_step, content_planning_step],
    )
    content_creation_workflow.print_response(
        input="AI trends in 2024",
        markdown=True,
    )

​
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.

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Developer Resources