diff --git a/services/mana-voice-bot/CLAUDE.md b/services/mana-voice-bot/CLAUDE.md
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+# CLAUDE.md - Mana Voice Bot
+
+## Service Overview
+
+German voice-to-voice assistant combining:
+- **STT**: Whisper via mana-stt (Port 3020)
+- **LLM**: Ollama with Gemma/Qwen (Port 11434)
+- **TTS**: Edge TTS (Microsoft, cloud API)
+
+**Port**: 3050
+
+## Architecture
+
+```
+Audio Input → Whisper (STT) → Ollama (LLM) → Edge TTS → Audio Output
+     ↓              ↓              ↓              ↓
+  [WAV/MP3]    [German Text]  [Response]     [MP3 Audio]
+```
+
+## Commands
+
+```bash
+# Setup
+./setup.sh
+
+# Development
+source venv/bin/activate
+uvicorn app.main:app --host 0.0.0.0 --port 3050 --reload
+
+# Production
+./start.sh
+
+# Test
+curl http://localhost:3050/health
+```
+
+## API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | Service health check |
+| `/voices` | GET | List German TTS voices |
+| `/models` | GET | List available Ollama models |
+| `/transcribe` | POST | Audio → Text (STT only) |
+| `/chat` | POST | Text → Text (LLM only) |
+| `/chat/audio` | POST | Text → Audio (LLM + TTS) |
+| `/tts` | POST | Text → Audio (TTS only) |
+| `/voice` | POST | Audio → Audio (Full pipeline) |
+| `/voice/metadata` | POST | Audio → JSON (Full pipeline, no audio) |
+
+## Usage Examples
+
+### Full Voice Pipeline
+```bash
+# Record audio and send to voice bot
+curl -X POST http://localhost:3050/voice \
+  -F "audio=@input.wav" \
+  -F "model=gemma3:4b" \
+  -F "voice=de-DE-ConradNeural" \
+  -o response.mp3
+```
+
+### Text to Audio
+```bash
+curl -X POST http://localhost:3050/chat/audio \
+  -H "Content-Type: application/json" \
+  -d '{"message": "Was ist die Hauptstadt von Deutschland?", "voice": "de-DE-KatjaNeural"}' \
+  -o response.mp3
+```
+
+### TTS Only
+```bash
+curl -X POST http://localhost:3050/tts \
+  -F "text=Hallo, wie geht es dir?" \
+  -F "voice=de-DE-ConradNeural" \
+  -o hello.mp3
+```
+
+## German Voices
+
+| Voice ID | Description |
+|----------|-------------|
+| `de-DE-ConradNeural` | Male - Professional (Default) |
+| `de-DE-KatjaNeural` | Female - Natural |
+| `de-DE-AmalaNeural` | Female - Friendly |
+| `de-DE-BerndNeural` | Male - Calm |
+| `de-DE-ChristophNeural` | Male - News |
+| `de-DE-ElkeNeural` | Female - Warm |
+| `de-DE-KillianNeural` | Male - Casual |
+| `de-DE-KlarissaNeural` | Female - Cheerful |
+| `de-DE-KlausNeural` | Male - Storyteller |
+| `de-DE-LouisaNeural` | Female - Assistant |
+| `de-DE-TanjaNeural` | Female - Business |
+
+## Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `PORT` | `3050` | Service port |
+| `STT_URL` | `http://localhost:3020` | mana-stt URL |
+| `OLLAMA_URL` | `http://localhost:11434` | Ollama URL |
+| `DEFAULT_MODEL` | `gemma3:4b` | Default LLM model |
+| `DEFAULT_VOICE` | `de-DE-ConradNeural` | Default TTS voice |
+| `SYSTEM_PROMPT` | (German assistant) | LLM system prompt |
+
+## Dependencies
+
+- `fastapi` - Web framework
+- `uvicorn` - ASGI server
+- `aiohttp` - Async HTTP client
+- `edge-tts` - Microsoft TTS
+- `python-multipart` - File uploads
+
+## Performance
+
+Typical latency breakdown:
+- STT (Whisper): 0.5-2s
+- LLM (Gemma 4B): 1-5s
+- TTS (Edge): 0.3-0.5s
+- **Total**: 2-7s
+
+## Mac Mini Deployment
+
+```bash
+# On Mac Mini
+cd ~/projects/manacore-monorepo/services/mana-voice-bot
+./setup.sh
+./start.sh
+
+# Or with launchd (autostart)
+# See scripts/mac-mini/setup-voice-bot.sh
+```
diff --git a/services/mana-voice-bot/app/__init__.py b/services/mana-voice-bot/app/__init__.py
new file mode 100644
index 000000000..b51cb8ece
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+++ b/services/mana-voice-bot/app/__init__.py
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+# Mana Voice Bot
diff --git a/services/mana-voice-bot/app/main.py b/services/mana-voice-bot/app/main.py
new file mode 100644
index 000000000..5115c98ed
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+++ b/services/mana-voice-bot/app/main.py
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+"""
+Mana Voice Bot - German Voice Assistant
+
+Complete voice-to-voice pipeline:
+1. STT: Whisper (mana-stt) - Speech to Text
+2. LLM: Ollama (Gemma/Qwen) - Text Processing
+3. TTS: Edge TTS - Text to Speech
+
+Optimized for German language.
+"""
+
+import asyncio
+import logging
+import os
+import tempfile
+import time
+from contextlib import asynccontextmanager
+from typing import Optional
+
+import aiohttp
+import edge_tts
+from fastapi import FastAPI, File, Form, HTTPException, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import Response, StreamingResponse
+from pydantic import BaseModel, Field
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+# Configuration
+PORT = int(os.getenv("PORT", "3050"))
+STT_URL = os.getenv("STT_URL", "http://localhost:3020")
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://localhost:11434")
+DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "gemma3:4b")
+DEFAULT_VOICE = os.getenv("DEFAULT_VOICE", "de-DE-ConradNeural")
+SYSTEM_PROMPT = os.getenv("SYSTEM_PROMPT", """Du bist ein freundlicher und hilfreicher deutscher Sprachassistent.
+Antworte immer auf Deutsch, kurz und prägnant.
+Halte deine Antworten unter 3 Sätzen, es sei denn, der Nutzer fragt nach mehr Details.""")
+
+CORS_ORIGINS = os.getenv(
+    "CORS_ORIGINS",
+    "https://mana.how,http://localhost:5173,http://localhost:3000",
+).split(",")
+
+# German Edge TTS voices
+GERMAN_VOICES = {
+    "de-DE-ConradNeural": "Male - Conrad (Professional)",
+    "de-DE-KatjaNeural": "Female - Katja (Natural)",
+    "de-DE-AmalaNeural": "Female - Amala (Friendly)",
+    "de-DE-BerndNeural": "Male - Bernd (Calm)",
+    "de-DE-ChristophNeural": "Male - Christoph (News)",
+    "de-DE-ElkeNeural": "Female - Elke (Warm)",
+    "de-DE-KillianNeural": "Male - Killian (Casual)",
+    "de-DE-KlarissaNeural": "Female - Klarissa (Cheerful)",
+    "de-DE-KlausNeural": "Male - Klaus (Storyteller)",
+    "de-DE-LouisaNeural": "Female - Louisa (Assistant)",
+    "de-DE-TanjaNeural": "Female - Tanja (Business)",
+}
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Application lifespan manager."""
+    logger.info(f"Starting Mana Voice Bot on port {PORT}")
+    logger.info(f"STT Service: {STT_URL}")
+    logger.info(f"Ollama: {OLLAMA_URL}")
+    logger.info(f"Default Model: {DEFAULT_MODEL}")
+    logger.info(f"Default Voice: {DEFAULT_VOICE}")
+    yield
+    logger.info("Shutting down Mana Voice Bot")
+
+
+app = FastAPI(
+    title="Mana Voice Bot",
+    description="German voice-to-voice assistant using Whisper + Ollama + Edge TTS",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# ============================================================================
+# Models
+# ============================================================================
+
+
+class ChatRequest(BaseModel):
+    """Text chat request."""
+    message: str = Field(..., description="User message")
+    model: str = Field(DEFAULT_MODEL, description="Ollama model to use")
+    voice: str = Field(DEFAULT_VOICE, description="TTS voice")
+    system_prompt: Optional[str] = Field(None, description="Custom system prompt")
+
+
+class TranscribeResponse(BaseModel):
+    """Transcription response."""
+    text: str
+    language: str
+    duration: float
+
+
+class ChatResponse(BaseModel):
+    """Chat response with text."""
+    user_text: str
+    assistant_text: str
+    model: str
+    processing_time: float
+
+
+class VoiceResponse(BaseModel):
+    """Voice processing response metadata."""
+    user_text: str
+    assistant_text: str
+    model: str
+    voice: str
+    stt_time: float
+    llm_time: float
+    tts_time: float
+    total_time: float
+
+
+# ============================================================================
+# Service Functions
+# ============================================================================
+
+
+async def transcribe_audio(audio_bytes: bytes, language: str = "de") -> dict:
+    """Transcribe audio using mana-stt (Whisper)."""
+    async with aiohttp.ClientSession() as session:
+        data = aiohttp.FormData()
+        data.add_field("file", audio_bytes, filename="audio.wav", content_type="audio/wav")
+        data.add_field("language", language)
+
+        async with session.post(f"{STT_URL}/transcribe", data=data) as response:
+            if response.status != 200:
+                error = await response.text()
+                raise HTTPException(status_code=500, detail=f"STT error: {error}")
+            return await response.json()
+
+
+async def chat_with_ollama(
+    message: str,
+    model: str = DEFAULT_MODEL,
+    system_prompt: str = SYSTEM_PROMPT,
+) -> str:
+    """Send message to Ollama and get response."""
+    async with aiohttp.ClientSession() as session:
+        payload = {
+            "model": model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": message},
+            ],
+            "stream": False,
+        }
+
+        async with session.post(
+            f"{OLLAMA_URL}/api/chat",
+            json=payload,
+            timeout=aiohttp.ClientTimeout(total=120),
+        ) as response:
+            if response.status != 200:
+                error = await response.text()
+                raise HTTPException(status_code=500, detail=f"Ollama error: {error}")
+
+            result = await response.json()
+            return result.get("message", {}).get("content", "")
+
+
+async def synthesize_speech(text: str, voice: str = DEFAULT_VOICE) -> bytes:
+    """Synthesize speech using Edge TTS."""
+    if voice not in GERMAN_VOICES:
+        voice = DEFAULT_VOICE
+
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as f:
+        output_path = f.name
+
+    try:
+        communicate = edge_tts.Communicate(text, voice)
+        await communicate.save(output_path)
+
+        with open(output_path, "rb") as f:
+            return f.read()
+    finally:
+        if os.path.exists(output_path):
+            os.unlink(output_path)
+
+
+# ============================================================================
+# Endpoints
+# ============================================================================
+
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint."""
+    # Check STT service
+    stt_ok = False
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(f"{STT_URL}/health", timeout=aiohttp.ClientTimeout(total=5)) as r:
+                stt_ok = r.status == 200
+    except Exception:
+        pass
+
+    # Check Ollama
+    ollama_ok = False
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(f"{OLLAMA_URL}/api/tags", timeout=aiohttp.ClientTimeout(total=5)) as r:
+                ollama_ok = r.status == 200
+    except Exception:
+        pass
+
+    return {
+        "status": "healthy" if (stt_ok and ollama_ok) else "degraded",
+        "services": {
+            "stt": "ok" if stt_ok else "unavailable",
+            "ollama": "ok" if ollama_ok else "unavailable",
+            "tts": "ok",  # Edge TTS is always available (cloud API)
+        },
+        "config": {
+            "default_model": DEFAULT_MODEL,
+            "default_voice": DEFAULT_VOICE,
+        },
+    }
+
+
+@app.get("/voices")
+async def list_voices():
+    """List available German voices."""
+    return {
+        "voices": GERMAN_VOICES,
+        "default": DEFAULT_VOICE,
+    }
+
+
+@app.get("/models")
+async def list_models():
+    """List available Ollama models."""
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(f"{OLLAMA_URL}/api/tags") as response:
+                if response.status == 200:
+                    data = await response.json()
+                    models = [m["name"] for m in data.get("models", [])]
+                    return {"models": models, "default": DEFAULT_MODEL}
+    except Exception as e:
+        logger.error(f"Failed to get models: {e}")
+
+    return {"models": [], "default": DEFAULT_MODEL, "error": "Could not fetch models"}
+
+
+@app.post("/transcribe", response_model=TranscribeResponse)
+async def transcribe(
+    audio: UploadFile = File(..., description="Audio file to transcribe"),
+    language: str = Form("de", description="Language code"),
+):
+    """Transcribe audio to text using Whisper."""
+    start = time.time()
+
+    audio_bytes = await audio.read()
+    if len(audio_bytes) == 0:
+        raise HTTPException(status_code=400, detail="Audio file is empty")
+
+    result = await transcribe_audio(audio_bytes, language)
+
+    return TranscribeResponse(
+        text=result.get("text", ""),
+        language=result.get("language", language),
+        duration=time.time() - start,
+    )
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """Text chat with the LLM."""
+    start = time.time()
+
+    response_text = await chat_with_ollama(
+        message=request.message,
+        model=request.model,
+        system_prompt=request.system_prompt or SYSTEM_PROMPT,
+    )
+
+    return ChatResponse(
+        user_text=request.message,
+        assistant_text=response_text,
+        model=request.model,
+        processing_time=time.time() - start,
+    )
+
+
+@app.post("/chat/audio")
+async def chat_audio(request: ChatRequest):
+    """Text chat with audio response."""
+    start = time.time()
+
+    # Get LLM response
+    llm_start = time.time()
+    response_text = await chat_with_ollama(
+        message=request.message,
+        model=request.model,
+        system_prompt=request.system_prompt or SYSTEM_PROMPT,
+    )
+    llm_time = time.time() - llm_start
+
+    # Synthesize speech
+    tts_start = time.time()
+    audio_bytes = await synthesize_speech(response_text, request.voice)
+    tts_time = time.time() - tts_start
+
+    return Response(
+        content=audio_bytes,
+        media_type="audio/mpeg",
+        headers={
+            "X-User-Text": request.message[:100],
+            "X-Assistant-Text": response_text[:200],
+            "X-Model": request.model,
+            "X-Voice": request.voice,
+            "X-LLM-Time": str(round(llm_time, 2)),
+            "X-TTS-Time": str(round(tts_time, 2)),
+            "X-Total-Time": str(round(time.time() - start, 2)),
+        },
+    )
+
+
+@app.post("/voice")
+async def voice_to_voice(
+    audio: UploadFile = File(..., description="Audio input"),
+    model: str = Form(DEFAULT_MODEL, description="Ollama model"),
+    voice: str = Form(DEFAULT_VOICE, description="TTS voice"),
+    language: str = Form("de", description="Input language"),
+    system_prompt: Optional[str] = Form(None, description="Custom system prompt"),
+):
+    """
+    Complete voice-to-voice pipeline.
+
+    1. Transcribe audio input (Whisper)
+    2. Process with LLM (Ollama)
+    3. Synthesize response (Edge TTS)
+
+    Returns audio response with metadata headers.
+    """
+    total_start = time.time()
+
+    # Read audio
+    audio_bytes = await audio.read()
+    if len(audio_bytes) == 0:
+        raise HTTPException(status_code=400, detail="Audio file is empty")
+
+    # 1. Speech-to-Text
+    stt_start = time.time()
+    try:
+        stt_result = await transcribe_audio(audio_bytes, language)
+        user_text = stt_result.get("text", "").strip()
+    except Exception as e:
+        logger.error(f"STT failed: {e}")
+        raise HTTPException(status_code=500, detail=f"Transcription failed: {e}")
+    stt_time = time.time() - stt_start
+
+    if not user_text:
+        raise HTTPException(status_code=400, detail="Could not transcribe audio - no speech detected")
+
+    logger.info(f"Transcribed: {user_text}")
+
+    # 2. LLM Processing
+    llm_start = time.time()
+    try:
+        assistant_text = await chat_with_ollama(
+            message=user_text,
+            model=model,
+            system_prompt=system_prompt or SYSTEM_PROMPT,
+        )
+    except Exception as e:
+        logger.error(f"LLM failed: {e}")
+        raise HTTPException(status_code=500, detail=f"LLM processing failed: {e}")
+    llm_time = time.time() - llm_start
+
+    logger.info(f"LLM response: {assistant_text[:100]}...")
+
+    # 3. Text-to-Speech
+    tts_start = time.time()
+    try:
+        response_audio = await synthesize_speech(assistant_text, voice)
+    except Exception as e:
+        logger.error(f"TTS failed: {e}")
+        raise HTTPException(status_code=500, detail=f"Speech synthesis failed: {e}")
+    tts_time = time.time() - tts_start
+
+    total_time = time.time() - total_start
+
+    logger.info(f"Pipeline complete - STT: {stt_time:.2f}s, LLM: {llm_time:.2f}s, TTS: {tts_time:.2f}s, Total: {total_time:.2f}s")
+
+    # Return audio with metadata
+    return Response(
+        content=response_audio,
+        media_type="audio/mpeg",
+        headers={
+            "X-User-Text": user_text[:200].replace("\n", " "),
+            "X-Assistant-Text": assistant_text[:500].replace("\n", " "),
+            "X-Model": model,
+            "X-Voice": voice,
+            "X-STT-Time": str(round(stt_time, 2)),
+            "X-LLM-Time": str(round(llm_time, 2)),
+            "X-TTS-Time": str(round(tts_time, 2)),
+            "X-Total-Time": str(round(total_time, 2)),
+        },
+    )
+
+
+@app.post("/voice/metadata", response_model=VoiceResponse)
+async def voice_to_voice_with_metadata(
+    audio: UploadFile = File(..., description="Audio input"),
+    model: str = Form(DEFAULT_MODEL, description="Ollama model"),
+    voice: str = Form(DEFAULT_VOICE, description="TTS voice"),
+    language: str = Form("de", description="Input language"),
+    system_prompt: Optional[str] = Form(None, description="Custom system prompt"),
+):
+    """
+    Voice-to-voice pipeline returning JSON metadata (without audio).
+    Useful for debugging or when you need the text response.
+    """
+    total_start = time.time()
+
+    audio_bytes = await audio.read()
+    if len(audio_bytes) == 0:
+        raise HTTPException(status_code=400, detail="Audio file is empty")
+
+    # STT
+    stt_start = time.time()
+    stt_result = await transcribe_audio(audio_bytes, language)
+    user_text = stt_result.get("text", "").strip()
+    stt_time = time.time() - stt_start
+
+    if not user_text:
+        raise HTTPException(status_code=400, detail="No speech detected")
+
+    # LLM
+    llm_start = time.time()
+    assistant_text = await chat_with_ollama(
+        message=user_text,
+        model=model,
+        system_prompt=system_prompt or SYSTEM_PROMPT,
+    )
+    llm_time = time.time() - llm_start
+
+    # TTS (just measure time, don't return audio)
+    tts_start = time.time()
+    await synthesize_speech(assistant_text, voice)
+    tts_time = time.time() - tts_start
+
+    return VoiceResponse(
+        user_text=user_text,
+        assistant_text=assistant_text,
+        model=model,
+        voice=voice,
+        stt_time=round(stt_time, 2),
+        llm_time=round(llm_time, 2),
+        tts_time=round(tts_time, 2),
+        total_time=round(time.time() - total_start, 2),
+    )
+
+
+@app.post("/tts")
+async def text_to_speech(
+    text: str = Form(..., description="Text to synthesize"),
+    voice: str = Form(DEFAULT_VOICE, description="Voice to use"),
+):
+    """Direct text-to-speech synthesis."""
+    if not text.strip():
+        raise HTTPException(status_code=400, detail="Text is empty")
+
+    audio_bytes = await synthesize_speech(text, voice)
+
+    return Response(
+        content=audio_bytes,
+        media_type="audio/mpeg",
+        headers={
+            "X-Voice": voice,
+            "X-Text-Length": str(len(text)),
+        },
+    )
+
+
+# ============================================================================
+# Main
+# ============================================================================
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=PORT)
diff --git a/services/mana-voice-bot/requirements.txt b/services/mana-voice-bot/requirements.txt
new file mode 100644
index 000000000..8488e8b54
--- /dev/null
+++ b/services/mana-voice-bot/requirements.txt
@@ -0,0 +1,6 @@
+fastapi>=0.100.0
+uvicorn>=0.20.0
+aiohttp>=3.8.0
+edge-tts>=6.0.0
+python-multipart>=0.0.6
+pydantic>=2.0.0
diff --git a/services/mana-voice-bot/setup.sh b/services/mana-voice-bot/setup.sh
new file mode 100755
index 000000000..23a24b824
--- /dev/null
+++ b/services/mana-voice-bot/setup.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+# Setup script for Mana Voice Bot
+
+set -e
+
+echo "Setting up Mana Voice Bot..."
+
+# Create virtual environment
+if [ ! -d "venv" ]; then
+    echo "Creating virtual environment..."
+    python3 -m venv venv
+fi
+
+# Activate and install dependencies
+source venv/bin/activate
+pip install --upgrade pip
+pip install -r requirements.txt
+
+echo ""
+echo "Setup complete!"
+echo ""
+echo "To start the service:"
+echo "  source venv/bin/activate"
+echo "  uvicorn app.main:app --host 0.0.0.0 --port 3050 --reload"
+echo ""
+echo "Or use the start script:"
+echo "  ./start.sh"
diff --git a/services/mana-voice-bot/start.sh b/services/mana-voice-bot/start.sh
new file mode 100755
index 000000000..db60e7e3a
--- /dev/null
+++ b/services/mana-voice-bot/start.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+# Start Mana Voice Bot
+
+cd "$(dirname "$0")"
+source venv/bin/activate
+
+export PORT=${PORT:-3050}
+export STT_URL=${STT_URL:-http://localhost:3020}
+export OLLAMA_URL=${OLLAMA_URL:-http://localhost:11434}
+export DEFAULT_MODEL=${DEFAULT_MODEL:-gemma3:4b}
+export DEFAULT_VOICE=${DEFAULT_VOICE:-de-DE-ConradNeural}
+
+echo "Starting Mana Voice Bot..."
+echo "  Port: $PORT"
+echo "  STT: $STT_URL"
+echo "  Ollama: $OLLAMA_URL"
+echo "  Model: $DEFAULT_MODEL"
+echo "  Voice: $DEFAULT_VOICE"
+echo ""
+
+exec uvicorn app.main:app --host 0.0.0.0 --port $PORT