Async SQLite for Workflow

Agno supports using SQLite asynchronously as a storage backend for Workflows, with the AsyncSqliteDb class.

Usage

async_sqlite_for_workflow.py

"""
Run: `uv pip install openai sqlalchemy aiosqlite` to install dependencies
"""

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.hackernews import HackerNewsTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow

# Initialize AsyncSqliteDb with a database file
db = AsyncSqliteDb(db_file="workflow_storage.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=[HackerNewsTools()],
    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],
)

if __name__ == "__main__":
    asyncio.run(
        content_creation_workflow.aprint_response(
            input="AI trends in 2024",
            markdown=True,
        )
    )

Params

Parameter	Type	Default	Description
db_engine	Optional[Engine]	-	The SQLAlchemy 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.
traces_table	Optional[str]	-	Name of the table to store traces.
spans_table	Optional[str]	-	Name of the table to store spans.