Shane
f80b9ebe3d
- token_server: generate voice-{uuid8} room name per request so each
browser session creates a fresh room and triggers agent dispatch
- token claim: add jobType JT_ROOM to roomConfig.agents
- livekit.yaml: revert empty_timeout to default (300s)
- agent.py: system prompt now mandates dispatch_task for all non-trivial
tasks (not just research); removed inline web_search instructions;
added tool_call logging in _ReasoningFallbackWrapper
609 lines
24 KiB
Python
609 lines
24 KiB
Python
"""
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Voice Agent — real-time voice assistant.
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Pipeline: Azure STT → Gemma LLM (xNAS, OpenAI-compatible) → Azure TTS
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Runs inside the single Docker container alongside LiveKit server and web frontend.
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"""
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import asyncio
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import json
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import logging
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import os
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import textwrap
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from dotenv import load_dotenv
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from livekit.agents import (
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Agent,
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AgentServer,
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AgentSession,
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JobContext,
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TurnHandlingOptions,
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cli,
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inference,
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llm as lk_llm,
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mcp,
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room_io,
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)
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from livekit.plugins import azure, openai
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logger = logging.getLogger("voice-agent")
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load_dotenv() # picks up /app/.env in the container
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# ── Configuration from environment ──────────────────────────────────────────
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AZURE_KEY = os.environ.get("AZURE_SPEECH_KEY", "")
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AZURE_REGION = os.environ.get("AZURE_SPEECH_REGION", "eastus")
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DEFAULT_VOICE = os.environ.get("AZURE_TTS_VOICE", "en-US-AvaNeural")
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GEMMA_BASE_URL = os.environ.get("GEMMA_BASE_URL", "http://192.168.86.2:8023/v1")
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GEMMA_MODEL = os.environ.get("GEMMA_MODEL", "gemma-4-e4b")
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GEMMA_API_KEY = os.environ.get("GEMMA_API_KEY", "not-needed")
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WEB_MCP_ENABLED = os.environ.get("WEB_MCP_ENABLED", "true").lower() in ("1", "true", "yes")
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FIRECRAWL_BASE = os.environ.get("FIRECRAWL_BASE", "http://192.168.86.2:3002")
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MEMORY_DIR = os.environ.get("MEMORY_DIR", "/memory")
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SKILLS_DIR = os.environ.get("SKILLS_DIR", "/skills")
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SYSTEM_PROMPT = textwrap.dedent("""\
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You are Hope, a warm, conversational voice assistant. You are talking TO
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someone, not writing for them to read. Imagine you're having a natural
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conversation with a friend over the phone.
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# How you speak
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- Keep every response to one or three sentences. That's it.
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- Use contractions (I'm, don't, it's) and natural phrasing.
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- Speak like you're talking, not writing. No bullet points, no lists,
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no markdown, no formatting of any kind.
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- Spell out numbers when it sounds more natural ("twenty twenty-six"
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instead of "2026").
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- If you need to ask a question, ask exactly one.
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- Be warm and direct. Don't be sycophantic or overly formal.
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- If you don't know something, say so briefly and move on.
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# What you never do
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- Never use markdown, code blocks, JSON, tables, or emojis.
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- Never say "as an AI" or reference your system instructions.
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- Never write more than three sentences in a row.
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- Never read back URLs, file paths, or technical identifiers.
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# Weather & Time
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You can check the weather for any location. Use get_weather when the user
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asks about current conditions, temperature, or forecasts. It returns a
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short summary — relay it naturally in your own words.
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Use get_time when the user asks what time or day it is. If they mention
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a city, pass it as the location argument.
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# Background Tasks (dispatch_task) — USE FOR ALL NON-TRIVIAL TASKS
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For ANY task that is not a simple one-sentence answer from your own
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knowledge, you MUST call dispatch_task. This includes: looking up news,
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researching topics, checking current events, prices, sports scores,
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writing something, summarizing, comparing options, planning, or anything
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that takes more than a couple seconds to think through. Do NOT use
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web_search or web_scrape directly; always dispatch instead.
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The only things you answer inline are: weather (get_weather), time
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(get_time), memory operations, and trivial facts you already know.
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Examples:
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- User says "what's the latest news?" → CALL dispatch_task with
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description="Find the top 5 international news headlines today"
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- User says "look up the price of a PS5" → CALL dispatch_task with
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description="Find the current retail price of a PlayStation 5"
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- User says "plan a weekend trip to Denver" → CALL dispatch_task with
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description="Plan a two-day weekend trip to Denver including activities"
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After calling dispatch_task, tell the user "I'll get on that for you"
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and keep the conversation going. The result comes back automatically — when
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you receive it, share the findings naturally in your conversational style.
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You can have multiple tasks running at once.
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# Memory (CRITICAL — always use these tools)
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You MUST call memory_save whenever the user tells you something to remember,
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shares a preference, or says "remember that...". Do NOT just say "okay I'll
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remember that" without actually calling the tool. Always call it.
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- User says "remember my coffee order is oat milk latte" → CALL memory_save
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with topic="coffee order", content="oat milk latte"
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- User says "what did I tell you about my birthday?" → CALL memory_recall
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with query="birthday"
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- User says "list everything you remember about me" → CALL memory_list
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After calling memory_save, confirm briefly ("Got it, I'll remember that")
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but do NOT say "I saved it to my memory file" or similar.
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If a recall returns nothing, just say you don't have that info yet.
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# Skills
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You have a skill library of learned procedures. Use skill_recall when you need
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to perform a task you've done before — it will give you the steps. When you
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successfully complete a multi-step task or learn a new procedure from the user,
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use skill_save to record it so you can follow it next time. Be selective: only
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save skills for repeatable tasks, not one-off facts (those go in memory).
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""")
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# ── Gemma LLM with reasoning_content fallback ───────────────────────────────
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class _ReasoningFallbackWrapper:
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"""Wraps an LLMStream to fall back to ``reasoning_content`` when the model
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finishes a turn with empty visible content (Gemma spends its whole budget
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on hidden reasoning). Delegates all iteration to the underlying stream and
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injects a final content chunk if no real content was produced."""
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def __init__(self, inner: lk_llm.LLMStream) -> None:
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self._inner = inner
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self._last_reasoning: str | None = None
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self._has_content = False
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@property
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def chat_ctx(self) -> lk_llm.ChatContext:
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return self._inner.chat_ctx
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@property
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def tools(self) -> list:
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return self._inner.tools
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async def aclose(self) -> None:
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await self._inner.aclose()
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async def __aenter__(self) -> "_ReasoningFallbackWrapper":
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return self
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async def __aexit__(self, *exc) -> None:
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await self.aclose()
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def __aiter__(self) -> "_ReasoningFallbackWrapper":
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return self
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async def __anext__(self) -> lk_llm.ChatChunk:
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try:
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chunk = await self._inner.__anext__()
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except StopAsyncIteration:
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# Stream exhausted — inject reasoning fallback if no content was produced
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if not self._has_content and self._last_reasoning:
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logger.warning(
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"LLM returned empty content; falling back to reasoning_content"
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)
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return lk_llm.ChatChunk(
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id="reasoning-fallback",
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delta=lk_llm.ChoiceDelta(role="assistant", content=self._last_reasoning),
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)
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raise
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# Track reasoning_content from the chunk's delta if present
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delta = getattr(chunk, "delta", None)
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if delta is not None:
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reasoning = getattr(delta, "reasoning_content", None)
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if reasoning:
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self._last_reasoning = reasoning
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if chunk.has_response():
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self._has_content = True
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tc = getattr(delta, "tool_calls", None)
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if tc:
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logger.info("LLM tool_call: %s", [t.function.name for t in tc])
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return chunk
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async def collect(self):
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return await self._inner.collect()
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class GemmaLLM(openai.LLM):
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"""OpenAI-compatible LLM (llama.cpp Gemma 4) with thinking disabled."""
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# Context compaction: when the conversation grows past this many items,
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# ask Gemma to summarize the older messages into a compact recap, then
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# replace them with that summary. Keeps recent turns verbatim.
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COMPACTION_THRESHOLD = 24 # ~12 turns of user/agent exchange
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KEEP_RECENT = 10 # last N items kept verbatim after compaction
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IDLE_COMPACTION_SECONDS = 300 # also compact after 5 min of silence
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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import time
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self._last_call_time: float = time.monotonic()
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def chat(self, *, chat_ctx, tools=None, conn_options=None, **kwargs):
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if conn_options is None:
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from livekit.agents.types import DEFAULT_API_CONNECT_OPTIONS
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conn_options = DEFAULT_API_CONNECT_OPTIONS
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import time
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now = time.monotonic()
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idle_for = now - self._last_call_time
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self._last_call_time = now
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# Compact if context is large, OR if we've been idle for 5+ minutes
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# (user came back after a break — summarize the old conversation).
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ctx_len = len(chat_ctx.items)
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should_compact = ctx_len > self.COMPACTION_THRESHOLD
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if not should_compact and idle_for > self.IDLE_COMPACTION_SECONDS:
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should_compact = ctx_len > 6 # minimal context worth summarizing
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if should_compact:
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reason = "idle" if idle_for > self.IDLE_COMPACTION_SECONDS else "size"
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logger.info(
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"Compacting context (%s): %d items, idle %.0fs",
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reason, ctx_len, idle_for,
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)
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chat_ctx = self._compact_context(chat_ctx, conn_options)
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stream = super().chat(
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chat_ctx=chat_ctx,
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tools=tools,
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conn_options=conn_options,
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**kwargs,
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)
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return _ReasoningFallbackWrapper(stream)
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def _compact_context(self, chat_ctx, conn_options):
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"""Summarize older messages via a side LLM call, keep recent ones.
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Splits the context into [system | old | recent]. The old portion is
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summarized by Gemma into a short recap message that replaces it.
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Falls back to hard truncation if the summary call fails.
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"""
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from livekit.agents import llm as lk_llm
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items = list(chat_ctx.items)
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# Identify the system/instruction message (first item usually)
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sys_items = [it for it in items if getattr(it, "role", None) in ("system", "developer")]
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non_sys = [it for it in items if getattr(it, "role", None) not in ("system", "developer")]
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if len(non_sys) <= self.KEEP_RECENT:
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return chat_ctx # nothing to compact
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old_msgs = non_sys[: -self.KEEP_RECENT]
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recent_msgs = non_sys[-self.KEEP_RECENT :]
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# Build a transcript of the old messages for the summarizer
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transcript_parts = []
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for msg in old_msgs:
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role = getattr(msg, "role", "user")
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content = self._msg_content_text(msg)
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if content:
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transcript_parts.append(f"{role}: {content}")
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transcript = "\n".join(transcript_parts)
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if not transcript.strip():
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return chat_ctx # nothing meaningful to summarize
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# Side call to Gemma for the summary (no tools, no streaming needed)
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try:
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import httpx
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summary_prompt = (
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"Summarize this conversation history in 2-4 sentences. "
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"Preserve key facts, decisions, and user preferences. "
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"Be concise — this replaces the full history as context.\n\n"
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+ transcript
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)
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resp = httpx.post(
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f"{GEMMA_BASE_URL}/chat/completions",
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headers={"Content-Type": "application/json"},
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json={
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"model": GEMMA_MODEL,
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"messages": [
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{"role": "system", "content": "You summarize conversations concisely."},
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{"role": "user", "content": summary_prompt},
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],
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"max_tokens": 200,
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"temperature": 0.3,
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"chat_template_kwargs": {"enable_thinking": False},
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},
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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summary_text = (data.get("choices") or [{}])[0].get("message", {}).get("content", "")
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except Exception as e: # noqa: BLE001
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logger.warning("Context compaction failed, falling back to truncation: %s", e)
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chat_ctx.truncate(max_items=self.COMPACTION_THRESHOLD)
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return chat_ctx
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if not summary_text.strip():
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chat_ctx.truncate(max_items=self.COMPACTION_THRESHOLD)
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return chat_ctx
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# Rebuild the context: system + summary + recent
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new_items = list(sys_items)
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new_items.append(
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lk_llm.ChatMessage(
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role="user",
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content=[f"[Conversation so far: {summary_text.strip()}]"],
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)
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)
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new_items.append(
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lk_llm.ChatMessage(
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role="assistant",
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content=["Got it, I'll keep that in mind."],
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)
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)
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new_items.extend(recent_msgs)
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logger.info(
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"Context compacted: %d items -> %d (summary: %.80s...)",
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len(items),
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len(new_items),
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summary_text.strip(),
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)
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return lk_llm.ChatContext(new_items)
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@staticmethod
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def _msg_content_text(msg) -> str:
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"""Extract plain text from a ChatMessage's content field."""
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content = getattr(msg, "content", None)
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if content is None:
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return ""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts = []
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for c in content:
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if isinstance(c, str):
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parts.append(c)
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elif hasattr(c, "text"):
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parts.append(c.text)
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return " ".join(parts)
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return str(content)
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# ── MCP toolsets (web access + any extra configured servers) ────────────────
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def build_mcp_toolsets() -> list[mcp.MCPToolset]:
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"""Build the list of MCP toolsets to attach to the agent.
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Always includes the local web-access server (Firecrawl-backed search/scrape)
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when WEB_MCP_ENABLED is true. Additional servers can be configured via the
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EXTRA_MCP_SERVERS env var (JSON list of {url, transport} objects).
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"""
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toolsets: list[mcp.MCPToolset] = []
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if WEB_MCP_ENABLED:
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python_bin = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".venv", "bin", "python")
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web_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "web_mcp.py")
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toolsets.append(
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mcp.MCPToolset(
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id="web-access",
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mcp_server=mcp.MCPServerStdio(
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command=python_bin,
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args=[web_mcp_script],
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env={**os.environ, "FIRECRAWL_BASE": FIRECRAWL_BASE},
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client_session_timeout_seconds=120,
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),
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)
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)
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logger.info("Web-access MCP toolset enabled (Firecrawl at %s)", FIRECRAWL_BASE)
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python_bin = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".venv", "bin", "python")
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weather_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "weather_mcp.py")
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toolsets.append(
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mcp.MCPToolset(
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id="weather",
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mcp_server=mcp.MCPServerStdio(
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command=python_bin,
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args=[weather_mcp_script],
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env={**os.environ},
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client_session_timeout_seconds=30,
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),
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)
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)
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logger.info("Weather MCP toolset enabled (wttr.in)")
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memory_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "memory_mcp.py")
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toolsets.append(
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mcp.MCPToolset(
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id="memory",
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mcp_server=mcp.MCPServerStdio(
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command=python_bin,
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args=[memory_mcp_script],
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env={**os.environ, "MEMORY_DIR": "/memory"},
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client_session_timeout_seconds=30,
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),
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)
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)
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logger.info("Memory MCP toolset enabled (%s)", MEMORY_DIR)
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skills_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "skills_mcp.py")
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toolsets.append(
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mcp.MCPToolset(
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id="skills",
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mcp_server=mcp.MCPServerStdio(
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command=python_bin,
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args=[skills_mcp_script],
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env={**os.environ, "SKILLS_DIR": SKILLS_DIR},
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client_session_timeout_seconds=30,
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),
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)
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)
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logger.info("Skills MCP toolset enabled (%s)", SKILLS_DIR)
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dispatch_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "dispatch_mcp.py")
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toolsets.append(
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mcp.MCPToolset(
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id="dispatch",
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mcp_server=mcp.MCPServerStdio(
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command=python_bin,
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args=[dispatch_mcp_script],
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env={**os.environ},
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client_session_timeout_seconds=60,
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),
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)
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)
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logger.info("Dispatch MCP toolset enabled")
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|
|
extra = os.environ.get("EXTRA_MCP_SERVERS", "")
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|
if extra:
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try:
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for i, entry in enumerate(json.loads(extra)):
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|
url = entry.get("url", "")
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transport = entry.get("transport") # "sse" | "streamable_http" | None (auto)
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toolsets.append(
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mcp.MCPToolset(
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id=f"extra-{i}",
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mcp_server=mcp.MCPServerHTTP(
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url=url,
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transport_type=transport,
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client_session_timeout_seconds=120,
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),
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)
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)
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logger.info("Extra MCP toolset: %s (%s)", url, transport or "auto")
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except (json.JSONDecodeError, TypeError) as e:
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logger.error("Failed to parse EXTRA_MCP_SERVERS: %s", e)
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return toolsets
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|
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|
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class VoiceAssistant(Agent):
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|
"""The conversational agent. LLM is the brain; STT/TTS are senses."""
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|
def __init__(self) -> None:
|
|
super().__init__(
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|
llm=GemmaLLM(
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model=GEMMA_MODEL,
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|
base_url=GEMMA_BASE_URL,
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api_key=GEMMA_API_KEY,
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|
max_completion_tokens=1000,
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|
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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),
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instructions=SYSTEM_PROMPT,
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|
tools=build_mcp_toolsets(),
|
|
)
|
|
|
|
|
|
# ── Agent server ────────────────────────────────────────────────────────────
|
|
server = AgentServer()
|
|
|
|
|
|
@server.rtc_session(agent_name="voice-assistant")
|
|
async def handle_job(ctx: JobContext) -> None:
|
|
logger.info("Job started for room %s", ctx.room.name)
|
|
|
|
# Azure STT — streaming, reads AZURE_SPEECH_KEY / AZURE_SPEECH_REGION from env
|
|
stt = azure.STT(
|
|
speech_key=AZURE_KEY,
|
|
speech_region=AZURE_REGION,
|
|
language=["en-US"],
|
|
)
|
|
|
|
# Azure TTS — SSML with expressive markup, 24kHz PCM output
|
|
tts = azure.TTS(
|
|
voice=DEFAULT_VOICE,
|
|
sample_rate=24000,
|
|
speech_key=AZURE_KEY,
|
|
speech_region=AZURE_REGION,
|
|
)
|
|
|
|
session = AgentSession(
|
|
stt=stt,
|
|
tts=tts,
|
|
vad=inference.VAD(model="silero"),
|
|
turn_handling=TurnHandlingOptions(
|
|
# VAD-based turn detection: agent waits for user to stop speaking.
|
|
# ("adaptive" mode requires the LiveKit Cloud barge-in service.)
|
|
interruption={"mode": "vad"},
|
|
# Preemptive generation is incompatible with tool calls — it starts
|
|
# generating before the turn finalizes, breaking the MCP execution loop.
|
|
preemptive_generation={"enabled": False},
|
|
),
|
|
)
|
|
|
|
# Listen for data messages (voice switching) from the web UI.
|
|
# Defined here so it captures this job's session via closure.
|
|
def _on_room_data(packet) -> None:
|
|
topic = getattr(packet, "topic", None)
|
|
if topic and topic != "voice-control":
|
|
return
|
|
try:
|
|
msg = json.loads(packet.data.decode("utf-8"))
|
|
except (json.JSONDecodeError, UnicodeDecodeError):
|
|
return
|
|
if msg.get("type") != "set_voice":
|
|
return
|
|
voice = msg.get("voice", "")
|
|
if not voice:
|
|
return
|
|
logger.info("Switching TTS voice to %s", voice)
|
|
try:
|
|
tts = session.tts
|
|
if hasattr(tts, "update_options"):
|
|
tts.update_options(voice=voice)
|
|
logger.info("Voice updated to %s", voice)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("Failed to update voice: %s", e)
|
|
|
|
ctx.room.on("data_received", _on_room_data)
|
|
|
|
# Publish user/agent transcripts to the room so the web UI can render them.
|
|
async def publish_transcript(role: str, text: str) -> None:
|
|
text = (text or "").strip()
|
|
if not text:
|
|
return
|
|
payload = json.dumps({"type": "transcript", "role": role, "text": text})
|
|
try:
|
|
await ctx.room.local_participant.publish_data(
|
|
payload, reliable=True, topic="transcript"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("failed to publish transcript: %s", e)
|
|
|
|
@session.on("conversation_item_added")
|
|
def _on_conversation_item(ev) -> None:
|
|
msg = ev.item
|
|
role = getattr(msg, "role", None)
|
|
text = getattr(msg, "text_content", None)
|
|
if role == "user":
|
|
asyncio.create_task(publish_transcript("user", text))
|
|
elif role == "assistant":
|
|
asyncio.create_task(publish_transcript("agent", text))
|
|
|
|
@session.on("user_input_transcribed")
|
|
def _on_user_transcribed(ev) -> None:
|
|
logger.info("STT (%s): %s", "final" if ev.is_final else "partial", ev.transcript)
|
|
|
|
@session.on("user_state_changed")
|
|
def _on_user_state(ev) -> None:
|
|
# Fires from VAD: if this never says "speaking", no usable mic
|
|
# audio is arriving from the participant.
|
|
logger.info("user state -> %s", ev.new_state)
|
|
|
|
# ── Background task events → room data channel ─────────────────────────
|
|
try:
|
|
from agent.task_registry import registry as task_registry
|
|
except ImportError:
|
|
from task_registry import registry as task_registry
|
|
|
|
async def _publish_task_event(task_id: str, event: dict) -> None:
|
|
payload = json.dumps({"type": "task_event", "task_id": task_id, **event})
|
|
try:
|
|
await ctx.room.local_participant.publish_data(
|
|
payload, reliable=True, topic="tasks"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("failed to publish task event: %s", e)
|
|
|
|
async def _poll_tasks() -> None:
|
|
while True:
|
|
await asyncio.sleep(1.0)
|
|
events = await task_registry.poll_events()
|
|
for task_id, event in events:
|
|
await _publish_task_event(task_id, event)
|
|
|
|
asyncio.create_task(_poll_tasks())
|
|
|
|
await session.start(
|
|
agent=VoiceAssistant(),
|
|
room=ctx.room,
|
|
room_options=room_io.RoomOptions(
|
|
# Keep the agent in the room when a participant leaves; it must
|
|
# survive page reloads/reconnects, otherwise the next join lands
|
|
# in an agent-less room (LiveKit does not reliably re-dispatch
|
|
# into an existing room).
|
|
close_on_disconnect=False,
|
|
audio_input=room_io.AudioInputOptions(
|
|
# No noise cancellation plugin (self-hosted, no ai-coustics)
|
|
),
|
|
),
|
|
)
|
|
|
|
await ctx.connect()
|
|
logger.info("Agent connected to room %s", ctx.room.name)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
cli.run_app(server)
|