AsyncPostgresDb stores a Workflow’s sessions and run history asynchronously in PostgreSQL. Use async Workflow methods such as arun() and aprint_response().
Usage
Install thesqlalchemy, psycopg, openai, and ddgs packages:
uv pip install sqlalchemy psycopg openai ddgs
Run PostgreSQL
Install Docker Desktop, then start PostgreSQL with pgvector on port5532:
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
async_postgres_for_workflow.py
import asyncio
from agno.agent import Agent
from agno.db.postgres import AsyncPostgresDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow
db_url = "postgresql+psycopg_async://ai:ai@localhost:5532/ai"
db = AsyncPostgresDb(db_url=db_url)
hackernews_agent = Agent(
name="HackerNews Agent",
model=OpenAIResponses(id="gpt-5.2"),
tools=[HackerNewsTools()],
role="Extract key insights and content from HackerNews posts",
)
web_agent = Agent(
name="Web Agent",
model=OpenAIResponses(id="gpt-5.2"),
tools=[WebSearchTools()],
role="Search the web for the latest news and trends",
)
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=OpenAIResponses(id="gpt-5.2"),
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",
],
)
research_step = Step(
name="Research Step",
team=research_team,
)
content_planning_step = Step(
name="Content Planning Step",
agent=content_planner,
)
async def main():
content_creation_workflow = Workflow(
name="Content Creation Workflow",
description="Automated content creation from blog posts to social media",
db=db,
steps=[research_step, content_planning_step],
)
try:
await content_creation_workflow.aprint_response(
input="AI trends in 2024",
markdown=True,
)
finally:
await db.close()
if __name__ == "__main__":
asyncio.run(main())
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
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 async database 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. |
culture_table | Optional[str] | - | Name of the table to store cultural knowledge. |
traces_table | Optional[str] | - | Name of the table to store traces. |
spans_table | Optional[str] | - | Name of the table to store spans. |
versions_table | Optional[str] | - | Name of the table to store schema versions. |
learnings_table | Optional[str] | - | Name of the table to store learnings. |
schedules_table | Optional[str] | - | Name of the table to store cron schedules. |
schedule_runs_table | Optional[str] | - | Name of the table to store schedule run history. |
approvals_table | Optional[str] | - | Name of the table to store human approval requests. |
auth_tokens_table | Optional[str] | - | Name of the table to store OAuth tokens for external services. |
service_accounts_table | Optional[str] | - | Name of the table to store service accounts. |
create_schema | bool | True | Whether to create the database schema if it doesn't exist. Set to False when the schema is managed externally. |