apps/docs/integrations/pipecat.mdx
Supermemory integrates with Pipecat, providing long-term memory capabilities for voice AI agents. Your Pipecat applications will remember past conversations and provide personalized responses based on user history.
To use Supermemory with Pipecat, install the required dependencies:
pip install supermemory-pipecat
Set up your API key as an environment variable:
export SUPERMEMORY_API_KEY=your_supermemory_api_key
You can obtain an API key from console.supermemory.ai.
Supermemory integration is provided through the SupermemoryPipecatService class in Pipecat:
from supermemory_pipecat import SupermemoryPipecatService
from supermemory_pipecat.service import InputParams
memory = SupermemoryPipecatService(
api_key=os.getenv("SUPERMEMORY_API_KEY"),
user_id="unique_user_id",
session_id="session_123",
params=InputParams(
mode="full", # "profile" | "query" | "full"
search_limit=10, # Max memories to retrieve
search_threshold=0.1, # Relevance threshold (0.0-1.0)
system_prompt="Based on previous conversations:\n\n",
),
)
The SupermemoryPipecatService should be positioned between your context aggregator and LLM service in the Pipecat pipeline:
pipeline = Pipeline([
transport.input(),
stt, # Speech-to-text
context_aggregator.user(),
memory, # <- Supermemory memory service
llm,
tts, # Text-to-speech
transport.output(),
context_aggregator.assistant(),
])
When integrated with Pipecat, Supermemory provides two key functionalities:
When a user message is detected, Supermemory retrieves relevant memories:
Retrieved memories are formatted and injected into the LLM context before generation, giving the model awareness of past conversations.
| Mode | Static Profile | Dynamic Profile | Search Results | Use Case |
|---|---|---|---|---|
"profile" | Yes | Yes | No | Personalization without search |
"query" | No | No | Yes | Finding relevant past context |
"full" | Yes | Yes | Yes | Complete memory (default) |
You can customize how memories are retrieved and used:
InputParams(
mode="full", # Memory mode (default: "full")
search_limit=10, # Max memories to retrieve (default: 10)
search_threshold=0.1, # Similarity threshold 0.0-1.0 (default: 0.1)
system_prompt="Based on previous conversations:\n\n",
inject_mode="auto", # "auto" | "system" | "user"
)
| Parameter | Type | Default | Description |
|---|---|---|---|
search_limit | int | 10 | Maximum number of memories to retrieve per query |
search_threshold | float | 0.1 | Minimum similarity threshold for memory retrieval |
mode | str | "full" | Memory retrieval mode: "profile", "query", or "full" |
system_prompt | str | "Based on previous conversations:\n\n" | Prefix text for memory context |
inject_mode | str | "auto" | How memories are injected: "auto", "system", or "user" |
The inject_mode parameter controls how memories are added to the LLM context:
| Mode | Behavior |
|---|---|
"auto" | Auto-detects based on frame types. If audio frames detected → injects to system prompt (speech-to-speech). If only text frames → injects as user message (STT/TTS). |
"system" | Always injects memories into the system prompt |
"user" | Always injects memories as a user message |
For speech-to-speech models like Gemini Live, the SDK automatically detects audio frames and injects memories into the system prompt. No configuration needed:
from supermemory_pipecat import SupermemoryPipecatService
# Auto-detection works out of the box
memory = SupermemoryPipecatService(
api_key=os.getenv("SUPERMEMORY_API_KEY"),
user_id="unique_user_id",
)
Here's a complete example of a Pipecat voice agent with Supermemory integration:
import os
from fastapi import FastAPI, WebSocket
from fastapi.middleware.cors import CORSMiddleware
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.serializers.protobuf import ProtobufFrameSerializer
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.openai.tts import OpenAITTSService
from pipecat.services.openai.stt import OpenAISTTService
from pipecat.transports.websocket.fastapi import (
FastAPIWebsocketParams,
FastAPIWebsocketTransport,
)
from supermemory_pipecat import SupermemoryPipecatService
from supermemory_pipecat.service import InputParams
app = FastAPI()
SYSTEM_PROMPT = """You are a helpful voice assistant with memory capabilities.
You remember information from past conversations and use it to provide personalized responses.
Keep responses brief and conversational."""
async def run_bot(websocket_client, user_id: str, session_id: str):
transport = FastAPIWebsocketTransport(
websocket=websocket_client,
params=FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
serializer=ProtobufFrameSerializer(),
),
)
stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"))
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-5-mini")
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
# Supermemory memory service
memory = SupermemoryPipecatService(
user_id=user_id,
session_id=session_id,
params=InputParams(
mode="full",
search_limit=10,
search_threshold=0.1,
),
)
context = OpenAILLMContext([{"role": "system", "content": SYSTEM_PROMPT}])
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline([
transport.input(),
stt,
context_aggregator.user(),
memory,
llm,
tts,
transport.output(),
context_aggregator.assistant(),
])
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
await run_bot(websocket, user_id="alice", session_id="session-123")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
For a complete example using Gemini Live speech-to-speech with Supermemory, check out the reference implementation:
<Card title="Pipecat Memory Example" icon="github" href="https://github.com/supermemoryai/pipecat-memory"
Full working example with Gemini Live, including frontend and backend code. </Card>