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

# Your First Memory

> Store and retrieve your first memory in 5 minutes

# Your First Memory

This guide shows you how to store and retrieve memories using the Aegis SDK.

## Prerequisites

<Note>
  Make sure the Aegis server is running (`aegis quickstart` or `docker-compose up -d`).
  See the [installation guide](/quickstart/installation) if you haven't set it up yet.
</Note>

## Store a Memory

```python theme={null}
from aegis_memory import AegisClient

# Connect to Aegis
client = AegisClient(
    api_key="dev-key",
    base_url="http://localhost:8000"
)

# Store a memory
result = client.add(
    content="User prefers dark mode and Python",
    agent_id="assistant",
    user_id="user_123"
)

print(f"Stored memory: {result.id}")
```

## Retrieve Memories

```python theme={null}
# Query by semantic similarity
memories = client.query(
    query="What are the user's preferences?",
    user_id="user_123",
    top_k=5
)

for memory in memories:
    print(f"- {memory.content} (score: {memory.score:.2f})")
```

Output:

```
- User prefers dark mode and Python (score: 0.94)
```

## Add More Context

```python theme={null}
# Add more memories
client.add("User is a backend developer", user_id="user_123")
client.add("User's project uses FastAPI", user_id="user_123")
client.add("User prefers async/await patterns", user_id="user_123")

# Now query for tech stack
memories = client.query("What tech stack does the user use?", user_id="user_123")
for m in memories:
    print(f"- {m.content}")
```

Output:

```
- User's project uses FastAPI
- User prefers async/await patterns
- User is a backend developer
- User prefers dark mode and Python
```

## Using Memory in a Prompt

```python theme={null}
# Get context for an LLM prompt
context = client.query(
    query="user preferences for code generation",
    user_id="user_123",
    top_k=3
)

context_str = "\n".join([f"- {m.content}" for m in context])

prompt = f"""Based on what you know about this user:
{context_str}

Generate a FastAPI endpoint for user authentication."""

# Now send to your LLM of choice
```

## Next Steps

<CardGroup cols={2}>
  <Card title="CrewAI Integration" icon="users" href="/quickstart/with-crewai">
    Add memory to CrewAI agents
  </Card>

  <Card title="Smart Memory" icon="lightbulb" href="/guides/smart-memory">
    Automatic memory extraction
  </Card>
</CardGroup>