RRepoGEO

REPOGEO REPORT · LITE

isoftstone-data-intelligence-ai/efflux-backend

Default branch main · commit c25742b0 · scanned 6/6/2026, 2:33:09 PM

GitHub: 722 stars · 71 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 isoftstone-data-intelligence-ai/efflux-backend, 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
  • highlicense#1
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highabout#2
    Add a concise repository description and relevant topics

    Why:

    CURRENT
    Description: (none)
Topics: (none)
    COPY-PASTE FIX
    Description: Backend for an LLM Agent chat client, featuring streaming responses, chat history, and Model Context Protocol (MCP) integration for standardized tool invocation.
Topics: llm, agent, chat, backend, fastapi, model-context-protocol, mcp, ai-agents, streaming, python
  • mediumreadme#3
    Refine the README's opening paragraph for clearer positioning

    Why:

    CURRENT
    Efflux is an LLM-based Agent chat client featuring streaming responses and chat history management. As an MCP Host, it leverages the Model Context Protocol to connect with various MCP Servers, enabling standardized tool invocation and data access for large language models.
    COPY-PASTE FIX
    Efflux is the backend for an advanced LLM Agent chat client, designed for real-time streaming responses and robust chat history management. As an MCP Host, it uniquely leverages the Model Context Protocol to enable standardized tool invocation and data access for large language models, setting it apart from generic web backends and other LLM frameworks.

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 isoftstone-data-intelligence-ai/efflux-backend
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FastAPI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. FastAPI · recommended 2×
  2. Node.js · recommended 1×
  3. Express.js · recommended 1×
  4. Fastify · recommended 1×
  5. Spring Boot · recommended 1×
  • CATEGORY QUERY
    What are good backend frameworks for building LLM agent chat applications with real-time streaming and history?
    you: not recommended
    AI recommended (in order):
    1. FastAPI
    2. Node.js
    3. Express.js
    4. Fastify
    5. Spring Boot
    6. Go
    7. net/http
    8. Gin
    9. Echo
    10. Phoenix
    11. Django
    12. Django Channels

    AI recommended 12 alternatives but never named isoftstone-data-intelligence-ai/efflux-backend. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement a backend for AI agents that supports multiple LLMs and standardized tool invocation?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Assistants API
    5. Microsoft Semantic Kernel
    6. LiteLLM
    7. FastAPI
    8. Flask
    9. Pydantic

    AI recommended 9 alternatives but never named isoftstone-data-intelligence-ai/efflux-backend. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    Suggestion:

  • 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 isoftstone-data-intelligence-ai/efflux-backend?
    pass
    AI did not name isoftstone-data-intelligence-ai/efflux-backend — likely talking about a different project

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

  • If a team adopts isoftstone-data-intelligence-ai/efflux-backend in production, what risks or prerequisites should they evaluate first?
    pass
    AI named isoftstone-data-intelligence-ai/efflux-backend 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 isoftstone-data-intelligence-ai/efflux-backend solve, and who is the primary audience?
    pass
    AI named isoftstone-data-intelligence-ai/efflux-backend explicitly

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

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isoftstone-data-intelligence-ai/efflux-backend — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite