# ModelEngine-Group/fit-framework

FIT: 企业级AI开发框架，提供多语言函数引擎（FIT）、流式编排引擎（WaterFlow）及Java生态的LangChain替代方案（FEL）。原生/Spring双模运行，支持插件热插拔与智能聚散部署，无缝统一大模型与业务系统。

Repository: https://github.com/ModelEngine-Group/fit-framework
Canonical: https://ross.abutalabs.com/products/fit-framework
Homepage: https://modelengine-ai.net
Language: Java
License: MIT
License Family: permissive
Topics: ai, java, python, agentic-ai, plugin, plugin-system
Last push: 2026-03-13T03:40:21+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 72, release rhythm 74, longevity 39
- inputs: {"age_days": 557, "days_push": 173, "days_rel": 173, "gap_med": 14.0, "n_releases_24m": 21}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2117, forks 335 (observed 2026-08-28T04:06:16.350571+00:00)

## What it is
FIT is an enterprise-grade AI development framework for the Java ecosystem, combining a polyglot function engine (Java/Python/C++), a streaming orchestration engine (WaterFlow), and FEL, a LangChain-style LLM application library for Java. It runs natively or within Spring, supports hot-pluggable plugins, and transparently switches between monolithic and distributed deployment.

## Use cases
- build LLM-powered applications in Java without switching to Python
- replace LangChain with a Java-native alternative
- orchestrate RAG retrieval pipelines with vector stores
- build AI agents with prompts and tool delegation
- compose business logic as streaming flows
- switch a monolith to distributed services without code changes
- hot-load plugins into a running AI application

## When to choose
- your team is Java-centric and wants AI/LLM capabilities in the existing stack
- you need both graphical and declarative flow orchestration
- you want transparent local/remote call routing across monolith and microservice deployments
- you need hot-swappable plugin architecture for AI services

## When to avoid
- your stack is Python-first and LangChain/LlamaIndex already fit
- you need a mature ecosystem with extensive community integrations
- you only need a simple LLM API wrapper without orchestration
- you require guaranteed long-term stability for a very young framework

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, rag, llm-inference, workflow-automation, plugin-system, rpc, web-framework
- domain: artificial-intelligence, large-language-models, developer-tools, backend, microservices
- platform: jvm, python, cross-platform
- tags: java-ai-framework, langchain-alternative, flow-orchestration, hot-pluggable-plugins, polyglot-functions, spring-integration, fel, ai-agents, retrieval-augmented-generation

## Member repositories
- ModelEngine-Group/fit-framework (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:16.350571+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:07.445633+00:00, confidence not recorded.
  - readme: https://github.com/ModelEngine-Group/fit-framework (fetched 2026-08-28T04:06:16.350571+00:00, sha d83409b6506b)
- Data as of 2026-08-30T08:39:29.467469+00:00.