pipecat-sdk (#669)pipecat-docs

### Changes

 - New documentation page: apps/docs/voice-realtime/pipecat.mdx
  - Installation guide for supermemory-pipecat package
  - Configuration with SupermemoryPipecatService and InputParams
  - Pipeline integration guide
  - How it works (Memory Retrieval, Context Enhancement)
  - Memory modes table (profile, query, full)
  - Configuration options with InputParams
  - Complete FastAPI voice agent example
 - Updated docs.json:
  - Added new "Voice & Realtime" group under SDKs tab with mic icon
  - Removed external npm/pypi link pages from SDK sidebar

### Files Changed

 apps/docs/voice-realtime/pipecat.mdx  (new)
 apps/docs/docs.json                   (modified)

### Navigation

SDKs → Voice & Realtime → Pipecat

#

2 files changed, 212 insertions, 8 deletions

diff --git a/apps/docs/docs.json b/apps/docs/docs.json
index 5905036a..26aff108 100644
--- a/apps/docs/docs.json
+++ b/apps/docs/docs.json
@@ -211,15 +211,13 @@
 							{
 								"group": "Supermemory SDKs",
 								"pages": [
-									"memory-api/sdks/native",
-									"memory-api/sdks/supermemory-npm",
-									"memory-api/sdks/supermemory-pypi"
+									"memory-api/sdks/native"
 								]
 							},
 							{
 								"group": "OpenAI SDK",
 								"icon": "sparkles",
-								"pages": ["memory-api/sdks/openai-plugins", "ai-sdk/npm"]
+								"pages": ["memory-api/sdks/openai-plugins"]
 							},
 							{
 								"group": "AI SDK",
@@ -228,8 +226,7 @@
 									"ai-sdk/overview",
 									"ai-sdk/user-profiles",
 									"ai-sdk/memory-tools",
-									"ai-sdk/infinite-chat",
-									"ai-sdk/npm"
+									"ai-sdk/infinite-chat"
 								]
 							},
 							{
@@ -240,8 +237,14 @@
 									"memory-graph/installation",
 									"memory-graph/quickstart",
 									"memory-graph/api-reference",
-									"memory-graph/examples",
-									"memory-graph/npm"
+									"memory-graph/examples"
+								]
+							},
+							{
+								"group": "Voice & Realtime",
+								"icon": "mic",
+								"pages": [
+									"voice-realtime/pipecat"
 								]
 							}
 						]
diff --git a/apps/docs/voice-realtime/pipecat.mdx b/apps/docs/voice-realtime/pipecat.mdx
new file mode 100644
index 00000000..6f100d65
--- /dev/null
+++ b/apps/docs/voice-realtime/pipecat.mdx
@@ -0,0 +1,201 @@
+---
+title: "Pipecat"
+description: "Integrate Supermemory with Pipecat for conversational memory in voice AI agents"
+---
+
+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.
+
+## Installation
+
+To use Supermemory with Pipecat, install the required dependencies:
+
+```bash
+pip install supermemory-pipecat
+```
+
+Set up your API key as an environment variable:
+
+```bash
+export SUPERMEMORY_API_KEY=your_supermemory_api_key
+```
+
+You can obtain an API key from [console.supermemory.ai](https://console.supermemory.ai).
+
+## Configuration
+
+Supermemory integration is provided through the `SupermemoryPipecatService` class in Pipecat:
+
+```python
+from supermemory_pipecat import SupermemoryPipecatService, 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",
+    ),
+)
+```
+
+## Pipeline Integration
+
+The `SupermemoryPipecatService` should be positioned between your context aggregator and LLM service in the Pipecat pipeline:
+
+```python
+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(),
+])
+```
+
+## How It Works
+
+When integrated with Pipecat, Supermemory provides two key functionalities:
+
+### 1. Memory Retrieval
+
+When a user message is detected, Supermemory retrieves relevant memories:
+
+- **Static Profile**: Persistent facts about the user
+- **Dynamic Profile**: Recent context and preferences
+- **Search Results**: Semantically relevant past memories
+
+### 2. Context Enhancement
+
+Retrieved memories are formatted and injected into the LLM context before generation, giving the model awareness of past conversations.
+
+## Memory Modes
+
+| 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) |
+
+## Configuration Options
+
+You can customize how memories are retrieved and used:
+
+### InputParams
+
+```python
+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",
+)
+```
+
+| 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 |
+
+## Example: Voice Agent with Memory
+
+Here's a complete example of a Pipecat voice agent with Supermemory integration:
+
+```python
+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, 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-4o-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)
+
+
[email protected]("/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)
+```