team_decision_log.py
"""
Team Learning: Decision Logging
================================
Teams can log decisions for auditing, debugging, and learning
using the DecisionLogStore.
Decision logs capture:
- What decision was made
- Reasoning and alternatives considered
- Context and outcomes
This is useful for teams where traceability matters,
like architecture decisions, security reviews, or compliance.
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import (
DecisionLogConfig,
LearningMachine,
LearningMode,
)
from agno.models.openai import OpenAIResponses
from agno.team import Team
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
architect = Agent(
name="Solutions Architect",
model=OpenAIResponses(id="gpt-5.2"),
role="Evaluate architecture options and trade-offs.",
)
cost_analyst = Agent(
name="Cost Analyst",
model=OpenAIResponses(id="gpt-5.2"),
role="Analyze cost implications of technical decisions.",
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
team = Team(
name="Architecture Review Board",
model=OpenAIResponses(id="gpt-5.2"),
members=[architect, cost_analyst],
db=db,
learning=LearningMachine(
decision_log=DecisionLogConfig(
mode=LearningMode.AGENTIC,
enable_agent_tools=True,
agent_can_save=True,
agent_can_search=True,
),
),
instructions=[
"You are an architecture review board.",
"When making significant technical decisions, use the log_decision tool to record them.",
"Include your reasoning and any alternatives you considered.",
],
markdown=True,
show_members_responses=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "grace@example.com"
# Session 1: Make an architecture decision
print("\n" + "=" * 60)
print("SESSION 1: Database selection decision")
print("=" * 60 + "\n")
team.print_response(
"We need to choose a database for our new real-time analytics service. "
"Options are PostgreSQL with TimescaleDB, ClickHouse, or Apache Druid. "
"We expect 100K events/sec and need sub-second query latency. "
"Please evaluate and log your decision.",
user_id=user_id,
session_id="session_1",
stream=True,
)
lm = team.learning_machine
print("\n--- Decision Log ---")
lm.decision_log_store.print(session_id="session_1", limit=5)
# Session 2: Another decision
print("\n" + "=" * 60)
print("SESSION 2: Caching strategy decision")
print("=" * 60 + "\n")
team.print_response(
"For the same analytics service, we need a caching layer. "
"Should we use Redis, Memcached, or an in-process cache like Caffeine? "
"We need to cache aggregated query results with 5-minute TTL. "
"Please evaluate and log your decision.",
user_id=user_id,
session_id="session_2",
stream=True,
)
print("\n--- Updated Decision Log ---")
lm.decision_log_store.print(limit=5)
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_decision_log.py, then run:python team_decision_log.py