Memory in Meko gives agents persistent, long-term recall of facts, preferences, and relationships extracted from conversations. Unlike conversation history (which stores raw messages), memory stores *derived knowledge* - the key facts and entities that an LLM extracts from interactions.

## How it works

Meko uses [mem0](https://github.com/mem0ai/mem0) to power its memory layer. When you add a memory, mem0 does the following:

1. Extracts entities and relationships from the text using an LLM.
1. Stores vector embeddings in pgvector for semantic similarity search.
1. Stores graph relationships in Apache AGE for entity-relationship queries.

This dual storage means memories can be found both by semantic similarity ("find memories about vacation preferences") and by graph traversal ("what entities are related to this user?").

## Memory operations

Meko exposes four core memory operations.

### Add memory

Store a new memory from text. The LLM extracts entities and relationships automatically.

```text
"Please remember that I like to take vacations at tropical beach locations, on a budget"
```

Behind the scenes, this calls `memory.add()` which extracts entities (user, tropical beaches, budget travel) and stores both vector embeddings and graph edges.

### Search memories

Find relevant memories using semantic similarity search. Returns the top matches with relevance scores.

```text
"What are my vacation preferences?"
```

This searches both the vector store (pgvector) and the graph store (Apache AGE), combining results for comprehensive recall.

### Get all memories

Retrieve every memory stored for a given user or agent. Useful for debugging or displaying a memory dashboard.

### Clear memories

Remove all memories for a user or agent. This is a destructive operation - cleared memories cannot be recovered.

## Memory vs. conversation history

|      | Memory | Conversation History |
| :--- | :----- | :------------------- |
| **What's stored** | Extracted facts and relationships | Raw messages (user + assistant) |
| **How it's created** | LLM extracts from conversations | Stored verbatim |
| **Search method** | Semantic similarity + graph traversal | Chronological lookup |
| **Use case** | "Remember my preferences across sessions" | "Show me what was said in the last chat" |

## Next steps

- [Work with memory](../../../guides/working-with-memory/) - How to add and search memories
- [Graph RAG](../graph-rag/) - How Apache AGE powers entity-relationship memory