RRepoGEO

REPOGEO REPORT · LITE

KwaiKEG/KwaiAgents

Default branch main · commit 3504ab8a · scanned 6/19/2026, 10:47:41 PM

GitHub: 1,200 stars · 112 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 KwaiKEG/KwaiAgents, 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 README opening to highlight system's purpose and KwaiKEG integration

    Why:

    CURRENT
    KwaiAgents is a series of Agent-related works open-sourced by the KwaiKEG from Kuaishou Technology. The open-sourced content includes:
1. **KAgentSys-Lite**: a lite version of the KAgentSys in the paper. While retaining some of the original system's functionality...
    COPY-PASTE FIX
    KwaiAgents is a comprehensive, generalized information-seeking agent system developed by KwaiKEG from Kuaishou Technology. It leverages tight integration with the KwaiKEG knowledge graph to simulate realistic social interactions and user behaviors, providing a robust framework for building advanced AI agents. This open-source initiative includes:
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    http://arxiv.org/abs/2312.04889
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, e.g., 'This project is licensed under [Specify License Name(s) from LICENSE file here]. Please refer to the LICENSE file for full details.'

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 KwaiKEG/KwaiAgents
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. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. AutoGPT · recommended 1×
  5. BabyAGI · recommended 1×
  • CATEGORY QUERY
    How can I build a generalized information-seeking agent system using large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGPT
    5. BabyAGI
    6. OpenAI API
    7. Hugging Face Transformers
    8. Hugging Face Datasets
    9. Pinecone
    10. Weaviate
    11. Qdrant

    AI recommended 11 alternatives but never named KwaiKEG/KwaiAgents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are lightweight open-source frameworks for developing autonomous LLM agents similar to Auto-GPT?
    you: not recommended
    AI recommended (in order):
    1. CrewAI
    2. AutoGen
    3. LangChain
    4. LlamaIndex
    5. Haystack
    6. MemGPT

    AI recommended 6 alternatives but never named KwaiKEG/KwaiAgents. 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 KwaiKEG/KwaiAgents?
    pass
    AI named KwaiKEG/KwaiAgents explicitly

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

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

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

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KwaiKEG/KwaiAgents — 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