docs: update for pipecat PR #4156

server/services/memory/mem0.mdx

@@ -152,8 +152,27 @@ memory = Mem0MemoryService(
 )
 ```
 
+### Retrieving Memories Outside the Pipeline
+
+The `get_memories()` method allows you to access stored memories outside the normal pipeline flow, such as when creating a personalized greeting at connection time:
+
+```python
+# Get all stored memories for the configured user/agent/run IDs
+memories = await memory.get_memories()
+
+# Create a personalized greeting
+if memories:
+    greeting = "Hello! Based on our previous conversations, I remember: "
+    for mem in memories[:3]:
+        greeting += f"{mem['memory']} "
+else:
+    greeting = "Hello! It's nice to meet you."
+```
+
 ## Notes
 
 - **At least one ID required**: You must provide at least one of `user_id`, `agent_id`, or `run_id`. A `ValueError` is raised if none are provided.
 - **Cloud vs local**: Use `api_key` for Mem0's cloud API, or `local_config` for a self-hosted Mem0 instance using `Memory.from_config()`.
 - **Pipeline placement**: Place the `Mem0MemoryService` before your LLM service in the pipeline. It intercepts `LLMContextFrame`, `OpenAILLMContextFrame`, and `LLMMessagesFrame` to enhance context with relevant memories before passing them downstream.
+- **Non-blocking operation**: All Mem0 API calls (storage, retrieval, search) run in background threads to avoid blocking the event loop. Message storage is fire-and-forget, so it doesn't delay downstream processing.
+- **Message role filtering**: Only messages with `user` or `assistant` roles are stored in Mem0. Messages with other roles (such as `system` or `developer`) are automatically filtered out, as the Mem0 API does not accept them.