diff --git a/SETUP.md b/SETUP.md
index 5211d13..e6a3992 100644
--- a/SETUP.md
+++ b/SETUP.md
@@ -40,6 +40,11 @@ Create `.env` in the project root:
 ```bash
 cat > .env << 'EOF'
 GROQ_API_KEY=your_groq_key_here
+# Required for the realtime demo (constella-realtime); not used by the legacy
+# push-to-talk demo (constella-demo) or the eval harness.
+CARTESIA_API_KEY=your_cartesia_key_here
+# Optional: override the default Cartesia voice (UUID from play.cartesia.ai/voices)
+# CARTESIA_VOICE_ID=
 ANTHROPIC_API_KEY=optional_for_llm_judge_eval
 HF_HOME=/Users/hgz/.cache/huggingface
 EOF
diff --git a/constella/demo/realtime.py b/constella/demo/realtime.py
new file mode 100644
index 0000000..5b2c339
--- /dev/null
+++ b/constella/demo/realtime.py
@@ -0,0 +1,311 @@
+"""Realtime voice demo — auto-commit on silence via fastrtc + Cartesia.
+
+Per-turn pipeline:
+    mic → fastrtc VAD → Groq Whisper → run_turn() → Cartesia streaming TTS → speaker
+
+UI is a Gradio Blocks layout: mic + text input + examples on the left, Ana's
+reply + running transcript on the right, specialist verdict JSON at the bottom.
+
+Usage:
+    constella-realtime                # localhost:7860
+    constella-realtime --port 8080
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Literal
+
+import gradio as gr
+import numpy as np
+
+from constella.orchestrator import run_turn
+from constella.realtime.audio import cartesia_pcm_to_numpy, numpy_to_wav_tempfile
+from constella.realtime.tts import SAMPLE_RATE as TTS_SAMPLE_RATE, stream_tts
+from constella.schemas import ConversationState, PatientContext
+
+log = logging.getLogger(__name__)
+
+DEFAULT_PATIENT = (
+    Path(__file__).resolve().parents[1] / "eval" / "scenarios" / "patient_maria.json"
+)
+
+EXAMPLE_LINES = [
+    "Hi, yes this is Maria.",
+    "I checked my sugar this morning and it was 142.",
+    "Hola, soy María. ¿Cómo está usted?",
+    "Pues mi blood sugar esta mañana estaba en 165, no muy mal.",
+    "Sí, tomé el metformin con breakfast pero el insulin lo olvidé last night.",
+    "Tengo un dolor fuerte en el pecho y me duele el brazo izquierdo.",
+]
+
+_SPANISH_FUNCTION_WORDS = frozenset({
+    "hola", "está", "sí", "bueno", "pero", "porque", "cómo", "qué",
+    "dónde", "entonces", "muy", "mucho", "también", "señora", "usted",
+    "soy", "eres", "tomó", "gracias", "bien", "tiene", "desde", "hospital",
+    "medicamento", "azúcar", "insulina", "doctor", "doctora", "ahora",
+    "poco", "ayer", "mañana", "preocupes", "quiero", "puedes",
+})
+
+
+def _load_patient() -> PatientContext:
+    return PatientContext.model_validate_json(DEFAULT_PATIENT.read_text())
+
+
+def _detect_reply_language(text: str) -> Literal["en", "es"] | None:
+    """Heuristic language detection on Ana's reply text.
+
+    We can't use the language specialist's verdict as the TTS hint because it
+    runs on the PATIENT's utterance — Ana may reply in a different language
+    (her prompt tells her to match the patient's register, which often means
+    Spanish even when the patient said a greeting in English). Cartesia needs
+    the language of the TEXT IT IS SPEAKING, which is Ana's reply.
+    """
+    if any(c in text for c in "ñ¿¡"):
+        return "es"
+    lower = text.lower()
+    if any(c in lower for c in "áéíóú"):
+        return "es"
+    words = set(lower.split())
+    if len(words & _SPANISH_FUNCTION_WORDS) >= 2:
+        return "es"
+    return "en"
+
+
+def _transcribe(wav_path: Path) -> str:
+    """Groq Whisper — batch, but fastrtc only calls us once per VAD pause so
+    the clip is already short (typically 2-5 s)."""
+    from openai import OpenAI
+
+    api_key = os.environ.get("GROQ_API_KEY")
+    if not api_key:
+        raise RuntimeError("GROQ_API_KEY required for ASR in the realtime demo.")
+    client = OpenAI(api_key=api_key, base_url="https://api.groq.com/openai/v1")
+    with open(wav_path, "rb") as f:
+        resp = client.audio.transcriptions.create(
+            model="whisper-large-v3-turbo",
+            file=f,
+            response_format="text",
+        )
+    return str(resp).strip()
+
+
+def _format_transcript(state: ConversationState) -> str:
+    if not state.history:
+        return "_(start of call)_"
+    lines = []
+    for turn in state.history[-20:]:
+        who = "**María:**" if turn.speaker == "patient" else "**Nurse Ana:**"
+        lines.append(f"{who} {turn.text}")
+    return "\n\n".join(lines)
+
+
+def _build_verdict(result) -> str:
+    verdict = {
+        "action": result.action.kind,
+        "latency_ms": {
+            "primary": round(result.primary_latency_ms),
+            "specialists_parallel": round(result.specialist_latency_ms),
+            "total": round(result.total_latency_ms),
+        },
+        "language": result.language.model_dump() if result.language else None,
+        "medication": result.medication.model_dump() if result.medication else None,
+        "labs": result.labs.model_dump() if result.labs else None,
+        "escalation": result.escalation.model_dump() if result.escalation else None,
+    }
+    return json.dumps(verdict, indent=2, default=str)
+
+
+# Module-level conversation state — single-user demo. Keeping it here (rather
+# than in gr.State) is deliberate: threading state through AdditionalOutputs
+# triggers a state_change event on every turn, which re-enters the
+# on_additional_outputs handler and silently drops subsequent UI updates.
+# For multi-user prod we'd key this by fastrtc session_id.
+_state: ConversationState | None = None
+
+
+def _ensure_state() -> ConversationState:
+    global _state
+    if _state is None:
+        _state = ConversationState(patient=_load_patient())
+    return _state
+
+
+def _voice_handler(audio, _webrtc_value):
+    """fastrtc handler — runs on every VAD pause.
+
+    Signature note: fastrtc prepends a `"__webrtc_value__"` placeholder when
+    the WebRTC component's value is passed as a string (see
+    fastrtc/tracks.py:set_args). After the audio replacement, the args become
+    (audio_tuple, webrtc_value). No other inputs are wired to the stream, so
+    the handler takes exactly two positional args.
+    """
+    from fastrtc import AdditionalOutputs
+    global _state
+
+    state = _ensure_state()
+    sample_rate, np_audio = audio
+    wav_path = numpy_to_wav_tempfile(sample_rate, np_audio)
+    try:
+        patient_text = _transcribe(wav_path)
+    finally:
+        wav_path.unlink(missing_ok=True)
+
+    if not patient_text:
+        log.info("empty transcription, skipping turn")
+        return
+
+    log.info("patient: %s", patient_text)
+    result = run_turn(state, patient_text)
+    _state = result.state
+    log.info(
+        "nurse [%s, %.0f ms]: %s",
+        result.action.kind,
+        result.total_latency_ms,
+        result.nurse_text,
+    )
+
+    reply_lang = _detect_reply_language(result.nurse_text)
+
+    yield AdditionalOutputs(
+        result.nurse_text,
+        _build_verdict(result),
+        _format_transcript(result.state),
+    )
+
+    for pcm_chunk in stream_tts(result.nurse_text, language=reply_lang):
+        yield cartesia_pcm_to_numpy(pcm_chunk, TTS_SAMPLE_RATE)
+
+
+def _text_handler(patient_text: str):
+    """Text-only fallback path: no audio playback, just text updates."""
+    global _state
+    if not patient_text or not patient_text.strip():
+        state = _ensure_state()
+        return "", "{}", _format_transcript(state)
+
+    state = _ensure_state()
+    result = run_turn(state, patient_text.strip())
+    _state = result.state
+    return (
+        result.nurse_text,
+        _build_verdict(result),
+        _format_transcript(result.state),
+    )
+
+
+def build_ui() -> gr.Blocks:
+    from fastrtc import ReplyOnPause, WebRTC
+
+    with gr.Blocks(title="Constella — bilingual constellation voice agent") as demo:
+        gr.Markdown(
+            "# Constella\n"
+            "You are **María González** — 58-year-old diabetic patient, just out of hospital. "
+            "**Nurse Ana** will follow up. Speak or type. English, Spanish, or both mid-sentence.\n\n"
+            "> **Voice path:** click **Record** and speak; Ana answers the moment you pause.  \n"
+            "> **Text path:** type below and press Enter."
+        )
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### You (María)")
+                webrtc = WebRTC(
+                    mode="send-receive",
+                    modality="audio",
+                    label="Speak",
+                    full_screen=False,
+                    height=240,
+                    button_labels={
+                        "start": "Record",
+                        "stop": "Stop",
+                        "waiting": "Ana is replying…",
+                    },
+                )
+                text_in = gr.Textbox(
+                    placeholder="or type here...",
+                    label="Text",
+                    lines=2,
+                )
+                send_btn = gr.Button("Send text", variant="secondary", size="sm")
+                gr.Examples(
+                    examples=[[ex] for ex in EXAMPLE_LINES],
+                    inputs=[text_in],
+                    label="Try these",
+                )
+
+            with gr.Column(scale=1):
+                gr.Markdown("### Nurse Ana")
+                nurse_text = gr.Textbox(
+                    label="Latest reply",
+                    lines=4,
+                    interactive=False,
+                )
+                transcript = gr.Markdown(
+                    value="_(start of call)_",
+                    label="Conversation transcript",
+                )
+
+        verdict_out = gr.Code(
+            label="What the 4 specialists saw (action + latency breakdown)",
+            language="json",
+            lines=18,
+        )
+
+        # Voice path — audio in, audio out, text/verdict/transcript as additional outputs.
+        webrtc.stream(
+            fn=ReplyOnPause(_voice_handler),
+            inputs=[webrtc],
+            outputs=[webrtc],
+            time_limit=60,
+        )
+        webrtc.on_additional_outputs(
+            lambda nt, v, t: (nt, v, t),
+            outputs=[nurse_text, verdict_out, transcript],
+        )
+
+        # Text path (no audio).
+        text_in.submit(
+            _text_handler,
+            inputs=[text_in],
+            outputs=[nurse_text, verdict_out, transcript],
+        )
+        send_btn.click(
+            _text_handler,
+            inputs=[text_in],
+            outputs=[nurse_text, verdict_out, transcript],
+        )
+
+    return demo
+
+
+def main() -> int:
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s %(name)s %(message)s",
+    )
+    provider_override = os.environ.get("CONSTELLA_PROVIDER", "").lower()
+    if os.environ.get("GROQ_API_KEY") and provider_override != "openrouter":
+        log.warning(
+            "Using Groq for LLM inference. Free-tier rate limits (6000 TPM on 8B) "
+            "will cause multi-second backoff on realtime bursts — Constella fires 5 "
+            "concurrent calls per turn. If you see latency > 3 s per turn, either "
+            "set CONSTELLA_PROVIDER=openrouter in .env or upgrade to Groq Dev tier "
+            "at https://console.groq.com/settings/billing."
+        )
+    if not os.environ.get("CARTESIA_API_KEY"):
+        log.error(
+            "CARTESIA_API_KEY is not set. The voice path will fail on the first "
+            "TTS call. Add it to .env and restart."
+        )
+    p = argparse.ArgumentParser()
+    p.add_argument("--port", type=int, default=7860)
+    args = p.parse_args()
+    build_ui().launch(server_port=args.port)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/constella/primary.py b/constella/primary.py
index 3de51e2..f1ccaf4 100644
--- a/constella/primary.py
+++ b/constella/primary.py
@@ -10,9 +10,63 @@
 """
 from __future__ import annotations
 
+from typing import Literal
+
 from constella.llm import PRIMARY_MODEL, chat
 from constella.schemas import ConversationState, Turn
 
+_SPANISH_MARKERS = frozenset("ñ¿¡áéíóú")
+_SPANISH_FUNCTION_WORDS = frozenset({
+    "hola", "está", "estás", "estas", "esta", "sí", "bueno", "buenos",
+    "buenas", "dias", "días", "tardes", "noches", "pero", "porque",
+    "como", "cómo", "qué", "que", "dónde", "donde", "entonces", "muy",
+    "mucho", "mucha", "también", "tambien", "señora", "señor", "usted",
+    "soy", "eres", "tomó", "gracias", "bien", "tiene", "tengo", "tienes",
+    "desde", "hospital", "medicamento", "azúcar", "insulina", "doctor",
+    "doctora", "ahora", "poco", "ayer", "mañana", "preocupes", "quiero",
+    "puedes", "ese", "con", "por", "para", "del", "las", "los", "nada",
+    "algo", "todo", "toda", "hace", "hacer", "estoy", "siento", "duele",
+})
+_ENGLISH_FUNCTION_WORDS = frozenset({
+    "the", "and", "is", "are", "was", "were", "i", "my", "you", "your",
+    "this", "that", "have", "has", "had", "with", "for", "but", "from",
+    "of", "in", "on", "to", "it", "a", "an", "not", "don't", "didn't",
+})
+
+
+def detect_utterance_language(text: str) -> Literal["en", "es", "mix"]:
+    """Heuristic language detector for a patient utterance.
+
+    Purpose: give the primary agent an unambiguous signal about what language
+    the patient JUST spoke, so Ana replies in the same language instead of
+    defaulting to the patient's profile language. Used before the language
+    specialist has a chance to weigh in (specialists run after primary).
+
+    Returns "mix" when both languages are present in detectable density.
+    """
+    lower = text.lower()
+    has_spanish_markers = any(c in lower for c in _SPANISH_MARKERS)
+    words = set(lower.split())
+    spanish_word_hits = len(words & _SPANISH_FUNCTION_WORDS)
+    english_word_hits = len(words & _ENGLISH_FUNCTION_WORDS)
+    looks_spanish = has_spanish_markers or spanish_word_hits >= 2
+    looks_english = english_word_hits >= 2
+    if looks_spanish and looks_english:
+        return "mix"
+    if looks_spanish:
+        return "es"
+    return "en"
+
+
+_LANGUAGE_DIRECTIVE = {
+    "en": "The patient just spoke in English. Reply in English.",
+    "es": "La paciente acaba de hablar en español. Responde en español.",
+    "mix": (
+        "The patient code-switched between English and Spanish. Match their "
+        "register and feel free to code-switch naturally."
+    ),
+}
+
 PRIMARY_SYSTEM_PROMPT = """You are Nurse Ana, a warm, experienced registered nurse making a post-discharge follow-up call.
 
 Your job on each turn:
@@ -30,7 +84,7 @@
 
 def build_user_prompt(state: ConversationState, last_patient_text: str) -> str:
     history_lines = []
-    for turn in state.history[-6:]:  # last 3 exchanges
+    for turn in state.history[-20:]:  # last 20 turns (~10 exchanges)
         speaker = "Patient" if turn.speaker == "patient" else "Nurse Ana"
         history_lines.append(f"{speaker}: {turn.text}")
     history_block = "\n".join(history_lines) if history_lines else "(start of call)"
@@ -40,11 +94,14 @@ def build_user_prompt(state: ConversationState, last_patient_text: str) -> str:
         for m in state.patient.medications
     )
 
+    detected_lang = detect_utterance_language(last_patient_text)
+    directive = _LANGUAGE_DIRECTIVE[detected_lang]
+
     return f"""Patient profile:
 - Name: {state.patient.name}
 - Age: {state.patient.age}
 - Pronouns: {state.patient.pronouns}
-- Primary language: {state.patient.primary_language}
+- Preferred language at home: {state.patient.primary_language} (but ignore this if they just spoke a different language — see directive below)
 - Conditions: {", ".join(state.patient.conditions)}
 - Discharge summary: {state.patient.discharge_summary}
 - Medications:
@@ -56,7 +113,9 @@ def build_user_prompt(state: ConversationState, last_patient_text: str) -> str:
 Patient just said:
 "{last_patient_text}"
 
-Respond as Nurse Ana. One to three short sentences. Match the patient's language."""
+LANGUAGE DIRECTIVE (mandatory): {directive}
+
+Respond as Nurse Ana. One to three short sentences."""
 
 
 def primary_respond(state: ConversationState, last_patient_text: str) -> str:
diff --git a/constella/realtime/__init__.py b/constella/realtime/__init__.py
new file mode 100644
index 0000000..8bc1670
--- /dev/null
+++ b/constella/realtime/__init__.py
@@ -0,0 +1,5 @@
+"""Realtime voice pipeline: fastrtc transport + Cartesia streaming TTS.
+
+The realtime demo (`constella-realtime`) uses these modules. The legacy
+push-to-talk Gradio demo in `constella.demo.app` does not touch this package.
+"""
diff --git a/constella/realtime/audio.py b/constella/realtime/audio.py
new file mode 100644
index 0000000..e803e13
--- /dev/null
+++ b/constella/realtime/audio.py
@@ -0,0 +1,44 @@
+"""Audio format conversion for the realtime pipeline.
+
+fastrtc hands us `(sample_rate, np.ndarray)` tuples; Groq Whisper wants a file
+path to a WAV; Cartesia streams raw `pcm_f32le` bytes back to us. These helpers
+bridge those three representations.
+"""
+from __future__ import annotations
+
+import tempfile
+import wave
+from pathlib import Path
+
+import numpy as np
+
+
+def numpy_to_wav_tempfile(sample_rate: int, audio: np.ndarray) -> Path:
+    """Write an fastrtc-shape audio array to a temp WAV that Whisper accepts.
+
+    fastrtc delivers `(1, N)` mono audio. We squeeze to 1D, coerce to int16,
+    and emit a standard 16-bit PCM WAV. Caller is responsible for deleting
+    the file once Whisper has finished with it.
+    """
+    mono = audio.squeeze()
+    if mono.dtype != np.int16:
+        if mono.dtype.kind == "f":
+            mono = np.clip(mono * 32767.0, -32768, 32767).astype(np.int16)
+        else:
+            mono = mono.astype(np.int16)
+
+    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    with wave.open(tmp.name, "wb") as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(int(sample_rate))
+        wf.writeframes(mono.tobytes())
+    return Path(tmp.name)
+
+
+def cartesia_pcm_to_numpy(
+    pcm_bytes: bytes, sample_rate: int
+) -> tuple[int, np.ndarray]:
+    """Convert a Cartesia `pcm_f32le` chunk into the `(sr, (1, N))` shape fastrtc expects."""
+    arr = np.frombuffer(pcm_bytes, dtype=np.float32)
+    return (sample_rate, arr.reshape(1, -1))
diff --git a/constella/realtime/tts.py b/constella/realtime/tts.py
new file mode 100644
index 0000000..a999c6e
--- /dev/null
+++ b/constella/realtime/tts.py
@@ -0,0 +1,89 @@
+"""Streaming TTS via Cartesia Sonic-3.
+
+Yields raw `pcm_f32le` bytes as the model synthesizes. The realtime handler
+converts those chunks into fastrtc audio frames so Ana's voice starts playing
+within ~100 ms of the first token arriving from the primary agent.
+
+Voices are native per language — a native-Spanish voice is used for Spanish
+replies and a native-English voice for English. Using a single multilingual
+voice makes the non-native language sound phonetically approximated (e.g.,
+American accent on Spanish), which is unusable for a bilingual healthcare
+agent. The tradeoff is that Ana's voice identity shifts slightly when she
+code-switches — analogous to a bilingual nurse in real life.
+"""
+from __future__ import annotations
+
+import logging
+import os
+from collections.abc import Iterator
+from typing import Literal
+
+log = logging.getLogger(__name__)
+
+# Native-English female, warm conversational — fits a U.S. nurse making a
+# follow-up call. Override via CARTESIA_VOICE_EN.
+_DEFAULT_VOICE_EN = "6ccbfb76-1fc6-48f7-b71d-91ac6298247b"  # Tessa - Kind Companion
+
+# Native-Latin-American-Spanish female, calm and professional — fits a nurse
+# talking to a U.S. Latino patient. Override via CARTESIA_VOICE_ES.
+_DEFAULT_VOICE_ES = "3597a26f-80ef-4bd5-8101-9699bc764917"  # Ximena - Calm Navigator
+
+_MODEL_ID = "sonic-3"
+SAMPLE_RATE = 44100
+
+
+def _pick_voice(language: Literal["en", "es"] | None) -> str:
+    """Pick a native voice for the given reply language.
+
+    Precedence:
+      1. CARTESIA_VOICE_ID — single-voice override (legacy behavior)
+      2. CARTESIA_VOICE_ES / CARTESIA_VOICE_EN — per-language overrides
+      3. Hard-coded defaults tuned for the healthcare-nurse register
+    """
+    single = os.environ.get("CARTESIA_VOICE_ID")
+    if single:
+        return single
+    if language == "es":
+        return os.environ.get("CARTESIA_VOICE_ES", _DEFAULT_VOICE_ES)
+    return os.environ.get("CARTESIA_VOICE_EN", _DEFAULT_VOICE_EN)
+
+
+def stream_tts(
+    text: str,
+    *,
+    language: Literal["en", "es"] | None = None,
+) -> Iterator[bytes]:
+    """Yield `pcm_f32le` audio chunks as Cartesia synthesizes `text`.
+
+    `language`: "en" or "es" selects a native voice AND passes the language
+    hint to Sonic-3. `None` falls back to the English voice (safest default).
+    """
+    from cartesia import Cartesia  # lazy import so pytest doesn't pull websockets
+
+    api_key = os.environ.get("CARTESIA_API_KEY")
+    if not api_key:
+        raise RuntimeError(
+            "CARTESIA_API_KEY is required for the realtime demo. Add it to .env."
+        )
+
+    voice_id = _pick_voice(language)
+    client = Cartesia(api_key=api_key)
+
+    with client.tts.websocket_connect() as conn:
+        ctx_kwargs: dict = {
+            "model_id": _MODEL_ID,
+            "voice": {"mode": "id", "id": voice_id},
+            "output_format": {
+                "container": "raw",
+                "encoding": "pcm_f32le",
+                "sample_rate": SAMPLE_RATE,
+            },
+        }
+        if language is not None:
+            ctx_kwargs["language"] = language
+        ctx = conn.context(**ctx_kwargs)
+        ctx.push(text)
+        ctx.no_more_inputs()
+        for response in ctx.receive():
+            if response.type == "chunk" and response.audio:
+                yield response.audio
diff --git a/docs/realtime_architecture.md b/docs/realtime_architecture.md
new file mode 100644
index 0000000..4131aa6
--- /dev/null
+++ b/docs/realtime_architecture.md
@@ -0,0 +1,207 @@
+# Realtime Voice — Backend Architecture
+
+> **Status:** design doc. Nothing in this document is implemented yet on this branch (`feat/realtime-voice`). This is the plan we're reviewing before writing code.
+
+## 1. Goal
+
+Replace the current record-send-reply Gradio UX with a phone-call feel:
+
+- Audio streams continuously in both directions over WebRTC.
+- Server-side VAD decides when the patient has stopped talking.
+- Ana's voice starts speaking 300-500 ms after the patient stops — not 2-3 s.
+- The patient can interrupt Ana mid-sentence (barge-in).
+- The **existing 4-specialist constellation is preserved** — it's Constella's whole point. We just swap out the I/O layer.
+
+What this is **not**: a rewrite into an end-to-end speech-to-speech model (OpenAI Realtime, Gemini Live). Those collapse the text LLM layer and would bypass the constellation entirely.
+
+## 2. Target stack
+
+| Stage | Today | Target | Why the swap |
+|---|---|---|---|
+| Transport | Gradio `gr.Audio` push-to-talk | WebRTC (Pipecat small-webrtc transport) | Bidirectional streaming, barge-in support |
+| VAD | None — user clicks stop | Silero VAD (local, runs in Pipecat) | No click, automatic turn boundaries |
+| ASR | Groq Whisper (batch) | Deepgram `nova-3` streaming | Streaming partials, ~150 ms finalization after EOT |
+| Primary LLM | Groq Llama 3.3 70B (sync) | Groq Llama 3.3 70B (streaming) | Stream tokens into TTS instead of waiting for full reply |
+| Specialists | Groq Llama 3.1 8B ×4, parallel | **unchanged** — Groq Llama 3.1 8B ×4, parallel | Constellation logic stays identical |
+| TTS | gTTS (batch MP3) | Cartesia Sonic-2 multilingual (streaming) | ~90 ms first-audible, real multilingual (en/es) |
+| Orchestration | single `run_turn()` call | Pipecat `Pipeline` of `FrameProcessor`s | Native streaming, interruption, turn-taking |
+
+**Keeping Groq for LLMs** because: (a) the constellation pattern has zero reason to change providers, (b) Groq's LPU latencies (~250 ms TTFT 70B, ~80 ms TTFT 8B) are what make streaming worth doing.
+
+## 3. Pipeline topology
+
+Pipecat organizes the runtime as a linear chain of `FrameProcessor`s. Audio frames, transcription frames, LLM frames, and TTS audio frames flow through in order. Our chain:
+
+```
+  Mic (WebRTC)
+       │ AudioRawFrame (20 ms chunks)
+       ▼
+  ┌─────────────────────────┐
+  │ SileroVADAnalyzer       │  decides when user started/stopped speaking
+  └─────────────┬───────────┘
+                │ UserStartedSpeakingFrame / UserStoppedSpeakingFrame
+                ▼
+  ┌─────────────────────────┐
+  │ DeepgramSTTService       │  streaming ASR, emits partial + final
+  └─────────────┬───────────┘
+                │ TranscriptionFrame (final, on VAD stop)
+                ▼
+  ┌─────────────────────────────────────────────────┐
+  │ ConstellationProcessor  ← THE NEW THING          │
+  │                                                  │
+  │  1. Append patient turn to ConversationState     │
+  │  2. Fan out in parallel:                         │
+  │       - primary.primary_respond() (streaming)    │
+  │       - language_specialist()                    │
+  │       - labs_specialist()                        │
+  │       - escalation_specialist()                  │
+  │       - (medication waits on primary's draft)    │
+  │  3. Await medication specialist on primary draft │
+  │  4. Merge verdicts → NextAction                  │
+  │  5. Rewrite if needed (blocks TTS)               │
+  │  6. Emit final TextFrame(s) for TTS              │
+  │  7. Append nurse turn to ConversationState       │
+  └─────────────┬───────────────────────────────────┘
+                │ TextFrame (nurse reply)
+                ▼
+  ┌─────────────────────────┐
+  │ CartesiaTTSService       │  streaming TTS, emits audio chunks
+  └─────────────┬───────────┘
+                │ TTSAudioRawFrame
+                ▼
+  Speaker (WebRTC)
+```
+
+The **critical design decision** is step 2: parallelism between primary and specialists.
+
+### 3a. Where the constellation plugs in
+
+Today `run_turn()` in `orchestrator.py` is a single synchronous function. In Pipecat it becomes a `FrameProcessor` subclass (`ConstellationProcessor`). The internals do the same work — primary + 4 specialists + merge — but the return is streamed as `TextFrame`s instead of returned as a string.
+
+```
+constella/
+├── orchestrator.py          # unchanged — run_turn() stays, used by eval harness
+├── realtime/                # NEW
+│   ├── __init__.py
+│   ├── pipeline.py          # Pipecat pipeline setup + transport wiring
+│   ├── constellation.py     # ConstellationProcessor (FrameProcessor subclass)
+│   └── session.py           # Per-call ConversationState, lifecycle hooks
+└── demo/
+    ├── app.py               # existing Gradio demo — stays, labelled "legacy"
+    └── realtime.py          # NEW constella-realtime entrypoint
+```
+
+Why a separate folder instead of modifying `orchestrator.py`: the eval harness (`constella-eval`) calls `run_turn()` synchronously and writes per-turn latency to a JSON report. Keeping it sync-friendly means eval keeps working without any changes.
+
+## 4. Running the specialists: parallelism strategy
+
+Three options, in increasing complexity:
+
+### Option A — constellation-then-speak (MVP)
+
+Run the whole `run_turn()` to completion, then stream the final text into TTS. Simplest mental model; easiest rollback if specialists hang.
+
+**Perceived latency (best case, Groq):**
+
+| Stage | Time |
+|---|---|
+| Deepgram ASR finalization after EOT | 150 ms |
+| Primary (70B, full response) | 500 ms |
+| Specialists in parallel (max) | 250 ms (overlaps with primary, effectively 0 ms added) |
+| Rewrite if needed | +400 ms (rare path) |
+| Cartesia TTS first audible | 90 ms |
+| **Total EOT → Ana's first audio** | **~740 ms happy path** |
+
+That's 3× better than today (~2.5 s) and below the 1 s threshold where conversation stops feeling snappy.
+
+### Option B — stream primary, run specialists concurrently, interrupt on flag
+
+Start TTS on primary's first tokens. Specialists run in parallel with TTS playback. If escalation/medication fires a flag, cut TTS mid-word with `BotInterruptionFrame` and replace with rewrite/escalation text.
+
+**Perceived latency:** ~300-400 ms EOT → first audio. Near-Gemini-Live feel.
+
+**Cost:** patient briefly hears the unsafe draft before it gets cut off. For a medication harm case (e.g., "take 200 mg of …" [cut off]), that's arguably worse than silence — it teaches the wrong dose for half a second.
+
+### Option C — hybrid: gate TTS on safety-critical specialists only
+
+- Start specialists on patient utterance immediately (language, labs, escalation run on patient text — no dependency on primary).
+- Primary streams into a buffer, not directly to TTS.
+- TTS starts as soon as **medication specialist returns safe** AND **escalation specialist returns non-urgent**.
+- Labs followup appended at end, no TTS gate needed.
+
+Roughly: if max(medication, escalation) < primary generation time, zero added latency over Option B — but with safety preserved.
+
+### Recommendation
+
+**Ship Option A first.** It's a 4× UX improvement with the lowest implementation risk and no new safety questions. Once that's stable, Option C is a latency optimization that can be added incrementally — the `ConstellationProcessor` interface doesn't change.
+
+## 5. Session state
+
+Today: `ConversationState` lives in Gradio's `gr.State` and gets serialized/deserialized per turn.
+
+New: `ConversationState` lives inside the `ConstellationProcessor` instance. One processor = one call. When the WebRTC session ends, the processor is destroyed. For multi-call history (not in scope for the MVP) we'd add a Redis-backed session store.
+
+The recent `[-20:]` history-window change applies automatically — the processor just calls `primary_respond(state, utterance)` with the accumulating state, same as today.
+
+## 6. Interruption / barge-in
+
+Pipecat handles this natively:
+
+1. While TTS is playing, VAD detects user speech.
+2. Pipeline emits `UserStartedSpeakingFrame`.
+3. Cartesia TTS receives `BotInterruptionFrame`, cancels current synthesis, flushes audio buffer.
+4. Speaker goes silent within ~100 ms.
+5. Deepgram starts transcribing the new utterance.
+
+No custom code needed beyond wiring these standard frames. The `ConstellationProcessor` needs to handle being mid-generation when interrupted — it should cancel the in-flight primary/specialist calls (their HTTP connections) and move on to the new patient utterance.
+
+## 7. Failure modes
+
+| Failure | Behavior |
+|---|---|
+| Deepgram WebSocket drops | Pipecat auto-reconnects; transient loss of a partial transcript, final still fires on EOT |
+| Specialist returns `None` (structured validation failed 3x) | Same as today — treat as "no verdict", orchestrator logs warning |
+| Primary LLM timeout | Fall back to a hard-coded "I didn't catch that — could you repeat?" safety phrase, don't surface an error to the user |
+| Cartesia TTS fails | Fall back to gTTS synchronously (slow, but better than silence); log alert |
+| VAD false trigger on background noise | Deepgram gets empty audio; the final `TranscriptionFrame` is `""` → `ConstellationProcessor` skips the turn |
+
+## 8. Env vars / API keys
+
+| Key | Provider | Purpose | Free tier? |
+|---|---|---|---|
+| `GROQ_API_KEY` | Groq | LLMs (unchanged) | yes |
+| `DEEPGRAM_API_KEY` | Deepgram | Streaming ASR | yes, $200 free credit |
+| `CARTESIA_API_KEY` | Cartesia | Streaming TTS | yes, free tier covers dev |
+
+Both Deepgram and Cartesia sign-ups are ~2 minutes each. You'll hand me those keys before Phase 3 below.
+
+## 9. Known tradeoffs vs today
+
+- **Spanglish code-switching on ASR.** Deepgram's `language=multi` detects one language per utterance, not mid-utterance switches. This is a regression vs. Groq Whisper, which handles Spanglish well. Mitigation: use Deepgram streaming for the live feel, then re-transcribe the final audio buffer with Groq Whisper for the canonical transcript that feeds specialists. Costs ~200 ms on EOT; acceptable. Alternative: evaluate Gladia Solaria, which markets Spanglish as a first-class feature.
+- **No multilingual gTTS-style free path.** Cartesia is paid (but cheap). gTTS stays as a fallback.
+- **Gradio text-input fallback.** The current demo's "type instead of speak" path is useful for dev. The new realtime page will have a text-input box too, but it bypasses ASR and feeds directly into the `ConstellationProcessor`.
+
+## 10. Implementation phases
+
+1. **Scaffold Pipecat dependencies.** Add `pipecat-ai[deepgram,cartesia,silero,webrtc]` to pyproject, pin versions, lock uv.
+2. **Wire `ConstellationProcessor`.** Wrap `run_turn()`. Keep Option A (no streaming primary) for v1. Unit-test with a fake Pipecat pipeline that injects `TranscriptionFrame`s.
+3. **Stand up the WebRTC transport.** Pipecat's `SmallWebRTCTransport` serves a static HTML page that opens a WebRTC peer connection to the Python server.
+4. **Replace `constella-demo` default route.** `constella-demo` points at the new realtime page; `constella-demo --legacy` keeps the current Gradio app.
+5. **Smoke-test server boot + text-input path.** No mic needed — verify the pipeline processes a typed patient utterance end-to-end and produces audio out.
+6. **User validates the live voice loop.** You speak, listen, report. I iterate on VAD thresholds, TTS voice selection, etc.
+7. **(Later) Option C latency optimization.** Gate TTS on medication + escalation specialists, not the full merge.
+
+## 11. What stays the same
+
+- `constella/orchestrator.py`, `constella/primary.py`, `constella/specialists/*.py`, `constella/schemas.py` — zero changes.
+- `constella/eval/` — zero changes, still batch-synchronous.
+- `tests/test_schemas.py` — zero changes.
+
+This is the point: the whole constellation is an I/O-agnostic function. The realtime work is purely a transport + streaming layer around it.
+
+## 12. Open questions for review
+
+1. **Option A vs C for MVP?** I recommend A. Confirms?
+2. **Spanglish ASR fallback to Groq Whisper on EOT — acceptable 200 ms overhead?**
+3. **Keep the Gradio demo parallel to the new one, or retire it?** I recommend keeping it as `constella-demo --legacy` for dev testing.
+4. **Deepgram vs Gladia for ASR?** Deepgram for MVP (larger ecosystem, better docs); revisit if Spanglish accuracy is a real problem.
diff --git a/pyproject.toml b/pyproject.toml
index 3dbeb5c..b0dba04 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,12 +16,16 @@ dependencies = [
     "gtts>=2.5",
     "python-dotenv>=1.0",
     "pytest>=8.0",
+    "fastrtc[vad]>=0.0.20",
+    "cartesia[websockets]>=2.0",
+    "numpy>=1.26",
 ]
 
 [project.scripts]
 constella-chat = "constella.cli:main"
 constella-eval = "constella.eval.run_eval:main"
 constella-demo = "constella.demo.app:main"
+constella-realtime = "constella.demo.realtime:main"
 
 [build-system]
 requires = ["hatchling"]
diff --git a/uv.lock b/uv.lock
index f7beac0..421a37f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,12 @@ version = 1
 revision = 3
 requires-python = ">=3.11, <3.13"
 resolution-markers = [
-    "sys_platform == 'win32'",
-    "sys_platform == 'emscripten'",
-    "sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version < '3.12' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and sys_platform == 'emscripten'",
+    "python_full_version < '3.12' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
 [[package]]
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 ]
 
+[[package]]
+name = "aioice"
+version = "0.10.2"
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+]
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+version = "1.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aioice" },
+    { name = "av" },
+    { name = "cryptography" },
+    { name = "google-crc32c" },
+    { name = "pyee" },
+    { name = "pylibsrtp" },
+    { name = "pyopenssl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/51/9c/4e027bfe0195de0442da301e2389329496745d40ae44d2d7c4571c4290ce/aiortc-1.14.0.tar.gz", hash = "sha256:adc8a67ace10a085721e588e06a00358ed8eaf5f6b62f0a95358ff45628dd762", size = 1180864, upload-time = "2025-10-13T21:40:37.905Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/57/ab/31646a49209568cde3b97eeade0d28bb78b400e6645c56422c101df68932/aiortc-1.14.0-py3-none-any.whl", hash = "sha256:4b244d7e482f4e1f67e685b3468269628eca1ec91fa5b329ab517738cfca086e", size = 93183, upload-time = "2025-10-13T21:40:36.59Z" },
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 [[package]]
 name = "annotated-doc"
 version = "0.0.4"
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