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FastAI 0.1.6 — Unified AI client for Java

Status License: MIT Java Platform JitPack

💡 One interface for all Local and Cloud AI models — No JSON, No HTTP, No Boilerplate.

FastAI is a minimalist, hyper-fast Java AI library that unifies all major LLM providers (Ollama, LM Studio, OpenAI, OpenRouter.ai, Claude, Mistral, DeepSeek) behind a single, elegant interface. Built for Java developers who hate JSON parsing, HTTP clients, and bloated frameworks.

If you need a drop-in AI module, multi-provider interchangeability, or clean FastJava-style code, FastAI is your solution.

FastAI Showcase

Quick Start

import fastai.AI;
import fastai.FastAI;

public class QuickStartDemo {
    public static void main(String[] args) {
        // 1. Direct Local GGUF Engine with full Vulkan/Metal GPU Offloading (ON)
        AI gpuAI = FastAI.connect("llama:models/qwen2.5-coder-1.5b.gguf")
                         .withGpu(true)        // Full GPU acceleration ON (99 layers)
                         .withContextSize(2048);

        // 2. Direct Local GGUF Engine on CPU Only (GPU OFF)
        AI cpuAI = FastAI.connect("llama:models/qwen2.5-coder-1.5b.gguf")
                        .withGpu(false);       // Force CPU execution (0 GPU layers)

        // 3. Fluid streaming with full sampling control
        gpuAI.withSystemPrompt("You are an expert Java performance engineer.")
             .withTemperature(0.7f)
             .withTopP(0.9f)
             .withTopK(40)
             .withMaxTokens(256)
             .stream("Write a quicksort in Java:", token -> {
                 System.out.print(token);
                 System.out.flush();
             });

        // 4. Cloud provider instance with identical sampling & streaming interface
        AI cloudAI = FastAI.connect("openai:gpt-4o", System.getenv("OPENAI_API_KEY"));
        cloudAI.withTemperature(0.2f)
               .withMaxTokens(500)
               .stream("Summarize the latest 2026 tech trends:", token -> {
                   System.out.print(token);
                   System.out.flush();
               });
    }
}

Table of Contents

Why FastAI?

Current AI libraries in Java (LangChain4j, Spring AI) are huge, framework-heavy, and come with dependency hell. Direct SDKs lock you into one provider.

FastAI solves this by providing:

  • Zero JSON handling — everything is native Java Strings and Files.
  • Provider Interchangeability — switch between ollama, openrouter, and openai by changing one string.
  • Zero Dependencies — pure Java 17+, no Jackson, no Spring.
  • True Unified Interface — AI is all you need to know.

Key Features

  • 🌐 Local + Cloud Support — Use local models, OpenRouter, or cloud giants with the exact same code.
  • ⚡ In-Process Local GPU Engine — Direct zero-IPC local LLM inference via FastAIModel with Vulkan (Intel/AMD/NVIDIA) and Metal (Apple Silicon) GPU offloading.
  • 🔌 OpenRouter Unified Gateway — Access 200+ models (Claude 3.5, DeepSeek R1, Llama 3.3, GPT-4o) using openrouter:model/name.
  • 📎 Simple Attachments — Pass a java.io.File and let FastAI handle the Base64/Multipart encoding.
  • 🎭 System Prompts — Native support for System vs User prompts.
  • ⚡ Ultra-Lightweight — Just drop the JAR into your project.
  • 🌊 Streaming First — Every provider supports unified streaming callbacks.

Installation

Option 1: Maven (Recommended)

Add the JitPack repository and the dependencies to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
<!-- FastAI Library -->
<dependency>
    <groupId>com.github.andrestubbe</groupId>
    <artifactId>fastai</artifactId>
    <version>0.1.6</version>
</dependency>

<!-- FastJSON (Required Dependency) -->
<dependency>
    <groupId>com.github.andrestubbe</groupId>
    <artifactId>FastJSON</artifactId>
    <version>0.1.6</version>
</dependency>

<!-- FastCore (Required Native Loader) -->
<dependency>
    <groupId>com.github.andrestubbe</groupId>
    <artifactId>fastcore</artifactId>
    <version>0.1.6</version>
</dependency>

<!-- FastString (Required Dependency) -->
<dependency>
    <groupId>com.github.andrestubbe</groupId>
    <artifactId>FastString</artifactId>
    <version>0.1.6</version>
</dependency>

<!-- FastAIModel (Local In-Process GPU Engine) -->
<dependency>
    <groupId>com.github.andrestubbe.FastAIModel</groupId>
    <artifactId>fastaimodel-llama</artifactId>
    <version>0.1.3</version>
</dependency>
</dependencies>

Option 2: Gradle (via JitPack)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:fastai:0.1.6'
    implementation 'com.github.andrestubbe:FastJSON:0.1.6'
    implementation 'com.github.andrestubbe:fastcore:0.1.6'
    implementation 'com.github.andrestubbe:FastString:0.1.6'
    implementation 'com.github.andrestubbe:FastBytes:0.1.6'
    implementation 'com.github.andrestubbe.FastAIModel:fastaimodel-llama:0.1.3'
}

Option 3: Direct Download (No Build Tool)

Download the latest JARs directly to add them to your classpath:

  1. 🚀 fastai-0.1.6.jar (Core Library)
  2. 🤖 fastaimodel-llama-0.1.3.jar (Local In-Process GPU Engine)
  3. 📦 FastJSON-0.1.6.jar (Required JSON Parser)
  4. ⚙️ fastcore-0.1.6.jar (Mandatory Native JNI Loader)
  5. 📦 FastString-0.1.6.jar (Required String Dependency)
  6. 📦 FastBytes-0.1.6.jar (Required Bytes Dependency)

Important

All JARs must be in your classpath for the native JNI calls to function correctly.

API Reference

Connect

// Local Providers & In-Process GPU Engine
AI ai = FastAI.connect("llama:models/qwen2.5-coder-1.5b.gguf"); // Native Vulkan/Metal In-Process GPU Engine
AI ai = FastAI.connect("ollama:llama3.1");
AI ai = FastAI.connect("lmstudio:phi3");

// OpenRouter Unified Gateway (200+ models)
AI ai = FastAI.connect("openrouter:anthropic/claude-3.5-sonnet", "sk-or-...");
AI ai = FastAI.connect("openrouter:deepseek/deepseek-r1", "sk-or-...");

// Cloud Providers (requires API Key as second argument)
AI ai = FastAI.connect("openai:gpt-4o", "sk-...");
AI ai = FastAI.connect("claude:opus", "sk-ant-...");
AI ai = FastAI.connect("mistral:large", "key...");
AI ai = FastAI.connect("deepseek:chat", "key...");

Generation & Prompting

// Simple prompt
String answer = ai.ask("Hello!");

// System + User prompt
String answer = ai.ask("You are a math expert.", "Explain integrals.");

// Multimodal (Vision/Files)
String answer = ai.ask("What is in this image?", new File("diagram.png"));

Streaming

ai.stream("Write a poem",System.out::print);

Providers Supported

Provider Type Status Features
Ollama Local ✔️ Native Chat, Streaming, List Models
llama.cpp Local ✔️ Native GGUF Local Inference (GPU missing)
LM Studio Local ✔️ Native Chat, Streaming via Local API
Gemini Cloud ✔️ Native Chat, Streaming, List Models
OpenRouter Cloud ✔️ Native Chat, Streaming, 200+ Models
OpenAI Cloud ✔️ Native Chat, Streaming
Anthropic Claude Cloud ✔️ Native Chat, Streaming
Mistral Cloud ✔️ Native Chat, Streaming
DeepSeek Cloud ✔️ Native Chat, Streaming

Performance

FastAI is zero-dependency and zero-allocation for the core connection layer:

Metric LangChain4j Spring AI FastAI
Dependencies 15+ 20+ 0
JAR Size ~5MB ~10MB ~50KB
Startup Time 2-3s 5-10s <100ms
Memory Overhead High High Minimal
Learning Curve Hours Hours 5 minutes

Examples

Every feature has a standalone example in examples/:

cd examples/Demo
mvn compile exec:java    # Run demo
Example Demonstrates
Demo Local AI, Cloud AI, Streaming

API Quick Reference

Method / Factory Return Type Description
FastAI.connect(spec, args...) AI Connects to a provider (e.g. "gemini", "ollama") with optional API key.
ai.ask(prompt) String Sends a simple user prompt to the model and returns the response.
ai.ask(systemPrompt, userPrompt) String Sends a prompt with a configured system instruction.
ai.ask(prompt, file) String Sends a prompt along with a file attachment (images/vision).
ai.stream(prompt, handler) void Streams tokens back dynamically as they are generated.
ai.getModels() List<String> Lists all available models from the provider.

Documentation

  • COMPILE.md: Full compilation guide (MSVC C++17 build chain + JNI Setup).
  • REFERENCE.md: Exhaustive catalog of SGR styles, OSC window parameters, and callback contracts.
  • PHILOSOPHY.md: Zero-allocation and low-overhead processing designs.
  • ROADMAP.md: Planned milestone features and performance extensions.
  • CHANGELOG.md

Platform Support

Platform Status
Windows 10/11 ✅ Fully Supported
Linux 🚧 Planned
macOS 🚧 Planned

License

MIT License See LICENSE file for details.

Related Projects

  • FastAIMemory - Unified conversation history and prompt formatters
  • FastAIModel - Native local inference runtime (GGUF/ONNX)
  • FastCore - Unified JNI loader and platform abstraction

Part of the FastJava Ecosystem — Making the JVM faster. Small package. Maximum speed. Zero bloat. 🚀📋

About

🤖 Unified AI client for Java. One interface for all Local and Cloud AI models. No JSON, No HTTP boilerplate.

github.com/andrestubbe

Topics

aianthropiccloud-aijavajitpackllmlocal-aiollamaopenaiperformanceunified-api

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