supermemory/packages/livekit-sdk-python

Supermemory LiveKit SDK

Persistent memory for LiveKit Agents voice sessions, powered by Supermemory.

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

pip install supermemory-livekit

export SUPERMEMORY_API_KEY=your_supermemory_api_key

Create a key at 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.

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 recall from llm_node:

async def llm_node(self, chat_ctx, tools, model_settings):
    await memory.enrich(chat_ctx)
    return Agent.default.llm_node(self, chat_ctx, tools, model_settings)

Recall in llm_node, not on_user_turn_completed: changing the turn context in that hook makes LiveKit discard its preemptive generation. Realtime models skip llm_node, so with one call await memory.on_user_turn_completed(turn_ctx, new_message) from on_user_turn_completed. SupermemoryAgent picks the right hook for you. Call capture listens to conversation_item_added.

Configuration

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=2.0,     # seconds; a slow recall is skipped
        capture="always",       # "always" | "never"
        capture_dreaming="dynamic",  # "dynamic" | "instant" (ready within a minute, extra operation)
    ),
)

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, processed right away so the next call can recall them, and are not tied to the call document.

Links

• Docs
• LiveKit Agents