RRepoGEO

REPOGEO REPORT · LITE

CopilotKit/aimock

Default branch main · commit 27695ee7 · scanned 6/14/2026, 2:16:53 PM

GitHub: 625 stars · 41 forks

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 CopilotKit/aimock, 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 to emphasize AI-specific mocking

    Why:

    CURRENT
    Mock infrastructure for AI application testing — LLM APIs, image generation, image editing, text-to-speech, transcription, audio translation, audio generation, video generation, embeddings, MCP tools, A2A agents, AG-UI event streams, vector databases, search, rerank, and moderation. One package, one port, zero dependencies.
    COPY-PASTE FIX
    aimock is a purpose-built mock server for AI application testing, designed specifically for LLM APIs and other AI services like image generation, vector databases, and search. Unlike generic HTTP mocks, aimock provides AI-specific response handling and seamless compatibility with common AI SDKs, all in one package with zero dependencies.
  • mediumtopics#2
    Refine topics to be more AI-specific and less generic

    Why:

    CURRENT
    ai-testing, aimock, llm, mcp, mock-server, openai
    COPY-PASTE FIX
    ai-testing, aimock, llm, mcp, openai, ai-mocking, llm-testing, vector-db-mocking
  • mediumabout#3
    Refine repository description for clearer AI-specific positioning

    Why:

    CURRENT
    Mock everything your AI app talks to — LLM APIs, MCP, A2A, AG-UI, vector DBs, search. One package, one port, zero dependencies.
    COPY-PASTE FIX
    Purpose-built mock server for AI application testing: simulate LLM APIs, vector DBs, search, and other AI services. Designed for AI SDK compatibility, one package, one port, zero dependencies.

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 CopilotKit/aimock
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
VCR.py
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. VCR.py · recommended 1×
  2. VCR (Ruby) · recommended 1×
  3. go-vcr · recommended 1×
  4. VCR.js · recommended 1×
  5. WireMock · recommended 1×
  • CATEGORY QUERY
    How can I mock LLM APIs and other AI services for robust application testing?
    you: not recommended
    AI recommended (in order):
    1. VCR.py
    2. VCR (Ruby)
    3. go-vcr
    4. VCR.js
    5. WireMock
    6. Mock Service Worker (MSW)
    7. Testcontainers
    8. Pact (Pactflow)
    9. OpenAPI Generator
    10. Swagger Codegen
    11. unittest.mock
    12. Sinon.js
    13. Mockito

    AI recommended 13 alternatives but never named CopilotKit/aimock. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for simulating AI infrastructure like vector DBs during development?
    you: not recommended
    AI recommended (in order):
    1. Chroma (chroma-core/chroma)
    2. LanceDB (lancedb/lancedb)
    3. FAISS (facebookresearch/faiss)
    4. Hnswlib (nmslib/hnswlib)
    5. Pgvector (pgvector/pgvector)
    6. Milvus Lite (milvus-io/milvus)

    AI recommended 6 alternatives but never named CopilotKit/aimock. 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 CopilotKit/aimock?
    pass
    AI named CopilotKit/aimock explicitly

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

  • If a team adopts CopilotKit/aimock in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CopilotKit/aimock 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 CopilotKit/aimock solve, and who is the primary audience?
    pass
    AI named CopilotKit/aimock 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite