Session Summary

Generate and persist session summaries with SessionSummaryManager.

Demonstrates configuring session summaries for an agent using PostgresDb.

session_summary.py
"""
Session Summary
===============

Demonstrates configuring session summaries for an agent using PostgresDb.
"""

from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.session.summary import SessionSummaryManager  # noqa: F401

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
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
#
# agent = Agent(
#     model=OpenAIChat(id="gpt-5.2"),
#     db=db,
#     enable_session_summaries=True,
#     session_id="session_summary",
#     add_session_summary_to_context=True,
# )
#
# 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")

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
# Method 2: Set session_summary_manager
session_summary_manager = SessionSummaryManager(model=OpenAIChat(id="gpt-5.2"))
agent = Agent(
    model=OpenAIChat(id="gpt-5.2"),
    db=db,
    session_id="session_summary",
    session_summary_manager=session_summary_manager,
)

# ---------------------------------------------------------------------------
# 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")

A supplied SessionSummaryManager generates summaries and, by default, adds an available summary to later prompts. Summary generation does not automatically prune stored runs or replace history enabled through add_history_to_context. Inspect the stored summary with agent.get_session_summary(session_id="session_summary").

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 session_summary.py, then run:

python session_summary.py

Full source: cookbook/06_storage/02_session_summary.py