AsyncSqliteDb stores a Workflow’s sessions and run history asynchronously in SQLite. Use async Workflow methods such as arun() and aprint_response().
Usage
Install thesqlalchemy asyncio extra, aiosqlite, openai, and ddgs:
uv pip install "sqlalchemy[asyncio]" aiosqlite openai ddgs
AsyncSqliteDb uses an async db_engine, then db_url, then db_file. A db_url must use an async driver such as sqlite+aiosqlite. If none is provided, it creates agno.db in the current directory. The following example uses db_file.
async_sqlite_for_workflow.py
import asyncio
from agno.agent import Agent
from agno.db.sqlite import AsyncSqliteDb
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 = AsyncSqliteDb(db_file="tmp/workflow.db")
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",
)
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_team = Team(
name="Research Team",
members=[hackernews_agent, web_agent],
instructions="Research tech topics from HackerNews and the web",
)
research_step = Step(
name="Research Step",
team=research_team,
)
content_planning_step = Step(
name="Content Planning Step",
agent=content_planner,
)
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],
)
async def main():
try:
await content_creation_workflow.aprint_response(
input="Recent AI trends",
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_engine | Optional[AsyncEngine] | - | The SQLAlchemy async database engine to use. |
db_url | Optional[str] | - | The database URL to connect to. |
db_file | Optional[str] | - | The database file to connect to. |
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 user 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 documents data. |
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. |