# google-ai-edge/litert-samples

> LiteRT and LiteRT-LM sample apps, model recipes, agent skills and utilities.

## Fast Facts & Telemetry
- **Canonical URL**: https://githubrepo.cloud/repo/google-ai-edge/litert-samples
- **GitHub Source**: https://github.com/google-ai-edge/litert-samples
- **Global Rank**: #9,508 on GitHubRepo
- **Stars**: 462
- **Forks**: 122
- **Open Issues**: 105
- **Primary Language**: Python
- **Software License**: Apache-2.0
- **Last Commit / Push**: Oct 12, 2026
- **Topics**: edge-ai, generative-ai, on-device-ai

## Project Overview
litert-samples is an open-source project written in Python: LiteRT and LiteRT-LM sample apps, model recipes, agent skills and utilities.

Engineered for speed, consistency, and developer ease, it solves common hurdles in edge-ai, generative-ai, on-device-ai. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

## Key Features
- Optimized execution pipeline written in Python for predictable speed.
- Zero-friction configuration with comprehensive sensible defaults out of the box.
- Cross-platform runtime support across Linux, macOS, and Windows environments.
- Strong typing and modular architecture designed for easy extension and maintainability.
- Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
- Active community maintenance with regular dependency updates and security patches.

## Installation & Quick Start
### Linux
```bash
pip install litert-samples
```

### macOS
```bash
pip install litert-samples # or: brew install litert-samples
```

### Windows
```bash
pip install litert-samples
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: Python >= 3.9, pip, virtualenv
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
litert-samples coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

## Production Use Cases
### Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with litert-samples.

### Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.

### Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.

### Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.

## Target Audience
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 462 GitHub stars and verified adoption.
- Permissive open-source distribution under the Apache-2.0 license.
- Built in Python for high execution speed and developer familiarity.
- Cross-platform compatibility across modern Linux, macOS, and Windows environments.
- Clean modular design allowing flexible configuration and pipeline integration.

## Considerations
- Requires familiarity with Python and modern CLI workflows.
- Ecosystem extensions may require manual configuration depending on environment constraints.
- Active development roadmap means breaking API changes may occur across major versions.

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