RRepoGEO

REPOGEO REPORT · LITE

andrewyng/translation-agent

Default branch main · commit e0fc605a · scanned 6/25/2026, 9:13:14 AM

GitHub: 5,766 stars · 707 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 andrewyng/translation-agent, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    A Python demonstration of an LLM-powered agentic workflow for highly customizable machine translation using reflection to improve accuracy and control style/dialect.
  • highreadme#2
    Refine the README's opening sentence to clarify its specialized focus

    Why:

    CURRENT
    This is a Python demonstration of a reflection agentic workflow for machine translation.
    COPY-PASTE FIX
    This Python demonstration implements a specialized LLM-powered agentic workflow for highly customizable machine translation using reflection. Unlike generic LLM orchestration frameworks, Translation Agent focuses specifically on breaking down and improving translation tasks.

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 andrewyng/translation-agent
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. Hugging Face Transformers · recommended 1×
  4. NLLB-200 · recommended 1×
  5. mBART-50 · recommended 1×
  • CATEGORY QUERY
    How to achieve highly customizable machine translation with LLM reflection for improved accuracy?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. NLLB-200
    3. mBART-50
    4. PEFT
    5. LoRA
    6. QLoRA
    7. LangChain
    8. LlamaIndex
    9. GPT-4
    10. Claude 3 Opus
    11. Mixtral 8x7B
    12. OpenNMT-py
    13. OpenAI API
    14. Anthropic API
    15. Llama 3
    16. Fairseq
    17. Google Cloud Translation Advanced
    18. Vertex AI
    19. Vertex AI's Generative AI Studio
    20. Vertex AI SDK
    21. Gemini 1.5 Pro
    22. PaLM 2
    23. Microsoft Azure AI Translator
    24. Custom Translator
    25. Azure OpenAI Service
    26. GPT-3.5 Turbo
    27. DeepL API

    AI recommended 27 alternatives but never named andrewyng/translation-agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a Python library for agentic LLM-driven translation with style and dialect control.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/haystack)
    4. Transformers (huggingface/transformers)
    5. Guidance (microsoft/guidance)
    6. LiteLLM

    AI recommended 6 alternatives but never named andrewyng/translation-agent. 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 andrewyng/translation-agent?
    pass
    AI did not name andrewyng/translation-agent — 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 andrewyng/translation-agent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named andrewyng/translation-agent 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 andrewyng/translation-agent solve, and who is the primary audience?
    pass
    AI named andrewyng/translation-agent explicitly

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

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andrewyng/translation-agent — 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