RRepoGEO

REPOGEO REPORT · LITE

philippgille/chromem-go

Default branch main · commit fbeda8ab · scanned 6/10/2026, 8:32:05 AM

GitHub: 972 stars · 66 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 philippgille/chromem-go, 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
    Clarify README's opening to explicitly disassociate from Chrome browser

    Why:

    CURRENT
    Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. In-memory with optional persistence.
    COPY-PASTE FIX
    An embeddable vector database for Go with a Chroma-like interface and zero third-party dependencies. It is an in-memory database with optional persistence, and is not related to the Google Chrome browser or its DevTools Protocol.
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://pkg.go.dev/github.com/philippgille/chromem-go
  • lowtopics#3
    Prioritize key vector database and RAG topics

    Why:

    CURRENT
    chroma, chromadb, cosine-similarity, embedded, embeddings, go, golang, in-memory, llm, llms, nearest-neighbor, rag, retrieval-augmented-generation, vector-database, vector-search
    COPY-PASTE FIX
    vector-database, vector-search, rag, retrieval-augmented-generation, llm, llms, embeddings, embedded, in-memory, go, golang, chroma, chromadb, cosine-similarity, nearest-neighbor

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 philippgille/chromem-go
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hnswlib
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hnswlib · recommended 1×
  2. hnswgo · recommended 1×
  3. hnsw · recommended 1×
  4. Faiss · recommended 1×
  5. go-faiss · recommended 1×
  • CATEGORY QUERY
    How to add in-memory vector search capabilities to my Go application without external services?
    you: not recommended
    AI recommended (in order):
    1. Hnswlib
    2. hnswgo
    3. hnsw
    4. Faiss
    5. go-faiss
    6. gorse/vector
    7. github.com/a-h/ann (github.com/a-h/ann)
    8. github.com/ekzhu/go-kad (github.com/ekzhu/go-kad)
    9. Custom Implementation

    AI recommended 9 alternatives but never named philippgille/chromem-go. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a lightweight Go library to perform RAG with embeddings for my LLM applications.
    you: not recommended
    AI recommended (in order):
    1. Go-LLM (github.com/go-llm/go-llm)
    2. LlamaIndex Go (github.com/run-llama/llama_index/tree/main/go)
    3. Go-Torch (github.com/gorgonia/gorgonia)
    4. Pinecone Go Client (github.com/pinecone-io/go-pinecone)
    5. Weaviate Go Client (github.com/weaviate/weaviate-go-client)
    6. Qdrant Go Client (github.com/qdrant/qdrant-go)

    AI recommended 6 alternatives but never named philippgille/chromem-go. 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 philippgille/chromem-go?
    pass
    AI named philippgille/chromem-go explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of philippgille/chromem-go. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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philippgille/chromem-go — 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
philippgille/chromem-go — RepoGEO report