RRepoGEO

REPOGEO REPORT · LITE

ModelEngine-Group/fit-framework

Default branch main · commit e2f285d1 · scanned 5/19/2026, 8:21:43 AM

GitHub: 2,107 stars · 333 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ModelEngine-Group/fit-framework, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Clarify repo description to emphasize LLM application focus

    Why:

    CURRENT
    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。
    COPY-PASTE FIX
    FIT: 企业级AI开发框架，专注于构建大模型（LLM）应用。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。
  • hightopics#2
    Add LLM-specific topics to improve categorization

    Why:

    CURRENT
    agentic-ai, ai, java, plugin, plugin-system, python
    COPY-PASTE FIX
    agentic-ai, ai, java, plugin, plugin-system, python, llm, large-language-models, langchain-alternative, orchestration
  • mediumreadme#3
    Add concise English LLM application summary to README

    Why:

    COPY-PASTE FIX
    Add this line right after the main bold Chinese description: "A Java enterprise AI development framework for building Large Language Model (LLM) applications, offering a LangChain alternative for the Java ecosystem and powerful orchestration capabilities."

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface ModelEngine-Group/fit-framework
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain4j
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain4j · recommended 1×
  2. Spring AI · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. Deep Java Library (DJL) · recommended 1×
  5. openai-java · recommended 1×
  • CATEGORY QUERY
    Looking for a robust Java-based framework to build LLM applications, similar to LangChain.
    you: not recommended
    AI recommended (in order):
    1. LangChain4j
    2. Spring AI
    3. Hugging Face Transformers
    4. Deep Java Library (DJL)
    5. openai-java

    AI recommended 5 alternatives but never named ModelEngine-Group/fit-framework. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to integrate large language models into existing enterprise Java systems with scalable orchestration?
    you: not recommended
    AI recommended (in order):
    1. Spring AI (spring-projects/spring-ai)
    2. Spring Boot (spring-projects/spring-boot)
    3. Kubernetes (kubernetes/kubernetes)
    4. OpenShift
    5. Spring Cloud Kubernetes (spring-cloud/spring-cloud-kubernetes)
    6. Spring Cloud OpenShift (spring-cloud/spring-cloud-openshift)
    7. LangChain4j (langchain4j/langchain4j)
    8. Hugging Face Inference Endpoints
    9. Hugging Face
    10. Spring WebClient (spring-projects/spring-framework)
    11. OkHttp (square/okhttp)
    12. Azure OpenAI Service
    13. Google Cloud Vertex AI
    14. AWS Bedrock
    15. Azure SDK for Java (Azure/azure-sdk-for-java)
    16. Google Cloud Client Libraries for Java (googleapis/java-cloud-clients)
    17. AWS SDK for Java (aws/aws-sdk-java-v2)
    18. AKS
    19. GKE
    20. EKS
    21. Knative (knative/serving)
    22. Apache Kafka (apache/kafka)
    23. Istio (istio/istio)
    24. Linkerd (linkerd/linkerd2)

    AI recommended 24 alternatives but never named ModelEngine-Group/fit-framework. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of ModelEngine-Group/fit-framework?
    pass
    AI named ModelEngine-Group/fit-framework explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts ModelEngine-Group/fit-framework in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ModelEngine-Group/fit-framework explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo ModelEngine-Group/fit-framework solve, and who is the primary audience?
    pass
    AI named ModelEngine-Group/fit-framework explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of ModelEngine-Group/fit-framework. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/ModelEngine-Group/fit-framework.svg)](https://repogeo.com/en/r/ModelEngine-Group/fit-framework)
HTML
<a href="https://repogeo.com/en/r/ModelEngine-Group/fit-framework"><img src="https://repogeo.com/badge/ModelEngine-Group/fit-framework.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

ModelEngine-Group/fit-framework — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite