RRepoGEO

REPOGEO REPORT · LITE

InfinitiBit/graphbit

Default branch main · commit f80c46e8 · scanned 6/8/2026, 11:31:19 PM

GitHub: 557 stars · 117 forks

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 InfinitiBit/graphbit, 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
  • highreadme#1
    Prominently feature the project's core purpose in the README's opening

    Why:

    CURRENT
    The current README starts with a centered div containing badges and links, followed by 'GraphBit - High Performance Agentic Framework' and then 'Type-Safe AI Agent Workflows with Rust Performance'.
    COPY-PASTE FIX
    Insert the following text as the very first content in the README, before any existing headings, badges, or links: 'GraphBit is the world’s first enterprise-grade Agentic AI framework, built on a Rust core with a Python wrapper for unmatched speed, security, and scalability. It enables reliable multi-agent workflows with minimal CPU and memory usage, making it production-ready for real-world enterprise environments.'
  • mediumreadme#2
    Add a section comparing GraphBit to common agentic AI frameworks

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why GraphBit?' or 'GraphBit vs. [Competitor Names]' that explicitly outlines GraphBit's unique advantages (e.g., Rust core performance, low resource usage, enterprise-grade reliability, type-safety) compared to frameworks like CrewAI, AutoGen, LangChain, and LlamaIndex.
  • mediumexamples#3
    Add a minimal quickstart example directly in the README

    Why:

    COPY-PASTE FIX
    Include a 'Quickstart' section in the README with a simple, copy-pasteable Python code snippet demonstrating how to initialize a basic multi-agent workflow using GraphBit.

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 InfinitiBit/graphbit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
joaomdmoura/crewai
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. joaomdmoura/crewai · recommended 1×
  2. microsoft/autogen · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. run-llama/llama_index · recommended 1×
  5. deepset-ai/haystack · recommended 1×
  • CATEGORY QUERY
    Need a production-ready agentic AI framework for reliable multi-agent workflows with low resource usage.
    you: not recommended
    AI recommended (in order):
    1. CrewAI (joaomdmoura/crewai)
    2. AutoGen (microsoft/autogen)
    3. LangChain (langchain-ai/langchain)
    4. LlamaIndex (run-llama/llama_index)
    5. Haystack (deepset-ai/haystack)

    AI recommended 5 alternatives but never named InfinitiBit/graphbit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are high-performance agentic AI frameworks for building scalable and secure enterprise applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft Semantic Kernel
    4. Haystack by deepset
    5. AutoGPT
    6. CrewAI
    7. OpenAI Assistants API

    AI recommended 7 alternatives but never named InfinitiBit/graphbit. 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 InfinitiBit/graphbit?
    pass
    AI named InfinitiBit/graphbit explicitly

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

  • If a team adopts InfinitiBit/graphbit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named InfinitiBit/graphbit 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 InfinitiBit/graphbit solve, and who is the primary audience?
    pass
    AI named InfinitiBit/graphbit explicitly

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

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InfinitiBit/graphbit — 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