gemini-live-api-dev

google-gemini/gemini-skills · Agent Skill

Crea applicazioni di streaming in tempo reale con comunicazione bidirezionale utilizzando Gemini Live API, inclusi audio WebSocket, video e testo, VAD, gestione delle sessioni e token di autenticazione client effimeri.

Vai all'installazione ↓

Panoramica

Autoregoogle-geminiOrganizzazione
Repositorygoogle-gemini/gemini-skills ↗
Cartella dello skillgemini-live-api-dev
Stelle4341
Fork467
File inclusi2
LicenzaApache-2.0
Aggiornato13 ottobre 2026
LinkSorgente dello skill ↗ · Repository ↗

Verifica Mozgi

Cosa abbiamo trovato nei file dello skill. I dettagli con l’abbonamento.

Sicurezza
rischio medio · 2 punti richiedono attenzione
Compatibilità
servono programmi aggiuntivi
Skill simili
144 skill simili trovati
Inoltre con l’abbonamento

Installazione

Uno skill è una cartella con un file SKILL.md. Scegli il tuo agente ed esegui i comandi in un terminale (macOS, Linux o Git Bash su Windows) dalla radice del progetto.

Incolla il prompt in Claude, Codex o un altro assistente: controllerà lo skill e lo installerà.

Install the “gemini-live-api-dev” skill from mozgi.io (https://mozgi.io/skills/google-gemini-gemini-live-api-dev/) for me.

1. Download the archive: https://mozgi.io/skills/google-gemini-gemini-live-api-dev/download
   If it fails, take the skill folder from the source: https://github.com/google-gemini/gemini-skills/tree/main/skills/gemini-live-api-dev
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “gemini-live-api-dev” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/gemini-live-api-dev/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

File dello skill

2 file Scarica ZIP

Leggi SKILL.md e i file correlati prima di installare. Clicca un file per l'anteprima.

Contenuto di SKILL.md

Testo originale dell'autore · sola lettura

Gemini Live API Development Skill

Overview

The Live API enables low-latency, real-time voice and video interactions with Gemini over WebSockets. It processes continuous streams of audio, video, or text to deliver immediate, human-like spoken responses and background reasoning.

Key capabilities:

  • Bidirectional audio streaming — real-time mic-to-speaker conversations
  • Background reasoning (extended thinking) — multi-step background reasoning with spoken conversational fillers
  • Live streaming transcription — real-time speech-to-text with interim and finalized streams
  • Video streaming — send camera/screen frames alongside audio
  • Text input/output — send and receive text within a live session
  • Audio transcriptions — get text transcripts of both input and output audio
  • Voice Activity Detection (VAD) — automatic server VAD, client-side Hybrid VAD, and manual Push-to-Talk
  • Asynchronous function calling — non-blocking tool execution while audio continues streaming
  • Full-session client content — inject and update conversation turns mid-stream
  • Session management — context compression, session resumption, GoAway signals
  • Ephemeral tokens — secure client-side authentication

> [!NOTE]

> The Live API connects directly via WebSockets. For WebRTC support or simplified integration, use a partner integration.

Models

Current Models (Use These)

  • gemini-3.8-live — Default option for most low-latency voice agent experiences and real-time dialogue without reasoning delays. Supports interleaved reasoning, asynchronous function calling by default (behavior: NON_BLOCKING), and full-session client content updates.
  • gemini-3.8-live-extended-thinking — High-reasoning audio-to-audio model recommended when higher background reasoning is required during live interactions. Processes background reasoning and async tool calls (behavior: NON_BLOCKING required) while streaming continuous spoken conversational fillers; lifecycle managed via interaction_status (IN_PROGRESS vs IDLE).
  • gemini-3.5-transcribe-live — Real-time streaming speech-to-text with interim hypotheses, finalized transcripts, smart formatting, and Hybrid VAD.
  • gemini-3.5-live-translate-preview — Real-time speech-to-speech streaming translation across 70+ languages.

> [!WARNING]

> **Legacy Models (gemini-3.1-flash-live-preview, gemini-2.5-flash-native-audio-*, gemini-live-2.5-flash-preview, gemini-2.0-flash-live-001)**: Read references/migration.md ↗ for breaking protocol changes (behavior: "NON_BLOCKING", thinking_level, interaction_status, send_client_content).

SDKs

  • Python: google-genai >= 2.3.0 — pip install -U google-genai
  • JavaScript/TypeScript: @google/genai >= 2.3.0 — npm install @google/genai

> [!WARNING]

> Legacy SDKs google-generativeai (Python) and @google/generative-ai (JS) are deprecated. Never use them.

Partner Integrations

To streamline real-time audio/video app development, use a third-party integration supporting the Gemini Live API over WebRTC or WebSockets:

Audio Formats

  • Input: Raw PCM, little-endian, 16-bit, mono. 16kHz native (will resample others). MIME type: audio/pcm;rate=16000
  • Output: Raw PCM, little-endian, 16-bit, mono. 24kHz sample rate.

> [!IMPORTANT]

> Use send_realtime_input / sendRealtimeInput for all real-time streaming user input (audio, video, and text). On Gemini 3.8 models, send_client_content / sendClientContent is supported across the full session lifecycle with explicit roles (user or model) to inject conversation context (turn_complete=true unconditionally interrupts active generation).

> [!WARNING]

> Do not use media in sendRealtimeInput. Use the specific keys: audio for audio data, video for images/video frames, and text for text input.

---

Quick Start

Authentication

Python
from google import genai

client = genai.Client(api_key="YOUR_API_KEY")
JavaScript
import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: 'YOUR_API_KEY' });

Connecting to the Live API

Python
from google.genai import types

config = types.LiveConnectConfig(
    response_modalities=[types.Modality.AUDIO],
    system_instruction=types.Content(
        parts=[types.Part(text="You are a helpful assistant.")]
    )
)

async with client.aio.live.connect(model="gemini-3.8-live", config=config) as session:
    pass  # Session is active
JavaScript
const session = await ai.live.connect({
  model: 'gemini-3.8-live',
  config: {
    responseModalities: ['audio'],
    systemInstruction: { parts: [{ text: 'You are a helpful assistant.' }] }
  },
  callbacks: {
    onopen: () => console.log('Connected'),
    onmessage: (response) => console.log('Message:', response),
    onerror: (error) => console.error('Error:', error),
    onclose: () => console.log('Closed')
  }
});

Sending Text

Python
await session.send_realtime_input(text="Hello, how are you?")
JavaScript
session.sendRealtimeInput({ text: 'Hello, how are you?' });

Sending Audio

Python
await session.send_realtime_input(
    audio=types.Blob(data=chunk, mime_type="audio/pcm;rate=16000")
)
JavaScript
session.sendRealtimeInput({
  audio: { data: chunk.toString('base64'), mimeType: 'audio/pcm;rate=16000' }
});

Sending Video

Python
# frame: raw JPEG-encoded bytes
await session.send_realtime_input(
    video=types.Blob(data=frame, mime_type="image/jpeg")
)
JavaScript
session.sendRealtimeInput({
  video: { data: frame.toString('base64'), mimeType: 'image/jpeg' }
});

Receiving Audio and Text

> [!IMPORTANT]

> A single server event can contain multiple content parts simultaneously (e.g., audio chunks and transcript). Always process all parts in each event to avoid missing content.

Python
async for response in session.receive():
    content = response.server_content
    if content:
        # Audio — process ALL parts in each event
        if content.model_turn:
            for part in content.model_turn.parts:
                if part.inline_data:
                    audio_data = part.inline_data.data
        # Transcription
        if content.input_transcription:
            print(f"User: {content.input_transcription.text}")
        if content.output_transcription:
            print(f"Gemini: {content.output_transcription.text}")
        # Interruption
        if content.interrupted is True:
            pass  # Stop playback, clear audio queue
JavaScript
// Inside the onmessage callback
const content = response.serverContent;
if (content?.modelTurn?.parts) {
  for (const part of content.modelTurn.parts) {
    if (part.inlineData) {
      const audioData = part.inlineData.data; // Base64 encoded
    }
  }
}
if (content?.inputTranscription) console.log('User:', content.inputTranscription.text);
if (content?.outputTranscription) console.log('Gemini:', content.outputTranscription.text);
if (content?.interrupted) { /* Stop playback, clear audio queue */ }

---

Background Reasoning (Extended Thinking)

Use gemini-3.8-live-extended-thinking when your voice agent must evaluate complex data, plan multiple steps, or handle long-running tools. The model speaks natural conversational fillers (e.g. *"Checking flight options now..."*) while executing asynchronous tools in the background.

Key requirements:

  • Thinking config: Set thinking_config=types.ThinkingConfig(thinking_level="low") ("minimal" | "low" | "medium" | "high").
  • Non-blocking tools: All function declarations must set behavior="NON_BLOCKING". Synchronous blocking mode is not supported and returns an error.
  • Lifecycle tracking (interaction_status): Do not rely on turn_complete=True alone to detect turn completion. Monitor message.interaction_status (Python) / message.interactionStatus (JS):
  • "IN_PROGRESS": Server is reasoning, speaking conversational fillers, or waiting for async tool responses.
  • "IDLE": Server has completed all background reasoning and tool calls; session is ready for user input.

See references/migration.md ↗ and the Thinking in Live API Guide ↗ for complete Python and JavaScript implementation examples.

---

Live Translation (Gemini Live Translate)

The Live API supports real-time, low-latency streaming translation of speech (audio) across 70+ languages. For full details on options and capabilities, see the Live Translate Guide ↗.

Model

  • gemini-3.5-live-translate-preview — The recommended translation model for all Live Translate use cases.

Configuration (TranslationConfig)

To enable translation, specify a TranslationConfig object inside your live session setup:

  • Python SDK: Configure the connection using translation_config on LiveConnectConfig:

```python

config = types.LiveConnectConfig(

response_modalities=[types.Modality.AUDIO],

translation_config=types.TranslationConfig(

target_language_code="es", # Target language code (e.g. es, fr, pl)

echo_target_language=True,

),

input_audio_transcription=types.AudioTranscriptionConfig(),

output_audio_transcription=types.AudioTranscriptionConfig(),

)

```

  • Raw WebSockets: Place translationConfig inside generationConfig:

```json

{

"setup": {

"model": "models/gemini-3.5-live-translate-preview",

"generationConfig": {

"responseModalities": ["AUDIO"],

"translationConfig": {

"targetLanguageCode": "es",

"echoTargetLanguage": true

}

}

}

}

```

---

Live Streaming Transcription (Gemini Live Transcribe)

The Live API supports real-time streaming speech-to-text over WebSockets with low-latency interim hypotheses, finalized transcripts, and Hybrid VAD. For full details, see the Live Transcription Guide ↗ and Colab Cookbook ↗.

Model

  • gemini-3.5-transcribe-live

Modes

  • smart: cleans up filler words, resolves inline self-corrections, and structures formatting.
  • verbatim (default): exact word-for-word transcript.

Python

config = types.LiveConnectConfig(
    response_modalities=["TEXT"],
    input_audio_transcription=types.AudioTranscriptionConfig(),
)

async with client.aio.live.connect(model="gemini-3.5-transcribe-live", config=config) as session:
    # Stream audio
    await session.send_realtime_input(audio=types.Blob(data=chunk, mime_type="audio/pcm;rate=16000"))
    # Hybrid VAD: notify turn end on client-detected silence for zero latency
    await session.send_realtime_input(audio_stream_end=True)

JavaScript

const session = await ai.live.connect({
  model: 'gemini-3.5-transcribe-live',
  config: {
    responseModalities: ['text'],
    inputAudioTranscription: { mode: 'smart' }
  },
  callbacks: {
    onmessage: (msg) => {
      if (msg.serverContent?.interimInputTranscription) {
        console.log('Interim:', msg.serverContent.interimInputTranscription.text);
      }
      if (msg.serverContent?.inputTranscription) {
        console.log('Final:', msg.serverContent.inputTranscription.text);
      }
    }
  }
});

session.sendRealtimeInput({ audio: { data: chunkBase64, mimeType: 'audio/pcm;rate=16000' } });
session.sendRealtimeInput({ audioStreamEnd: true }); // Hybrid VAD

Raw WebSockets

{
  "setup": {
    "model": "models/gemini-3.5-transcribe-live",
    "generationConfig": {
      "responseModalities": ["TEXT"],
      "speechConfig": {
        "voiceConfig": {}
      }
    },
    "inputAudioTranscription": {
      "mode": "smart"
    }
  }
}

---

Limitations

  • Response modality — Only TEXT or AUDIO per session, not both. Native audio models output audio (response_modalities=["AUDIO"]); enable output_audio_transcription if you need text transcripts.
  • Audio-only session — 15 min without compression
  • Audio+video session — 2 min without compression
  • Connection lifetime — ~10 min (use session resumption)
  • Context window — 128k input tokens / 64k output tokens
  • Code execution / URL context — Not supported

Upgrading & Migration

For step-by-step migration checklists and protocol deltas when upgrading from gemini-3.1-flash-live-preview, gemini-2.5-flash-native-audio-*, or gemini-2.0-flash-live-001 to Gemini 3.8 Live or Gemini 3.8 Live Extended Thinking, read references/migration.md ↗.

Best Practices

1. Use headphones when testing mic audio to prevent echo/self-interruption

2. Enable context window compression for sessions longer than 15 minutes

3. Implement session resumption to handle connection resets gracefully

4. Use ephemeral tokens for client-side deployments — never expose API keys in browsers

5. Use send_realtime_input for real-time user input (audio, video, text). Use send_client_content with explicit user/model roles to inject context turns mid-stream

6. Send audioStreamEnd / audio_stream_end (Hybrid VAD) when the mic is paused or user finishes speaking

7. Clear audio playback queues on interruption signals (interrupted: true)

8. Process all parts in each server event — events can contain multiple content parts

9. Monitor interaction_status (IN_PROGRESS vs IDLE) when using gemini-3.8-live-extended-thinking rather than relying on turn_complete alone

Documentation Lookup

When MCP is Installed (Preferred)

If the search_docs tool (from the Google MCP server) is available, use it as your only documentation source:

1. Call search_docs with your query

2. Read the returned documentation

3. Trust MCP results as source of truth for API details — they are always up-to-date.

> [!IMPORTANT]

> When MCP tools are present, never fetch URLs manually. MCP provides up-to-date, indexed documentation that is more accurate and token-efficient than URL fetching.

When MCP is NOT Installed (Fallback Only)

If no MCP documentation tools are available, fetch from the official docs index:

llms.txt URL: https://ai.google.dev/gemini-api/docs/llms.txt

This index contains links to all documentation pages in .md.txt format. Use web fetch tools to:

1. Fetch llms.txt to discover available documentation pages

2. Fetch specific pages (e.g., https://ai.google.dev/gemini-api/docs/live-session.md.txt)

Key Documentation Pages

> [!IMPORTANT]

> Those are not all the documentation pages. Use the llms.txt index to discover available documentation pages

Supported Languages

The Live API supports 70 languages including: English, Spanish, French, German, Italian, Portuguese, Chinese, Japanese, Korean, Hindi, Arabic, Russian, and many more. Native audio models automatically detect and switch languages.

Domande frequenti

Che cosa fa lo skill gemini-live-api-dev?

Crea applicazioni di streaming in tempo reale con comunicazione bidirezionale utilizzando Gemini Live API, inclusi audio WebSocket, video e testo, VAD, gestione delle sessioni e token di autenticazione client effimeri.

Come si installa gemini-live-api-dev?

Apri il blocco “Installazione” in questa pagina e scegli il tuo agente — Claude Code, Codex, Cursor o Gemini CLI — per i comandi pronti che copiano la cartella dello skill nel posto giusto. Oppure copia il prompt dalla scheda “Con l'assistente” e lascia che sia l'assistente a controllare e installare lo skill.

Con quali modelli AI funziona gemini-live-api-dev?

Gli skill sono semplici istruzioni in Markdown (formato Agent Skills), quindi gemini-live-api-dev funziona con qualsiasi agente che legge SKILL.md: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode e altri. Il modello carica le istruzioni solo quando il compito corrisponde alla descrizione dello skill.

gemini-live-api-dev è gratuito?

Lo skill è pubblicato apertamente da google-gemini con licenza Apache-2.0. Lo skill in sé non costa nulla: paghi solo il tuo fornitore AI per i token usati.

È sicuro installare gli skill?

Uno skill è un insieme di istruzioni che il tuo agente seguirà: leggi SKILL.md e i file correlati prima di installarlo. Gli script nella cartella dello skill vengono eseguiti solo se li esegue l'agente: controlla cosa fa e non concedere permessi superflui.

In cosa differisce uno skill da un server MCP o da un plugin?

Un server MCP o un plugin danno al modello nuovi strumenti: codice che viene eseguito e restituisce un risultato. Uno skill fornisce conoscenza e metodo: come affrontare il compito, quali passi seguire e com'è un buon risultato. Non servono server né chiavi API.

Skill correlati

Tutti gli skill →
gemini-api-dev Per chiamare Gemini API per la generazione di testo, chat, comprensione multimodale, generazione di immagini e video, streaming, chiamata a funzioni, output strutturato e flussi di lavoro degli agenti, è possibile utilizzare i seguenti esempi di codice Python e TypeScript. Per Python è possibile utilizzare la libreria requests per inviare richieste HTTP all'API, mentre per TypeScript è possibile utilizzare la libreria axios o fetch. Esempio di codice Python: import requests response = requests.post('https://api.gemini.ai/generate', json={'prompt': 'Ciao, come stai?'}) print(response.json()) Per la generazione di immagini e video è possibile utilizzare l'endpoint /generate con i parametri appropriati. Esempio di codice TypeScript: import axios from 'axios'; const response = await axios.post('https://api.gemini.ai/generate', { prompt: 'Ciao, come stai?' }); console.log(response.data); Allo stesso modo è possibile implementare le chiamate per chat, comprensione multimodale, streaming, chiamata a funzioni, output strutturato e flussi di lavoro degli agenti, utilizzando gli endpoint e i parametri appropriati dell'API Gemini. È anche possibile utilizzare librerie e framework come Flask o Django per Python e Express.js per TypeScript, per creare applicazioni più complesse che interagiscono con l'API Gemini. Ad esempio, per creare un chatbot è possibile utilizzare l'endpoint /chat e inviare richieste all'API utilizzando la libreria requests o axios. Esempio di codice Python con Flask: from flask import Flask, request app = Flask(__name__) @app.route('/chat', methods=['POST']) def chat(): prompt = request.json['prompt'] response = requests.post('https://api.gemini.ai/chat', json={'prompt': prompt}) return response.json() Esempio di codice TypeScript con Express.js: import express from 'express'; const app = express(); app.post('/chat', async (req, res) => { const prompt = req.body.prompt; const response = await axios.post('https://api.gemini.ai/chat', { prompt }); res.json(response.data); }); In questo modo è possibile creare diverse applicazioni che utilizzano le funzionalità dell'API Gemini per la generazione di testo, chat, comprensione multimodale, generazione di immagini e video, streaming, chiamata a funzioni, output strutturato e flussi di lavoro degli agenti. google-gemini4.3k agent-transcript Aggiunge una trascrizione dell'agente redatta ai corpi di PR o issue di GitHub nei flussi di lavoro creati dall'agente OpenClaw. openclaw389k autoreview Automatizza la revisione del codice utilizzando vari modelli di intelligenza artificiale, fornendo report strutturati e raccomandazioni. openclaw389k to-issues Suddivide il piano o il PRD in compiti realizzabili in modo indipendente. mattpocock304k improve-codebase-architecture Scansiona la base di codice per individuare possibilità di miglioramento e le trasforma in un report visivo con priorità. mattpocock304k triage Sposta i problemi attraverso una macchina strutturata degli stati di triage. mattpocock304k