FIT Framework is an enterprise-grade AI development framework for Java developers, offering a multi-language function engine (FIT), a stream orchestration engine (WaterFlow), and a LangChain alternative (FEL). It aims to unify large language models with business systems, enabling Java developers to build modern AI applications without switching tech stacks, while retaining Java's performance and engineering advantages.

How It Works

FIT Core provides a multi-language (Java, Python, C++) function computing base with hot-swappable plugins and a unique "intelligent clustering and distribution" deployment. This allows seamless switching between monolithic and distributed services, with automatic routing for local or RPC calls, abstracting infrastructure complexity. WaterFlow Engine breaks down BPM and reactive programming barriers, offering graphical orchestration and declarative APIs for dynamic business logic assembly, from micro-flows to long transactions. FEL (FIT Expression for LLM) provides a Java-native approach to LLM application development, encapsulating models, knowledge bases, and toolchains using standardized primitives.

Quick Start & Requirements

• Install/Run: Compile with mvn clean install, then run build/bin/fit start.
• Prerequisites: JDK 17 or 21, Maven 3.8.8+.
• Setup: Requires setting a FIT_FRAMEWORK_HOME environment variable and creating a plugin directory.
• Docs: FIT Quick Start, WaterFlow Quick Start, FEL Quick Start.

Highlighted Details

• First full-stack AI framework for the Java ecosystem.
• Supports native and Spring dual-mode operation.
• Offers hot-swappable plugins and intelligent deployment modes.
• Aims for 80% zero-configuration for common scenarios via "intelligent contracts."

Maintenance & Community

• Active development with a SNAPSHOT release.
• Community channels include WeChat Official Account (FitFramework), WeChat technical exchange group, and QQ group (1029802553).
• Contribution guidelines are available in CONTRIBUTING.md.

Licensing & Compatibility

• Licensed under MIT.
• Compatible with commercial and closed-source applications.

Limitations & Caveats

The project is currently at a SNAPSHOT version, indicating potential instability or ongoing changes. Specific details on performance benchmarks or real-world adoption in commercial products are not immediately available in the README, though a "Model Engine" is mentioned as an example.