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