Summary TinyAI is a full-stack, lightweight AI framework built entirely in Java, aiming to provide comprehensive AI capabilities within the Java ecosystem. It targets Java developers, researchers, and educators, offering an extensible, modular, and production-ready solution for building AI applications with zero core dependencies.

How It Works The framework utilizes a pure Java, modular architecture with 22 core modules, spanning from a multi-dimensional array computation engine and auto-differentiation to advanced intelligent agent systems and large language models. This design leverages Java's ecosystem, providing a clear, educational, and production-ready foundation for AI development.

Quick Start & Requirements

• Prerequisites: Java JDK 17+, Maven 3.6+. Recommended 8GB+ RAM for large models.
• Installation: Clone repo, set JAVA_HOME, then use Maven (mvn clean compile, mvn install).
• Repository: https://github.com/leavesfly/TinyAI.git.

Highlighted Details

• Intelligent Agent System: Comprehensive features including RAG, tool calling, self-evolution, multi-agent collaboration, cognitive patterns, embodied intelligence (autonomous driving, robotics), and AI programming assistance.
• Deep Learning Core: N-dimensional array library (CPU/GPU/TPU support), dynamic auto-differentiation, neural networks, ML framework, and RL algorithms.
• Large Language Models: Implements GPT series, DeepSeek, and Qwen3 models, supporting LoRA fine-tuning and MoE architectures.
• Applications: Demonstrates enterprise use cases like intelligent customer service, code generation, document processing, and autonomous driving simulations.

Maintenance & Community Actively developed with recent additions to embodied intelligence and manuscript agents. Support is available via GitHub Issues, a discussion community, and email. Contribution guidelines emphasize Java standards, comments, and unit testing.

Licensing & Compatibility Released under the Apache License 2.0, which permits commercial use and integration into closed-source projects.

Limitations & Caveats GPU/TPU acceleration for the N-dimensional array library may implicitly require compatible hardware/drivers. Primary documentation and comments are in Chinese, potentially posing a language barrier. The project is under active development, with new modules being added, suggesting it may not yet be feature-complete for all potential use cases.