RRepoGEO

REPOGEO REPORT · LITE

zai-org/CodeGeeX2

Default branch main · commit 754a2082 · scanned 5/28/2026, 5:12:41 PM

GitHub: 7,563 stars · 536 forks

AI VISIBILITY SCORE
33 /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
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 zai-org/CodeGeeX2, 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 for AI recall

    Why:

    CURRENT
    ⭐️ 最新一代 CodeGeeX4 模型已经正式开源。
The newest CodeGeeX4 has been released.

# CodeGeeX2: 更强大的多语言代码生成模型
    COPY-PASTE FIX
    Insert the following sentence directly after the 'The newest CodeGeeX4 has been released.' line and before the '# CodeGeeX2: 更强大的多语言代码生成模型' heading: 'CodeGeeX2 is a leading open-source, multilingual AI coding assistant and generation model, offering superior performance and comprehensive features for developers across 100+ programming languages.'
  • mediumtopics#2
    Enhance GitHub topics for better categorization

    Why:

    CURRENT
    code, code-generation, pretrained-models, tool
    COPY-PASTE FIX
    code, code-generation, pretrained-models, tool, llm, ai-assistant, developer-tools, code-completion, multilingual-code, code-assistant
  • mediumreadme#3
    Explicitly state core differentiators in README

    Why:

    COPY-PASTE FIX
    Integrate a new 'Key Differentiators' section or a prominent paragraph early in the README, explicitly listing CodeGeeX2's unique advantages. For example: 'Key Differentiators: CodeGeeX2 stands out with its superior performance (e.g., 35.9% Pass@1 on Python HumanEval-X, surpassing StarCoder-15B), comprehensive multilingual support (100+ languages), efficient local deployment (6GB VRAM), and advanced AI programming assistant features (contextual completion, cross-file completion, code explanation, translation, debugging).'

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 zai-org/CodeGeeX2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 2×
  2. CodeSearchNet · recommended 1×
  3. BigQuery Public Datasets · recommended 1×
  4. OpenAI Codex · recommended 1×
  5. Google Gemini · recommended 1×
  • CATEGORY QUERY
    How can I improve code generation accuracy for various programming languages with an AI assistant?
    you: not recommended
    AI recommended (in order):
    1. CodeSearchNet
    2. BigQuery Public Datasets
    3. GitHub Copilot
    4. OpenAI Codex
    5. Google Gemini
    6. OpenAI GPT-4
    7. Faiss (facebookresearch/faiss)
    8. Pinecone
    9. ESLint (eslint/eslint)
    10. Pylint (pylint-dev/pylint)
    11. SonarQube

    AI recommended 11 alternatives but never named zai-org/CodeGeeX2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an efficient AI model to assist with coding in multiple programming languages.
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Tabnine
    3. CodeWhisperer (Amazon Q Developer)
    4. Google Gemini (via Google Cloud Code Assist)
    5. Cursor

    AI recommended 5 alternatives but never named zai-org/CodeGeeX2. 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 zai-org/CodeGeeX2?
    pass
    AI did not name zai-org/CodeGeeX2 — 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 zai-org/CodeGeeX2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zai-org/CodeGeeX2 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 zai-org/CodeGeeX2 solve, and who is the primary audience?
    pass
    AI named zai-org/CodeGeeX2 explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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