RRepoGEO

REPOGEO REPORT · LITE

CommandCodeAI/BaseAI

Default branch main · commit 17c75fa4 · scanned 7/1/2026, 6:47:57 AM

GitHub: 1,259 stars · 108 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
40 /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
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 CommandCodeAI/BaseAI, 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 repository description to reflect archived status

    Why:

    CURRENT
    BaseAI — The Web AI Framework. The easiest way to build serverless autonomous AI agents with memory. Start building local-first, agentic pipes, tools, and memory. Deploy serverless with one command.
    COPY-PASTE FIX
    BaseAI is archived. It served as a foundational example for building serverless autonomous AI agents. For active development, please see Langbase AI Primitives.
  • mediumreadme#2
    Add a clear statement about the project's license(s) to the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Insert specific license name(s) here, e.g., 'a custom license based on Apache 2.0 and MIT principles']. Please refer to the LICENSE file for full details.
  • lowtopics#3
    Refine repository topics to align with its archived status and role as an example

    Why:

    CURRENT
    ai, anthropic, artificial-intelligence, baseai, cohere, firewor, gemini, grok, groq, langbase, mistral, openai, perplexity, togetherai, xai
    COPY-PASTE FIX
    ai, artificial-intelligence, ai-agents, serverless-ai, memory-agents, ai-framework-example, langbase-primitives, archived-project

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 CommandCodeAI/BaseAI
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. Microsoft Semantic Kernel · recommended 1×
  5. AgentVerse · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for building serverless autonomous AI agents with memory?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft Semantic Kernel
    4. Haystack
    5. AgentVerse

    AI recommended 5 alternatives but never named CommandCodeAI/BaseAI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I develop local-first AI agents with integrated tools and RAG capabilities?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Hugging Face
    5. Llama 3
    6. Mistral
    7. Gemma
    8. transformers
    9. bitsandbytes
    10. ollama
    11. ChromaDB
    12. FAISS
    13. LanceDB
    14. FastAPI
    15. Flask

    AI recommended 15 alternatives but never named CommandCodeAI/BaseAI. 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 CommandCodeAI/BaseAI?
    pass
    AI named CommandCodeAI/BaseAI explicitly

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

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

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

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CommandCodeAI/BaseAI — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite