MySQLDb class.
Usage
Install theagno, sqlalchemy, pymysql, openai, and ddgs packages:
uv pip install agno sqlalchemy pymysql openai ddgs
Set OpenAI Key
Set yourOPENAI_API_KEY as an environment variable. You can get one from OpenAI.
export OPENAI_API_KEY=sk-***
setx OPENAI_API_KEY sk-***
Run MySQL
Install docker desktop and run MySQL on port 3306 using: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
mysql_for_team.py
from typing import List
from agno.agent import Agent
from agno.db.mysql import MySQLDb
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from pydantic import BaseModel
# MySQL connection settings
db_url = "mysql+pymysql://ai:ai@localhost:3306/ai"
db = MySQLDb(db_url=db_url)
class Article(BaseModel):
title: str
summary: str
reference_links: List[str]
hn_researcher = Agent(
name="HackerNews Researcher",
model=OpenAIChat("gpt-4o"),
role="Gets top stories from hackernews.",
tools=[HackerNewsTools()],
)
web_searcher = Agent(
name="Web Searcher",
model=OpenAIChat("gpt-4o"),
role="Searches the web for information on a topic",
tools=[WebSearchTools()],
add_datetime_to_context=True,
)
hn_team = Team(
name="HackerNews Team",
model=OpenAIChat("gpt-4o"),
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, provide a thoughtful and engaging summary.",
],
output_schema=Article,
markdown=True,
show_members_responses=True,
add_member_tools_to_context=False,
)
if __name__ == "__main__":
hn_team.print_response("Write an article about the top 2 stories on hackernews")
Params
| Parameter | Type | Default | Description |
|---|---|---|---|
id | Optional[str] | None | Database ID. Derived from the connection and schema when omitted. |
db_engine | Optional[Engine] | None | SQLAlchemy database engine. Provide this or db_url. |
db_schema | Optional[str] | None | Database schema. Uses "ai" when omitted. |
db_url | Optional[str] | None | Database URL. Provide this or db_engine. |
session_table | Optional[str] | None | Table for Agent, Team, and Workflow sessions. Uses "agno_sessions" when omitted. |
memory_table | Optional[str] | None | Table for memories. Uses "agno_memories" when omitted. |
metrics_table | Optional[str] | None | Table for metrics. Uses "agno_metrics" when omitted. |
eval_table | Optional[str] | None | Table for evaluation run data. Uses "agno_eval_runs" when omitted. |
knowledge_table | Optional[str] | None | Table for knowledge content. Uses "agno_knowledge" when omitted. |
culture_table | Optional[str] | None | Table for cultural knowledge. Uses "agno_culture" when omitted. |
traces_table | Optional[str] | None | Table for traces. Uses "agno_traces" when omitted. |
spans_table | Optional[str] | None | Table for spans. Uses "agno_spans" when omitted. |
versions_table | Optional[str] | None | Table for schema versions. Uses "agno_schema_versions" when omitted. |
create_schema | bool | True | Create the database schema when it does not exist. Set to False when migrations manage it. |