google__adk-docs/docs/agents/models/litert-lm.md

LiteRT-LM model host for ADK agents

Supported in ADKPython v0.1.0Kotlin v0.4.0

You can use the LiteRT-LM library to efficiently run language models locally on various compute devices without requiring specialized processors such as graphics processing units (GPU) or tensor processing units (TPU). LiteRT-LM supports many models, including Google Gemma models as well as third-party models. This guide provides instructions for setting up LiteRT-LM with ADK for the following languages:

• Python
• Kotlin

Python

These instructions describe how to use LiteRT-LM server with ADK in Python with a Gemma open weights model, including using LiteRT-LM 's local hosting model server lit.

Install resources

You need to download a model to use with LiteRT-LM, and the lit CLI tool to help you find a model download it.

Install lit CLI tool

Download and install the lit CLI tool by following these instructions in the LiteRT-LM GitHub repository.

Download a model

Before you start the server, you need to download a model. You'll need a Hugging Face user access token to download a LiteRT-LM model using lit. You can get a token for your Hugging Face account here.

To see a list of models available for download, use the lit list command:

lit list --show_all

Download a model using the lit pull command:

export HUGGING_FACE_HUB_TOKEN="**your Hugging Face token**"
lit pull gemma3n-e2b

Configure your agent

Configure your agent to connect to LiteRT-LM and a hosted model. When running Gemma models with LiteRT-LM, you configure a Gemini model class with the model identifier and local network address.

To use LiteRT-LM with ADK and a Gemma model:

1. Set base_url to the LiteRT-LM server URL, including the scheme, for example: http://localhost:8001.
2. Set model to the LiteRT-LM model name, for example: gemma3n-e2b.

The following example code shows how to configure an agent to connect to the locally hosted LiteRT-LM instance serving the Gemma model configuration described above:

from google.adk.agents import Agent
from google.adk.models import Gemini

root_agent = Agent(
    model=Gemini(
        model="gemma3n-e2b",
        base_url="http://localhost:8001",
    ),
    name="dice_agent",
    description=(
        "hello world agent that can roll a die of 8 sides and check prime"
        " numbers."
    ),
    instruction="""
      You roll dice and answer questions about the outcome of the dice rolls.
    """,
    tools=[
        roll_die,
        check_prime,
    ],
)

Then run the agent as usual:

adk web

Running the LiteRT-LM server

The LiteRT-LM server is a separate process that serves LiteRT-LM models. It is started by the LiteRT-LM CLI tool lit.

Run the server

After downloading a model, start the LiteRT-LM server locally by running the following command:

lit serve --port 8001

!!! tip "Local Server Port Number"

You may choose any port number for the LiteRT-LM server as long as it matches the `base_url` you set in the `Gemini` class in your agent code.

Debugging

To see incoming requests to the LiteRT-LM server and the exact input sent to the model, use the --verbose flag:

lit serve --port 8001 --verbose

Kotlin

These instructions describe how to use LiteRT-LM with ADK in Kotlin using the com.google.adk.kt.litertlm package.

Install resources

You need to download a model to use with LiteRT-LM, and the litert-lm CLI tool to help you find a model download it.

Install LiteRT-LM CLI

Prerequisites: Python 3.10 or higher

To install the CLI, run:

pip install --upgrade litert-lm

For additional installation methods, such as using uv, see LiteRT-LM CLI Installation Guide.

Download a model

Download a model compatible with LiteRT-LM to use the litert-lm CLI tool. Use litert-lm to download models directly from Hugging Face:

litert-lm import \
  --from-huggingface-repo litert-community/gemma-4-E2B-it-litert-lm \
  gemma-4-E2B-it.litertlm

Once downloaded, the model is stored locally at:

~/.litert-lm/models/gemma-4-E2B-it.litertlm/model.litertlm

For more details about litert-lm, refer to the LiteRT-LM CLI Usage Guide.

Add dependencies

ADK Kotlin works with LiteRT-LM through an adapter package, com.google.adk:google-adk-kotlin-litertlm.

In your build.gradle.kts, add com.google.adk:google-adk-kotlin-litertlm and com.google.ai.edge.litertlm:litertlm-jvm to your dependencies:

repositories {
    mavenCentral()
    google()
}

dependencies {
    implementation("com.google.adk:google-adk-kotlin-core:0.9.0")
    implementation("com.google.adk:google-adk-kotlin-litertlm:0.9.0")
    implementation("com.google.ai.edge.litertlm:litertlm-jvm:0.13.1")
    // other dependencies...
}

Configure agent model

Run a local model for your agent with LiteRT-LM by configuring a LiteRtLmModel object as part of your LlmAgent object. If you do not already have a ADK Kotlin project, follow the Kotlin Quickstart for ADK getting started guide. The following code example shows you how to configure an LlmAgent, and set the model parameter to a LiteRtLmModel:

 object HelloTimeAgent {

    // Get model path from environment variable.
    private val modelPath: String by lazy {
        System.getenv("LITERT_LM_MODEL_PATH")
            ?: throw IllegalStateException(
                "LITERT_LM_MODEL_PATH environment variable must be set pointing to a .litertlm file."
            )
    }

    @JvmField
    val rootAgent =
        LlmAgent(
            name = "hello_time_agent",
            description = "Tells the current time in a specified city.",
            model =
                LiteRtLmModel.create(
                    EngineConfig(modelPath = modelPath, backend = Backend.CPU())
                ),
            instruction =
                Instruction(
                    "You are a helpful assistant that tells the current time in a city. " +
                        "Use the 'getCurrentTime' tool for this purpose."
                ),
            tools = TimeService().generatedTools(),
        )
}

In this example, the path to the LiteRT-LM model file is read from the environment variable LITERT_LM_MODEL_PATH. The model will be run on the CPU. You can run the model on a GPU by setting backend = Backend.GPU().

When you run the agent, set LITERT_LM_MODEL_PATH to the location of the model file, for example: ~/.litert-lm/models/gemma-4-E2B-it.litertlm/model.litertlm.

Run your agent

If you followed the Kotlin Quickstart for ADK with the above modifications, you can run your ADK agent using the command-line REPL with the environment variable LITERT_LM_MODEL_PATH set to the path of the model file:

LITERT_LM_MODEL_PATH=~/.litert-lm/models/gemma-4-E2B-it.litertlm/model.litertlm ./gradlew run

Example interaction:

Agent hello_time_agent is ready. Type 'exit' to quit.

You > what's your name?

hello_time_agent > I am Gemma 4, a Large Language Model developed by Google DeepMind.

You > what time is it in paris?

hello_time_agent > calls tool: getCurrentTime

hello_time_agent > The time in Paris is 10:30 am.