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

# Agent Memory

> Store and recall user-specific facts across agent runs.

Agent memory stores user-specific facts in a database and recalls them in later runs.

## User Memories

Set `enable_agentic_memory=True` to let the agent decide when to create or update memories:

```python memory_demo.py theme={null}
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.db.postgres import PostgresDb
from rich.pretty import pprint

user_id = "ava"

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

db = PostgresDb(
    db_url=db_url,
    memory_table="user_memories",
)


memory_agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    enable_agentic_memory=True,
    # Alternatively, run MemoryManager at the end of every run:
    # update_memory_on_run=True,
    markdown=True,
)

db.clear_memories()

memory_agent.print_response(
    "My name is Ava and I like to ski.",
    user_id=user_id,
    stream=True,
)
print("Memories about Ava:")
pprint(memory_agent.get_user_memories(user_id=user_id))

memory_agent.print_response(
    "I live in San Francisco. Where should I move within a four-hour drive?",
    user_id=user_id,
    stream=True,
)
print("Memories about Ava:")
pprint(memory_agent.get_user_memories(user_id=user_id))
```

<Tip>
  `enable_agentic_memory=True` gives the agent one `update_user_memory` tool backed by `MemoryManager`.
  Set `update_memory_on_run=True` to run `MemoryManager` at the end of every run instead.
</Tip>

<Note>
  See the [Memory overview](/memory/overview).
</Note>

## Developer Resources

* [Agent schema](/reference/agents/agent)
* [Memory examples](/examples/memory/overview)
* [Memory cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/11_memory/)