> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Team Learning: Decision Logging

> Teams can log decisions for auditing, debugging, and learning using the DecisionLogStore.

```python team_decision_log.py theme={null}
"""
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

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno "psycopg[binary]" openai sqlalchemy
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Run the example">
    Save the code above as `team_decision_log.py`, then run:

    ```bash theme={null}
    python team_decision_log.py
    ```
  </Step>
</Steps>

Full source: [cookbook/03\_teams/12\_learning/06\_team\_decision\_log.py](https://github.com/agno-agi/agno/blob/main/cookbook/03_teams/12_learning/06_team_decision_log.py)