> ## 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.

# Entity Memory: Agentic Mode

> The agent decides when to store and retrieve information.

```python entity_memory_agentic.py theme={null}
"""
Entity Memory: Agentic Mode
===========================
Entity Memory stores knowledge about external things:
- Companies, people, projects
- Facts, events, relationships
- Shared context across users

AGENTIC mode gives the agent explicit tools to manage entities:
- search_entities, create_entity
- add_fact, add_event, add_relationship

The agent decides when to store and retrieve information.

Compare with: 5a_entity_memory_always.py for automatic extraction.
"""

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

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

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

# AGENTIC mode: Agent gets entity tools and decides when to use them.
# You'll see tool calls like "create_entity", "add_fact" in responses.
agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    instructions=(
        "You're a sales assistant tracking companies and contacts. "
        "Be concise. Always search for existing entities before creating new ones."
    ),
    learning=LearningMachine(
        entity_memory=EntityMemoryConfig(
            mode=LearningMode.AGENTIC,
        ),
    ),
    markdown=True,
)

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

if __name__ == "__main__":
    from rich.pretty import pprint

    user_id = "sales@example.com"

    # Session 1: Create entity
    print("\n" + "=" * 60)
    print("SESSION 1: Create entity (watch for tool calls)")
    print("=" * 60 + "\n")

    agent.print_response(
        "Track Acme Corp - fintech startup in SF, 50 employees, "
        "uses Python and Postgres. CTO is Jane Smith.",
        user_id=user_id,
        session_id="session_1",
        stream=True,
    )

    print("\n--- Created Entities ---")
    entities = agent.learning_machine.entity_memory_store.search(query="acme", limit=10)
    pprint(entities)

    # Session 2: Update same entity
    print("\n" + "=" * 60)
    print("SESSION 2: Update existing entity")
    print("=" * 60 + "\n")

    agent.print_response(
        "Acme Corp just raised $50M Series B from Sequoia.",
        user_id=user_id,
        session_id="session_2",
        stream=True,
    )

    print("\n--- Updated Entities ---")
    entities = agent.learning_machine.entity_memory_store.search(query="acme", limit=10)
    pprint(entities)
```

## 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 `entity_memory_agentic.py`, then run:

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

Full source: [cookbook/08\_learning/01\_basics/5b\_entity\_memory\_agentic.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/01_basics/5b_entity_memory_agentic.py)