RRepoGEO

REPOGEO REPORT · LITE

HKUDS/Auto-Deep-Research

Default branch main · commit 9d671a33 · scanned 5/9/2026, 2:47:52 PM

GitHub: 1,520 stars · 216 forks

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 HKUDS/Auto-Deep-Research, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory of the repository. Choose an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and include its full text.
  • highabout#2
    Update the repository's 'About' description for clarity

    Why:

    CURRENT
    Your Fully-Automated Personal AI Assistant
    COPY-PASTE FIX
    A fully-automated, cost-effective AI assistant for deep learning research, offering an open-source alternative to OpenAI's Deep Research.
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    agent, deep-research, llm
    COPY-PASTE FIX
    agent, deep-research, llm, ai-assistant, research-automation, llm-agent, gaia-benchmark, open-source-ai

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 HKUDS/Auto-Deep-Research
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. Significant-Gravitas/AutoGPT · recommended 1×
  4. yoheinakajima/babyagi · recommended 1×
  5. TransformerOptimus/SuperAGI · recommended 1×
  • CATEGORY QUERY
    What are open-source, cost-effective alternatives for fully automated deep research assistants?
    you: not recommended
    AI recommended (in order):
    1. AutoGPT (Significant-Gravitas/AutoGPT)
    2. BabyAGI (yoheinakajima/babyagi)
    3. SuperAGI (TransformerOptimus/SuperAGI)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Llama 3
    7. Mistral
    8. Falcon

    AI recommended 8 alternatives but never named HKUDS/Auto-Deep-Research. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an AI agent framework supporting various LLMs with flexible function-calling capabilities.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. CrewAI (joaomdmoura/crewAI)
    4. AutoGen (microsoft/autogen)
    5. Haystack (deepset-ai/haystack)
    6. Marvin (prefect-ai/marvin)

    AI recommended 6 alternatives but never named HKUDS/Auto-Deep-Research. 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 HKUDS/Auto-Deep-Research?
    pass
    AI named HKUDS/Auto-Deep-Research explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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HKUDS/Auto-Deep-Research — 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