AsyncMySQLDb stores a Team’s sessions and run history asynchronously in MySQL. Use async Team methods such as arun() and aprint_response().
Usage
Install thesqlalchemy, asyncmy, openai, and ddgs packages:
uv pip install sqlalchemy asyncmy openai ddgs
Run MySQL
Install Docker Desktop, then start MySQL on port3306:
docker run -d \
--name mysql \
-e MYSQL_ROOT_PASSWORD=ai \
-e MYSQL_DATABASE=ai \
-e MYSQL_USER=ai \
-e MYSQL_PASSWORD=ai \
-p 3306:3306 \
mysql:8
async_mysql_for_team.py
import asyncio
import uuid
from typing import List
from agno.agent import Agent
from agno.db.base import SessionType
from agno.db.mysql import AsyncMySQLDb
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from pydantic import BaseModel
db_url = "mysql+asyncmy://ai:ai@localhost:3306/ai"
db = AsyncMySQLDb(db_url=db_url)
class Article(BaseModel):
title: str
summary: str
reference_links: List[str]
hn_researcher = Agent(
name="HackerNews Researcher",
role="Gets top stories from HackerNews.",
tools=[HackerNewsTools()],
)
web_searcher = Agent(
name="Web Searcher",
role="Searches the web for information on a topic",
tools=[WebSearchTools()],
add_datetime_to_context=True,
)
hn_team = Team(
name="HackerNews Team",
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, summarize each story and include its reference links.",
],
output_schema=Article,
markdown=True,
show_members_responses=True,
)
async def main():
try:
session_id = str(uuid.uuid4())
await hn_team.aprint_response(
"Write an article about the top 2 stories on HackerNews",
session_id=session_id,
)
session_data = await db.get_session(
session_id=session_id, session_type=SessionType.TEAM
)
print("\n=== SESSION DATA ===")
print(session_data.to_dict())
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. |
create_schema | bool | True | Whether to create the database schema if it doesn't exist. Set to False when the schema is managed externally. |