supermemory/packages/livekit-sdk-python/README.md

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# Supermemory LiveKit SDK
Persistent memory for [LiveKit Agents](https://docs.livekit.io/agents/) voice sessions, powered by [Supermemory](https://supermemory.ai).
The plugin recalls the caller's profile and relevant memories before each reply, stores the call as one conversation, and gives the model tools to search, remember, and forget. A Supermemory outage does not end the call.
## Installation
```bash
pip install supermemory-livekit
```
```bash
export SUPERMEMORY_API_KEY=your_supermemory_api_key
```
Create a key at [console.supermemory.ai](https://console.supermemory.ai).
## Quick start
Scope memory with a stable caller id. A LiveKit participant identity works when it is already a container tag (`letters`, `numbers`, `_`, `-`, `:`). Otherwise set the participant attribute `supermemory_container_tag`.
```python
import json
import os
from livekit import agents
from livekit.agents import AgentServer, AgentSession, ChatContext, JobContext
from supermemory_livekit import SupermemoryAgent, SupermemoryLiveKit
server = AgentServer()
@server.rtc_session(agent_name="memory-agent")
async def entrypoint(ctx: JobContext):
memory = SupermemoryLiveKit(api_key=os.getenv("SUPERMEMORY_API_KEY"))
metadata = json.loads(ctx.job.metadata or "{}")
container_tag = metadata.get("container_tag")
if container_tag:
memory.bind(container_tag=container_tag, session_id=ctx.room.name)
chat_ctx = ChatContext()
if container_tag:
await memory.preload(chat_ctx)
await ctx.connect()
if not container_tag:
participant = await ctx.wait_for_participant()
memory.bind(participant=participant, session_id=ctx.room.name)
session = AgentSession(
stt="deepgram/nova-3:en",
llm="openai/gpt-4.1-mini",
tts="cartesia/sonic-3",
)
memory.attach(session)
await session.start(
room=ctx.room,
agent=SupermemoryAgent(
memory,
chat_ctx=chat_ctx,
instructions=(
"You are a helpful voice assistant. You remember this caller across calls. "
"Use that naturally, and do not mention the memory system."
),
),
)
if __name__ == "__main__":
agents.cli.run_app(server)
```
If you already have an `Agent` subclass, pass `tools=memory.tools()` and call `await memory.on_user_turn_completed(turn_ctx, new_message)` from `on_user_turn_completed`.
`on_user_turn_completed` runs for STT-LLM-TTS pipelines. Realtime models only hit that hook when turn detection runs in the agent, not inside the model. Call capture still listens to `conversation_item_added`.
## Configuration
```python
from supermemory_livekit import InputParams, SupermemoryLiveKit
memory = SupermemoryLiveKit(
container_tag="user_123",
session_id="room-123",
params=InputParams(
mode="full", # "profile" | "query" | "full"
search_limit=10,
search_threshold=0.1,
recall_timeout=1.5, # seconds; a slow recall is skipped
capture="always", # "always" | "never"
),
)
```
| Mode | Profile | Search | Use when |
| --- | --- | --- | --- |
| `profile` | Yes | No | You only need durable facts |
| `query` | No | Yes | You only need memories related to this turn |
| `full` | Yes | Yes | Default |
One call is stored as a single document under custom id `lk-<session_id>`, so a reconnect with the same session id updates that document instead of creating another. Explicit `remember` calls are separate facts and are not tied to the call document.
## Links
- [Docs](https://supermemory.ai/docs/integrations/livekit)
- [LiveKit Agents](https://docs.livekit.io/agents/)