team_session_planning.py
"""
Team Learning: Session Planning
================================
Teams can track session goals and progress using SessionContext
with planning mode enabled.
Planning mode captures:
- Current goal and sub-tasks
- Plan steps with completion status
- Progress markers across turns
This is useful for teams that work on multi-step tasks like
deployment pipelines, project planning, or onboarding flows.
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import (
LearningMachine,
LearningMode,
SessionContextConfig,
UserProfileConfig,
)
from agno.models.openai import OpenAIResponses
from agno.team import Team
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
devops_engineer = Agent(
name="DevOps Engineer",
model=OpenAIResponses(id="gpt-5.2"),
role="Handle infrastructure, CI/CD, and deployment tasks.",
)
security_reviewer = Agent(
name="Security Reviewer",
model=OpenAIResponses(id="gpt-5.2"),
role="Review security considerations and compliance requirements.",
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
team = Team(
name="Release Team",
model=OpenAIResponses(id="gpt-5.2"),
members=[devops_engineer, security_reviewer],
db=db,
learning=LearningMachine(
user_profile=UserProfileConfig(
mode=LearningMode.ALWAYS,
),
session_context=SessionContextConfig(
enable_planning=True,
),
),
markdown=True,
show_members_responses=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "diana@example.com"
session_id = "release_v2"
# Turn 1: Define the release goal
print("\n" + "=" * 60)
print("TURN 1: Define release goal")
print("=" * 60 + "\n")
team.print_response(
"I'm Diana, release manager. We need to deploy v2.0 to production. "
"Give me a 3-step release checklist covering infra, security, and rollout.",
user_id=user_id,
session_id=session_id,
stream=True,
)
lm = team.learning_machine
print("\n--- Session Context ---")
lm.session_context_store.print(session_id=session_id)
# Turn 2: Complete first step
print("\n" + "=" * 60)
print("TURN 2: Infrastructure ready")
print("=" * 60 + "\n")
team.print_response(
"Infrastructure is ready - staging tests passed. "
"What security checks should we run before proceeding?",
user_id=user_id,
session_id=session_id,
stream=True,
)
print("\n--- Updated Session Context ---")
lm.session_context_store.print(session_id=session_id)
# Turn 3: Final step
print("\n" + "=" * 60)
print("TURN 3: Security cleared, ready for rollout")
print("=" * 60 + "\n")
team.print_response(
"Security review passed. What's the recommended rollout strategy?",
user_id=user_id,
session_id=session_id,
stream=True,
)
print("\n--- Final Session Context ---")
lm.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 "psycopg[binary]" openai 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 \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
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
5
Run the example
Save the code above as
team_session_planning.py, then run:python team_session_planning.py