RRepoGEO

REPOGEO REPORT · LITE

polyuiislab/infiAgent

Default branch main · commit ac97e466 · scanned 5/18/2026, 9:46:50 AM

GitHub: 1,175 stars · 51 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 polyuiislab/infiAgent, 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
    Reposition the README introduction to clearly state its AI agent framework category and differentiators

    Why:

    CURRENT
    **infiAgent Also called MLA (Multi-Level Agent)** is an agent framework designed for **unlimited runtime** without tool calling chaos or system crashes caused by cumulative task resources and conversation history.
    COPY-PASTE FIX
    **infiAgent (also known as MLA)** is a cutting-edge **AI agent framework** designed to build robust, domain-specific agents capable of handling **multi-day, complex tasks** with true breakpoint continuation and persistent memory. Unlike traditional frameworks, MLA focuses on **unlimited runtime** without context accumulation issues, offering a unique solution for orchestrating multi-level AI tasks via simple configuration.
  • hightopics#2
    Add more specific AI agent framework topics

    Why:

    CURRENT
    agent, framework, sdk, sdk-python
    COPY-PASTE FIX
    agent, framework, sdk, sdk-python, ai-agent, llm, multi-agent-system, agent-orchestration
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/polyuiislab/infiAgent

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 polyuiislab/infiAgent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. CrewAI · recommended 2×
  3. PostgreSQL · recommended 1×
  4. MongoDB · recommended 1×
  5. Microsoft Semantic Kernel · recommended 1×
  • CATEGORY QUERY
    How to build robust AI agents capable of handling multi-day complex tasks with recovery?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. PostgreSQL
    3. MongoDB
    4. Microsoft Semantic Kernel
    5. Azure Durable Functions
    6. AutoGPT
    7. OpenAI Assistants API
    8. CrewAI
    9. Temporal.io
    10. Apache Kafka
    11. RabbitMQ
    12. AWS SQS

    AI recommended 12 alternatives but never named polyuiislab/infiAgent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an agent framework to orchestrate complex, multi-level AI tasks via configuration.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGen
    5. CrewAI
    6. Marvin

    AI recommended 6 alternatives but never named polyuiislab/infiAgent. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 polyuiislab/infiAgent?
    pass
    AI named polyuiislab/infiAgent explicitly

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

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

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

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polyuiislab/infiAgent — 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