Documentation Index
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A data agent that only runs in a notebook helps one analyst. Behind an API, it answers questions from a Slack channel, a BI dashboard’s natural-language box, or an “explain this metric” widget in your product. AgentOS turns the agent into that API.
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS
from agno.tools.sql import SQLTools
agent = Agent(
id="data-agent",
model=OpenAIResponses(id="gpt-5.5"),
db=PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"),
tools=[SQLTools(db_url="postgresql+psycopg://readonly@warehouse/analytics")],
learning=True,
)
agent_os = AgentOS(agents=[agent])
app = agent_os.get_app()
if __name__ == "__main__":
agent_os.serve(app="data_agent:app", port=7777)
curl -X POST http://localhost:7777/agents/data-agent/runs \
-F 'message=MRR by plan, last 6 months' \
-F 'user_id=ab@acme.com' \
-F 'session_id=q4-review' \
-F 'stream=false'
Surfaces a data agent lives on
| Surface | Shape |
|---|
| Slack channel | An interface maps a thread to a session; the team asks in plain English |
| Dashboard NL box | A widget posts the question and renders the answer plus its SQL |
| Scheduled digest | A cron job runs the agent and posts “yesterday’s numbers” every morning |
| Backend check | A pipeline calls the agent to sanity-check a metric before publishing |
Learnings are shared, sessions are not
A data agent’s value compounds when corrections are shared across the team. Scope conversation threads per user with session_id, but keep learnings in a shared namespace so a fix one analyst’s question triggered helps everyone.
| State | Scope |
|---|
| Conversation thread | Per user_id and session_id |
| Learnings about the warehouse | Shared namespace across users |
Next steps
| Task | Guide |
|---|
| Add Slack or a browser surface | Interfaces |
| Lock down the endpoints | Security and auth |
Developer Resources
