RRepoGEO

REPOGEO REPORT · LITE

HKUDS/Auto-Deep-Research

Default branch main · commit 9d671a33 · scanned 6/19/2026, 11:28:07 AM

GitHub: 1,564 stars · 221 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 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
  • highabout#1
    Update the repository description to clearly state its specialized function

    Why:

    CURRENT
    Your Fully-Automated Personal AI Assistant
    COPY-PASTE FIX
    Your fully-automated, cost-effective AI assistant for deep research, offering an open-source alternative to commercial tools.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects your project's intent.
  • mediumtopics#3
    Refine repository topics to emphasize specialization and improve category recall

    Why:

    CURRENT
    agent, deep-research, llm
    COPY-PASTE FIX
    deep-research, llm, ai-assistant, research-automation, 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
AutoGPT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AutoGPT · recommended 1×
  2. BabyAGI · recommended 1×
  3. LangChain Agents · recommended 1×
  4. SuperAGI · recommended 1×
  5. AgentGPT · recommended 1×
  • CATEGORY QUERY
    Looking for a cost-effective, fully automated AI assistant for deep research with broad LLM compatibility.
    you: not recommended
    AI recommended (in order):
    1. AutoGPT
    2. BabyAGI
    3. LangChain Agents
    4. SuperAGI
    5. AgentGPT
    6. CrewAI

    AI recommended 6 alternatives but never named HKUDS/Auto-Deep-Research. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are open-source alternatives to commercial deep research AI tools offering high performance?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. JAX (google/jax)
    4. Hugging Face Transformers (huggingface/transformers)
    5. MXNet (apache/mxnet)
    6. DeepSpeed (microsoft/DeepSpeed)

    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?

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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