"""
Session Context: Planning Mode
==============================
Session Context tracks the current conversation's state:
- What's been discussed
- Current goals and their status
- Active plans and progress
Planning mode (enable_planning=True) adds structured goal tracking -
summary plus goal, plan steps, and progress markers.
Compare with: 3a_session_context_summary.py for lightweight tracking.
"""
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")
# Planning mode: Tracks goals, plans, and progress in addition to summary.
# Good for task-oriented conversations where you want structured progress.
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
db=db,
instructions="Be very concise. Give brief, actionable answers.",
learning=LearningMachine(
session_context=SessionContextConfig(
enable_planning=True,
),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "planner@example.com"
session_id = "deploy_app"
# Turn 1: Set a goal with clear steps
print("\n" + "=" * 60)
print("TURN 1: Set goal")
print("=" * 60 + "\n")
agent.print_response(
"Help me deploy a Python app to production. Give me 3 steps.",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Turn 2: Complete first step
print("\n" + "=" * 60)
print("TURN 2: Complete step 1")
print("=" * 60 + "\n")
agent.print_response(
"Done with step 1. What's the command for step 2?",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Turn 3: Complete second step
print("\n" + "=" * 60)
print("TURN 3: Complete step 2")
print("=" * 60 + "\n")
agent.print_response(
"Step 2 done. What's left?",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
