tibet-voice-cache 0.1.1

Separated user/AI conversation cache for streaming voice models. Gives your voice AI memory without role confusion.

tibet-voice-cache

Conversation memory for streaming voice AI. Give your voice model persistent context without role confusion.

pip install tibet-voice-cache

What it does

Streaming voice models (Gemini Live, OpenAI Realtime) lose context between sessions. When you inject raw conversation history as user/model turns, the model replays old responses, talks to itself, or confuses who said what.

tibet-voice-cache stores user and AI utterances separately and builds clean context summaries for your system instruction — no fake turns, no role confusion.

It is provider- and transport-agnostic: it works on plain transcripts (add_user / add_ai), so any audio source plugs in through a small adapter. A Gemini Live adapter ships today; OpenAI Realtime, a SIP/WebRTC bridge, or stored-audio transcripts all follow the same adapters/base pattern.

client / device ──audio──► your proxy ──audio──► streaming voice model
                       │                       │
                  input_transcript         output_transcript
                       │                       │
                       ▼                       ▼
                  user_said[]              ai_said[]
                       │                       │
                       └──── system instruction injection ────┘
                                    │
                           "Earlier discussed:
                            User asked about X
                            You answered Y"

Quick start

from tibet_voice_cache import VoiceCache

# Create a cache (in-memory or persistent)
cache = VoiceCache(actor="user_123", storage_dir="./cache")

# During your voice session, record what's said
cache.add_user("What's the weather like?")
cache.add_ai("It's sunny and 22 degrees!")
cache.complete_turn()

# Next session: inject context into system instruction
system_prompt = cache.inject_into_system_instruction(
    "You are a helpful voice assistant."
)
# Result:
# "You are a helpful voice assistant.
#
#  === PRIOR CONTEXT ===
#  The user previously said:
#    - What's the weather like?
#  You previously responded:
#    - It's sunny and 22 degrees!
#  === END CONTEXT ===
#  Do NOT respond to the above context unless the user refers to it."

Gemini Flash Live adapter

Drop-in integration for Google's Gemini Live API:

from tibet_voice_cache import VoiceCache
from tibet_voice_cache.adapters.gemini_live import GeminiLiveAdapter
from google import genai

cache = VoiceCache(actor="user_123", storage_dir="./cache")
adapter = GeminiLiveAdapter(cache)

# Build config with context already injected
config = adapter.build_config(
    base_instruction="You are OomLlama, a friendly Dutch AI assistant.",
    voice="Kore",
)

client = genai.Client(api_key=API_KEY)
async with client.aio.live.connect(model="gemini-3.1-flash-live-preview", config=config) as session:
    # In your relay loop, hook into transcription events:
    async for msg in session.receive():
        if msg.server_content:
            if msg.server_content.input_transcription:
                adapter.on_input_transcript(msg.server_content.input_transcription.text)
            if msg.server_content.output_transcription:
                adapter.on_output_transcript(msg.server_content.output_transcription.text)
            if msg.server_content.turn_complete:
                adapter.on_turn_complete()

    # Session ends — persist cache for next time
    adapter.on_session_end()

Summary styles

Choose how context is formatted for your model:

from tibet_voice_cache import VoiceCache, SummaryStyle

# Labeled (default) — clear sections
cache = VoiceCache(actor="user", summary_style=SummaryStyle.LABELED)

# Compact — minimal tokens
cache = VoiceCache(actor="user", summary_style=SummaryStyle.COMPACT)
# → [Context] User: weather question. You: sunny, 22 degrees.

# Narrative — natural language
cache = VoiceCache(actor="user", summary_style=SummaryStyle.NARRATIVE)
# → Earlier in the conversation:
#   - The user said: "What's the weather like?"
#   - You replied: "It's sunny and 22 degrees!"

# Chronological — ordered pairs
cache = VoiceCache(actor="user", summary_style=SummaryStyle.CHRONOLOGICAL)
# → Previously discussed:
#     1. User: What's the weather? -> You: Sunny, 22 degrees

Multi-language labels

Built-in English and Dutch, or bring your own:

# Dutch labels
cache = VoiceCache(actor="user", summary_style=SummaryStyle.LABELED)
cache.summary_builder.labels = SummaryBuilder.LABELS["nl"]

# Custom labels
cache.summary_builder.labels = {
    "header": "--- KONTEXT ---",
    "footer": "--- ENDE ---",
    "user_label": "Benutzer sagte:",
    "ai_label": "Du antwortetest:",
    "no_replay": "Reagiere nicht auf obigen Kontext.",
    ...
}

Bulk session import

Save all transcripts at once when a session ends:

cache.add_session_transcripts(
    user_texts=["Hello", "How are you?", "Tell me a joke"],
    ai_texts=["Hi there!", "I'm great!", "Why did the chicken..."],
)

Migrate from mixed history

Coming from a [{"role": "user", "content": "..."}, {"role": "assistant", ...}] format?

legacy_history = [
    {"role": "user", "content": "Hello"},
    {"role": "assistant", "content": "Hi!"},
]
cache = VoiceCache.from_mixed_history(legacy_history, actor="migrated")

Persistence

Cache is stored as JSON per actor. Works with any filesystem:

# Local storage
cache = VoiceCache(actor="user_123", storage_dir="./cache")

# Shared storage (NFS, S3 mount, etc.)
cache = VoiceCache(actor="user_123", storage_dir="/mnt/shared/voice-cache")

# In-memory only (no persistence)
cache = VoiceCache(actor="user_123")

File format:

{
  "actor": "user_123",
  "user_said": [
    {"text": "Hello", "timestamp": 1712234567.89, "turn_id": 0}
  ],
  "ai_said": [
    {"text": "Hi there!", "timestamp": 1712234568.12, "turn_id": 0}
  ],
  "turn_counter": 1,
  "updated": "2026-04-04T10:30:00+00:00"
}

Why not Google's Context Cache API?

Google's context caching is designed for REST API calls — cache large documents, save tokens. For the Live API (streaming audio), their approach is send_client_content with raw turns — which causes the exact role confusion this package solves.

Google's own docs recommend: "For longer contexts, provide a single message summary." That's exactly what tibet-voice-cache does, automatically.

Part of the TIBET ecosystem

tibet-voice-cache is part of TIBET — Traceable Intent-Based Event Tokens. Built by the HumoticaOS family.

Package	Description
tibet-voice-cache	This package — voice conversation memory
tibet-voice-cache-mcp	MCP server wrapper (coming soon)

Roadmap

The memory layer — separated user/AI utterances, clean context injection — is what ships today, and it runs in production. Next, all on the same adapters/base pattern (the cache stays provider-agnostic):

• SIP / WebRTC bridge — feed live telephony or browser-voice transcripts straight into the cache, role-confusion-free.
• OpenAI Realtime adapter — a sibling to the Gemini Live one.
• Voice-driven actions — let an utterance trigger a governed action, not just remembered context, enforced through the TIBET/JIS layer so the action carries its intent and audit trail.

These are deliberate next steps, not shipped. The cache is honest about what it does today.

License

MIT — use it, fork it, give your voice AI a memory.

Credits

Designed by Jasper van de Meent. Built by Jasper and Root AI as part of HumoticaOS.

---

Stack-positie: Groep specialized · Eigen tijdlijn — niet onderdeel van de ainternet/evidence-spine · ← tibet-phantom · tibet-voice-cache-mcp → · See STACK.md · See demo/golden-path/ for the spine end-to-end.

Enterprise

For private hub hosting, SLA support, custom integrations, or compliance guidance:

Enterprise	[e-mail]
Support	[e-mail]
Security	[e-mail]

See ENTERPRISE.md for details.