> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-service-account.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Decision Logs: Basic Usage

> Use DecisionLogStore to record and retrieve agent decisions.

```python basic_decision_log.py theme={null}
"""
Decision Logs: Basic Usage
==========================

This example demonstrates how to use DecisionLogStore to record
and retrieve agent decisions.

DecisionLogStore is useful for:
- Auditing agent behavior
- Debugging unexpected outcomes
- Learning from past decisions
- Building feedback loops

Run:
    .venvs/demo/bin/python cookbook/08_learning/09_decision_logs/01_basic_decision_log.py
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import DecisionLogConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
# Database connection
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
# Create an agent with decision logging
# AGENTIC mode: Agent explicitly logs decisions via the log_decision tool
agent = Agent(
    id="decision-logger",
    name="Decision Logger",
    model=OpenAIResponses(id="gpt-5.5"),
    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 a helpful assistant that logs important decisions.",
        "When you make a significant choice (like selecting a tool, choosing a response style, or deciding to ask for clarification), use the log_decision tool to record it.",
        "Include your reasoning and any alternatives you considered.",
    ],
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    # Test: Ask the agent to make a decision
    print("=== Test 1: Agent logs a decision ===\n")
    agent.print_response(
        "I need help choosing between Python and JavaScript for a web scraping project. What would you recommend?",
        session_id="session-001",
    )

    # View logged decisions
    print("\n=== Decisions Logged ===\n")
    decision_store = agent.learning_machine.decision_log_store
    if decision_store:
        decision_store.print(agent_id="decision-logger", limit=5)
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai psycopg-binary 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 `basic_decision_log.py`, then run:

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

Full source: [cookbook/08\_learning/09\_decision\_logs/01\_basic\_decision\_log.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/09_decision_logs/01_basic_decision_log.py)