google__adk-docs/docs/live/models.md

Supported models for live agents

Supported in ADKPython v0.1.0

Live agents require a model that can hold a bidirectional connection; a standard Gemini model will not. For the models ADK supports outside live agents, and for non-Gemini providers, see Models for agents.

Live models

Live agents run on models that take audio in and produce audio out, end to end, with no intermediate text-to-speech stage. That is what gives them human-like speech with natural prosody, and it is what a standard Gemini model cannot do over a bidirectional connection.

The same model has a different ID on each backend:

Model	AI Studio	Agent Platform
Gemini 2.5 Flash Live	gemini-2.5-flash-native-audio-preview-12-2025	gemini-live-2.5-flash-native-audio

gemini-live-2.5-flash-native-audio is ADK's LlmAgent.DEFAULT_LIVE_MODEL and the model used in this section's examples.

Choosing a backend

Live models are reached through one of two backends. ADK talks to both with the same code; you switch with environment variables, so you can develop on one and deploy on the other.

	AI Studio	Agent Platform
Full name	Google AI Studio	Gemini Enterprise Agent Platform
Best for	Prototyping, development	Production, enterprise
Auth	API key (GOOGLE_API_KEY)	Cloud credentials (GOOGLE_CLOUD_PROJECT, GOOGLE_CLOUD_LOCATION)
Setup	API key only	Cloud project setup
Limits	Session duration and concurrency	Session duration and concurrency

Switch with the GOOGLE_GENAI_USE_ENTERPRISE environment variable (FALSE for AI Studio, TRUE for Agent Platform); no code changes. See the quickstarts for setup.

!!! note "Agent Platform: confirm location support"

Live model availability varies by location on Agent Platform. Check your
`GOOGLE_CLOUD_LOCATION` against the endpoint-locations table in
[Agent Platform locations](https://docs.cloud.google.com/gemini-enterprise-agent-platform/resources/locations)
before deploying; a regional endpoint such as `us-central1`, `us-east1`, or
`asia-northeast1` is the safe default.

These models produce audio directly, with natural prosody, and detect the conversation language on their own. What you configure on top — voices, transcription, turn detection — is described in Configuration.

One property is fixed at the model level: Live models produce audio only. They do not support the TEXT response modality, so to get text alongside speech you use audio transcription.

Per-model feature support

A few RunConfig settings depend on which model you are running:

Feature	gemini-live-2.5-flash-native-audio
Proactivity and affective dialog	Opt-in via RunConfig
response_scheduling on tools	Supported

Platform limits and quotas

Both backends cap how long a connection and a session can run and how many sessions run at once. These numbers change, so treat the upstream documentation as authoritative and verify before you rely on a limit in production.

Limit	AI Studio	Agent Platform
Session duration, audio-only	15 min	15 min
Session duration, audio + video	2 min	2 min
Connection lifetime	~10 min	~10 min
Concurrent sessions	See rate limits	Up to 1,000 per project on pay-as-you-go; no limit with Provisioned Throughput

Agent Platform additionally caps a conversation session at 10 minutes by default, separately from the audio-only limit above.

Enabling context window compression lets a session be extended past the duration limits. On Agent Platform, request concurrent-session increases from the Cloud Console Quotas page under "Bidi generate content concurrent requests". Verify the current numbers against the AI Studio, Gemini API rate limits, and Agent Platform documentation.

How to handle model names

Read the model name from an environment variable rather than hard-coding it. The same model has a different ID on AI Studio and Agent Platform, so an .env var is what lets one codebase target both backends, and it insulates you from model deprecations.

Recommended Pattern:

import os
from google.adk.agents import Agent

# Use environment variable with fallback to a sensible default
agent = Agent(
    name="my_agent",
    model=os.getenv("DEMO_AGENT_MODEL", "gemini-live-2.5-flash-native-audio"),
    tools=[...],
    instruction="..."
)

Why use environment variables:

• Backend-specific IDs: The same model is named differently on AI Studio and Agent Platform, so moving between them means changing the model ID. An env var keeps that out of your code
• Model availability changes: Models are released and deprecated regularly. A live agent written a year ago should not be pinned in code to a model that no longer exists
• Environment-specific configuration: Use different models for development, staging, and production

Configuration in .env file:

# AI Studio
DEMO_AGENT_MODEL=gemini-2.5-flash-native-audio-preview-12-2025

# Agent Platform
# DEMO_AGENT_MODEL=gemini-live-2.5-flash-native-audio

!!! note "Environment Variable Loading Order"

When using `.env` files with `python-dotenv`, you must call `load_dotenv()` **before** importing any modules that read environment variables. Otherwise, `os.getenv()` will return `None` and fall back to the default value, ignoring your `.env` configuration.

**Correct order in `main.py`:**

```python
from dotenv import load_dotenv
from pathlib import Path

# Load .env file BEFORE importing agent
load_dotenv(Path(__file__).parent / ".env")

# Now safe to import modules that use environment variables
from google_search_agent.agent import agent
```

**Incorrect order (will not work):**

```python
from dotenv import load_dotenv
from google_search_agent.agent import agent  # Agent reads env var here

# Too late! Agent already initialized with default model
load_dotenv(Path(__file__).parent / ".env")
```

This is a Python import behavior: when you import a module, its top-level code executes immediately. If your agent module calls `os.getenv("DEMO_AGENT_MODEL")` at import time, the `.env` file must already be loaded.

Selecting the right model:

1. Choose a backend: AI Studio for prototyping, Agent Platform for production. This picks the ID column in the table above
2. Check current availability: Refer to the model table above and the official documentation
3. Configure environment variable: Set the model name in your .env file and read it from there when constructing the agent

Model compatibility and availability

For the latest information on model compatibility and availability:

• AI Studio: See the Gemini models documentation and the Live API capabilities guide
• Agent Platform: See the Live API overview and the Agent Platform model documentation

Always verify model availability and feature support in the official documentation before deploying to production.