RRepoGEO

REPOGEO REPORT · LITE

mudler/LocalRecall

Default branch main · commit 9a3b3321 · scanned 6/14/2026, 12:27:26 PM

GitHub: 833 stars · 101 forks

AI VISIBILITY SCORE
22 /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
1 / 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 mudler/LocalRecall, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening sentence to emphasize AI agent memory

    Why:

    CURRENT
    A lightweight, no-frills RESTful API designed for managing knowledge bases and files stored in vector databases—**no GPU, internet, or cloud services required**! LocalRecall provides a simple and generic abstraction layer to handle knowledge retrieval, ideal for AI agents and chatbots to manage both long-term and short-term memory seamlessly.
    COPY-PASTE FIX
    LocalRecall is a lightweight, no-frills RESTful API providing a 100% local memory layer and knowledge base specifically designed for AI agents and chatbots. It manages knowledge retrieval and files stored in vector databases—**no GPU, internet, or cloud services required**—enabling seamless long-term and short-term memory management.
  • mediumhomepage#2
    Add the project homepage URL to the About section

    Why:

    COPY-PASTE FIX
    https://github.com/mudler/LocalRecall

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 mudler/LocalRecall
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/faiss
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/faiss · recommended 2×
  2. chroma-core/chroma · recommended 1×
  3. UKPLab/sentence-transformers · recommended 1×
  4. facebookresearch/fastText · recommended 1×
  5. duckdb/duckdb · recommended 1×
  • CATEGORY QUERY
    How to build an AI agent knowledge base that runs entirely offline without cloud dependencies?
    you: not recommended
    AI recommended (in order):
    1. ChromaDB (chroma-core/chroma)
    2. Sentence Transformers (UKPLab/sentence-transformers)
    3. FastText (facebookresearch/fastText)
    4. FAISS (facebookresearch/faiss)
    5. DuckDB (duckdb/duckdb)
    6. SQLite
    7. Hugging Face `transformers` library (huggingface/transformers)
    8. Llama.cpp (ggerganov/llama.cpp)
    9. Ollama (ollama/ollama)
    10. NLTK (nltk/nltk)
    11. spaCy (explosion/spaCy)

    AI recommended 11 alternatives but never named mudler/LocalRecall. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are options for a local vector database and memory layer for AI agents?
    you: not recommended
    AI recommended (in order):
    1. Chroma (chromadb/chroma)
    2. FAISS (facebookresearch/faiss)
    3. LanceDB (lancedb/lancedb)
    4. Qdrant (qdrant/qdrant)
    5. Milvus Lite (milvus-io/milvus)
    6. Weaviate (weaviate/weaviate)

    AI recommended 6 alternatives but never named mudler/LocalRecall. 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 mudler/LocalRecall?
    pass
    AI did not name mudler/LocalRecall — likely talking about a different project

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

  • If a team adopts mudler/LocalRecall in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mudler/LocalRecall 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 mudler/LocalRecall solve, and who is the primary audience?
    pass
    AI did not name mudler/LocalRecall — likely talking about a different project

    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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mudler/LocalRecall — 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