Team Learning: Async Mode

Demonstrates Team learning with async database operations.

team_async_learning.py
"""
Team Learning: Async Mode
=========================
Demonstrates Team learning with async database operations.

Uses AsyncPostgresDb for non-blocking database access.
"""

import asyncio

from agno.agent import Agent
from agno.db.postgres import AsyncPostgresDb
from agno.models.openai import OpenAIResponses
from agno.team import Team

db = AsyncPostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

researcher = Agent(
    name="Researcher",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Research topics thoroughly.",
)

summarizer = Agent(
    name="Summarizer",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Create concise summaries.",
)

team = Team(
    name="Research Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[researcher, summarizer],
    db=db,
    learning=True,
    markdown=True,
)


async def main():
    user_id = "async_test@example.com"

    print("\n" + "=" * 60)
    print("SESSION 1: Async learning extraction")
    print("=" * 60 + "\n")

    await team.aprint_response(
        "I'm a backend engineer interested in distributed systems. "
        "I prefer deep technical content with architecture diagrams.",
        user_id=user_id,
        session_id="async_session_1",
        stream=True,
    )

    lm = team.learning_machine
    print("\n--- Extracted Profile (Async) ---")
    lm.user_profile_store.print(user_id=user_id)

    print("\n" + "=" * 60)
    print("SESSION 2: Async recall in new session")
    print("=" * 60 + "\n")

    await team.aprint_response(
        "Based on my background, explain consensus algorithms.",
        user_id=user_id,
        session_id="async_session_2",
        stream=True,
    )


if __name__ == "__main__":
    asyncio.run(main())

Example behavior

Replace lm.user_profile_store.print(user_id=user_id) inside main() with an awaited profile read. The synchronous printer calls get(), which does not await an async database:

profile = await lm.user_profile_store.aget(user_id=user_id)
print(profile)

The default profile stores name fields. This prompt describes professional interests, so inspect await lm.user_memory_store.aget(user_id=user_id) for those observations. Learning extraction is model-dependent; an empty profile does not by itself indicate that async learning failed.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno "psycopg[binary]" openai sqlalchemy

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18

Run the example

Save the code above as team_async_learning.py, then run:

python team_async_learning.py

Full source: cookbook/03_teams/12_learning/09_team_async_learning.py