> ## 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.

# Session Context: Summary Mode

> Summary mode provides lightweight tracking - a running summary without goal/plan structure.

```python session_context_summary.py theme={null}
"""
Session Context: Summary Mode
=============================
Session Context tracks the current conversation's state:
- What's been discussed
- Key decisions made
- Important context

Summary mode provides lightweight tracking - a running summary without goal/plan structure.

Compare with: 3b_session_context_planning.py for goal-oriented tracking.
"""

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

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

# Summary mode: Just tracks what's been discussed, no planning overhead.
# Good for general conversations where you want continuity without structure.
agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    instructions="Be very concise. Give brief answers in 1-2 sentences.",
    learning=LearningMachine(session_context=True),
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    user_id = "session@example.com"
    session_id = "api_design"

    # Turn 1: Start discussion
    print("\n" + "=" * 60)
    print("TURN 1: Start discussion")
    print("=" * 60 + "\n")

    agent.print_response(
        "I'm designing a REST API for a todo app. PUT or PATCH for updates?",
        user_id=user_id,
        session_id=session_id,
        stream=True,
    )
    agent.learning_machine.session_context_store.print(session_id=session_id)

    # Turn 2: Follow-up
    print("\n" + "=" * 60)
    print("TURN 2: Follow-up question")
    print("=" * 60 + "\n")

    agent.print_response(
        "What URL structure for that endpoint?",
        user_id=user_id,
        session_id=session_id,
        stream=True,
    )
    agent.learning_machine.session_context_store.print(session_id=session_id)

    # Turn 3: Test recall
    print("\n" + "=" * 60)
    print("TURN 3: Test context recall")
    print("=" * 60 + "\n")

    agent.print_response(
        "What did we decide?",
        user_id=user_id,
        session_id=session_id,
        stream=True,
    )
    agent.learning_machine.session_context_store.print(session_id=session_id)
```

## 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 `session_context_summary.py`, then run:

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

Full source: [cookbook/08\_learning/01\_basics/3a\_session\_context\_summary.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/01_basics/3a_session_context_summary.py)