Session Summary
Enable session summaries with enable_session_summaries or a custom SessionSummaryManager, stored in Postgres.
Use the session summary to store the conversation summary.
"""
Session Summary
=============================
This example shows how to use the session summary to store the conversation summary.
"""
from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from agno.session.summary import SessionSummaryManager # noqa: F401
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url, session_table="sessions")
# Method 1: Set enable_session_summaries to True
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
model=OpenAIResponses(id="gpt-5-mini"),
db=db,
enable_session_summaries=True,
session_id="session_123",
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
agent.print_response("Hi my name is John and I live in New York")
agent.print_response("I like to play basketball and hike in the mountains")
print(agent.get_session_summary(session_id="session_123"))
# Method 2: Set session_summary_manager
# session_summary_manager = SessionSummaryManager(model=OpenAIResponses(id="gpt-5-mini"))
# agent = Agent(
# model=OpenAIResponses(id="gpt-5-mini"),
# db=db,
# session_id="session_summary",
# session_summary_manager=session_summary_manager,
# )
# agent.print_response("Hi my name is John and I live in New York")
# agent.print_response("I like to play basketball and hike in the mountains")Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activateInstall dependencies
uv pip install -U agno "psycopg[binary]" openai sqlalchemyExport 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:18Run the example
Save the code above as session_summary.py, then run:
python session_summary.pyFull source: cookbook/02_agents/05_state_and_session/session_summary.py