RRepoGEO

REPOGEO REPORT · LITE

agiresearch/A-mem

Default branch main · commit ceffb860 · scanned 5/22/2026, 9:53:34 AM

GitHub: 1,021 stars · 112 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 agiresearch/A-mem, 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 the README to highlight A-MEM's unique category

    Why:

    COPY-PASTE FIX
    Add the following sentence early in the README, for example, after the 'Introduction' section: "Unlike general LLM frameworks or standalone vector databases, A-MEM provides a modular, multi-modal, and cognitively inspired approach to LLM memory, explicitly distinguishing between and managing episodic, semantic, and working memory for advanced agent capabilities."
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the project's GitHub repository URL (e.g., https://github.com/agiresearch/A-mem) or the URL of the associated research paper if available, to the 'Homepage' field in the repository's 'About' section.
  • lowtopics#3
    Expand and refine repository topics for better categorization

    Why:

    CURRENT
    agent, llm, memory
    COPY-PASTE FIX
    agent, llm, memory, agentic-memory, knowledge-graph, zettelkasten, chromadb

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 agiresearch/A-mem
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. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. weaviate/weaviate · recommended 1×
  5. redis/redis · recommended 1×
  • CATEGORY QUERY
    How can I give my LLM agent a more dynamic and organized memory system?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Pinecone
    4. Weaviate (weaviate/weaviate)
    5. Redis (redis/redis)
    6. Faiss (facebookresearch/faiss)

    AI recommended 6 alternatives but never named agiresearch/A-mem. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are sophisticated memory solutions for LLM agents to manage and utilize past interactions?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Chroma
    4. Weaviate
    5. LlamaIndex
    6. Milvus
    7. Qdrant
    8. Elasticsearch
    9. MemGPT
    10. Faiss
    11. Redis
    12. Redis Stack
    13. RedisJSON
    14. RediSearch

    AI recommended 14 alternatives but never named agiresearch/A-mem. 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 agiresearch/A-mem?
    pass
    AI named agiresearch/A-mem explicitly

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

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

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

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agiresearch/A-mem — 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