summary_mode.py
"""
Session Context: Summary Mode (Deep Dive)
=========================================
Running summary of conversation state.
Summary mode maintains a running summary of the conversation that
persists across reconnections. Each turn, the summary is updated
to include the new information.
Compare with: 02_planning_mode.py for goal/plan tracking.
See also: 01_basics/3a_session_context_summary.py for the basics.
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearningMachine, SessionContextConfig
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
learning=LearningMachine(
session_context=SessionContextConfig(
enable_planning=False, # Summary only
),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run: Multi-Turn Summary
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "debug@example.com"
session_id = "debug_session"
# Turn 1: Initial question
print("\n" + "=" * 60)
print("TURN 1: Initial question")
print("=" * 60 + "\n")
agent.print_response(
"I'm debugging a memory leak in my Python FastAPI server. "
"It processes large JSON payloads.",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Turn 2: More context
print("\n" + "=" * 60)
print("TURN 2: More context")
print("=" * 60 + "\n")
agent.print_response(
"The memory grows even when there's no traffic. "
"I've checked for unclosed file handles already.",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Turn 3: Follow-up
print("\n" + "=" * 60)
print("TURN 3: Follow-up")
print("=" * 60 + "\n")
agent.print_response(
"Could it be related to Pydantic model caching?",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Simulate reconnection
print("\n" + "=" * 60)
print("TURN 4: Recall after 'reconnection'")
print("=" * 60 + "\n")
agent.print_response(
"What were we debugging?",
user_id=user_id,
session_id=session_id,
stream=True,
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai psycopg-binary sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
summary_mode.py, then run:python summary_mode.py