planning_mode.py
"""
Session Context: Planning Mode (Deep Dive)
==========================================
Goal, plan, and progress tracking for task-oriented sessions.
Planning mode adds:
- Goal: What the user is trying to achieve
- Plan: Steps to reach the goal
- Progress: Completed steps
Use for task-oriented agents where tracking progress matters.
Compare with: 01_summary_mode.py for summary-only (faster).
See also: 01_basics/3b_session_context_planning.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=True, # Track goal, plan, progress
),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run: Task Planning
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "deploy@example.com"
session_id = "deploy_session"
# Step 1: State the goal
print("\n" + "=" * 60)
print("STEP 1: State the goal")
print("=" * 60 + "\n")
agent.print_response(
"I need to deploy a new Python web app to AWS. Help me plan this.",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Step 2: Complete first task
print("\n" + "=" * 60)
print("STEP 2: First task done")
print("=" * 60 + "\n")
agent.print_response(
"Done! I've created the Dockerfile and it builds successfully.",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Step 3: More progress
print("\n" + "=" * 60)
print("STEP 3: More progress")
print("=" * 60 + "\n")
agent.print_response(
"ECR repository is set up and I've pushed the image.",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
# Step 4: What's next?
print("\n" + "=" * 60)
print("STEP 4: What's next?")
print("=" * 60 + "\n")
agent.print_response(
"What should I do next?",
user_id=user_id,
session_id=session_id,
stream=True,
)
agent.learning_machine.session_context_store.print(session_id=session_id)
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
planning_mode.py, then run:python planning_mode.py