RRepoGEO

REPOGEO REPORT · LITE

cocoindex-io/cocoindex

Default branch main · commit d001d42b · scanned 6/17/2026, 11:16:56 PM

GitHub: 10,372 stars · 807 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 cocoindex-io/cocoindex, 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 H1/opening to clearly state core purpose for AI agents

    Why:

    CURRENT
    Your agents deserve *fresh context.*
    COPY-PASTE FIX
    CocoIndex is an open-source **incremental indexing engine** designed to provide **fresh, real-time context and data to long-horizon AI agents and RAG applications.**
  • mediumcomparison#2
    Add a 'Why CocoIndex?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why CocoIndex? 
 CocoIndex stands apart from traditional vector databases like Pinecone and Weaviate by offering a complete incremental indexing engine, not just vector storage. It's built specifically for the dynamic needs of long-horizon AI agents, providing real-time context updates and transformations that RAG frameworks like LlamaIndex or LangChain would otherwise require manual orchestration for.
  • lowtopics#3
    Refine topics for greater specificity in AI agent context

    Why:

    CURRENT
    agentic-data-framework, ai, ai-agents, change-data-capture, codebase-intelligence, context-engineering, data-engineering, data-indexing, data-processing, etl, help-wanted, indexing, knowledge-graph, llm, long-horizon-agent, python, rag, real-time, rust, semantic-search
    COPY-PASTE FIX
    agentic-data-framework, ai, ai-agents, change-data-capture, codebase-intelligence, context-engineering, data-indexing, incremental-indexing, knowledge-graph, llm, long-horizon-agent, python, rag, real-time, rust, semantic-search, agent-context-management, real-time-data-pipelines

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 cocoindex-io/cocoindex
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. Weaviate · recommended 2×
  3. LlamaIndex · recommended 1×
  4. LangChain · recommended 1×
  5. Neo4j · recommended 1×
  • CATEGORY QUERY
    How to efficiently provide up-to-date context and data to long-horizon AI agents?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. LlamaIndex
    4. LangChain
    5. Neo4j
    6. Redis Stack
    7. OpenSearch

    AI recommended 7 alternatives but never named cocoindex-io/cocoindex. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for real-time incremental data indexing for RAG and AI applications?
    you: not recommended
    AI recommended (in order):
    1. Elasticsearch
    2. Apache Solr
    3. Pinecone
    4. Weaviate
    5. Qdrant
    6. Milvus
    7. Redis (with Redis Stack/RediSearch)

    AI recommended 7 alternatives but never named cocoindex-io/cocoindex. 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 cocoindex-io/cocoindex?
    pass
    AI named cocoindex-io/cocoindex explicitly

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

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

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

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cocoindex-io/cocoindex — 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