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

# Entity Memory: Facts and Events (Deep Dive)

> Semantic (facts) vs episodic (events) memory for entities.

```python facts_and_events.py theme={null}
"""
Entity Memory: Facts and Events (Deep Dive)
============================================
Semantic (facts) vs episodic (events) memory for entities.

Entity Memory stores knowledge about external entities:
- Facts: Timeless truths ("Acme uses PostgreSQL")
- Events: Time-bound occurrences ("Acme raised $30M on Jan 15")

AGENTIC mode gives the agent tools to create/update entities.

Compare with: 04_always_extraction.py for automatic extraction.
See also: 01_basics/5a_entity_memory_always.py for the basics.
"""

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")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    instructions=(
        "Track information about companies and people. "
        "Distinguish between facts (timeless) and events (time-bound)."
    ),
    learning=LearningMachine(
        entity_memory=EntityMemoryConfig(
            mode=LearningMode.AGENTIC,
            namespace="global",
        ),
    ),
    markdown=True,
)

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

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

    user_id = "research@example.com"
    session_id = "company_research"

    # Share facts and events
    print("\n" + "=" * 60)
    print("MESSAGE 1: Share mixed facts and events")
    print("=" * 60 + "\n")

    agent.print_response(
        "Notes from my meeting with DataPipe: "
        "They're based in San Francisco. "
        "They build real-time ETL infrastructure in Rust. "
        "Their CTO is Marcus Chen. "
        "They just hit 1000 customers last month. "
        "Series B closed at $80M two weeks ago.",
        user_id=user_id,
        session_id=session_id,
        stream=True,
    )
    print("\n--- Entities ---")
    pprint(
        agent.learning_machine.entity_memory_store.search(query="datapipe", limit=10)
    )

    # Query the entity
    print("\n" + "=" * 60)
    print("MESSAGE 2: Query the entity")
    print("=" * 60 + "\n")

    agent.print_response(
        "What do we know about DataPipe?",
        user_id=user_id,
        session_id="session_2",
        stream=True,
    )

    # Add more events
    print("\n" + "=" * 60)
    print("MESSAGE 3: Add more events")
    print("=" * 60 + "\n")

    agent.print_response(
        "Update on DataPipe: They announced a partnership with BigCloud yesterday. "
        "They're also opening a London office next quarter.",
        user_id=user_id,
        session_id="session_3",
        stream=True,
    )
    print("\n--- Updated Entities ---")
    pprint(
        agent.learning_machine.entity_memory_store.search(query="datapipe", limit=10)
    )
```

## 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 `facts_and_events.py`, then run:

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

Full source: [cookbook/08\_learning/04\_entity\_memory/01\_facts\_and\_events.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/04_entity_memory/01_facts_and_events.py)