RRepoGEO

REPOGEO REPORT · LITE

TsinghuaC3I/Awesome-Memory-for-Agents

Default branch main · commit a0024d92 · scanned 6/4/2026, 5:27:58 PM

GitHub: 544 stars · 40 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 TsinghuaC3I/Awesome-Memory-for-Agents, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    awesome-list, paper-collection, memory-for-agents, language-agents, llm-agents, artificial-intelligence, research-papers
  • highreadme#2
    Reposition the README's opening to explicitly state it's an 'awesome list' of papers

    Why:

    CURRENT
    # Awesome-Memory-for-Agents

The paper list is maintained by Hongyi Liu, Yu Fu, Kaiyan Zhang, contributed by Yuxin Zuo, Che Jiang, Guoli Jia, Yuru Wang, Kaikai Zhao, Yuchen Fan, Zhenzhao Yuan, Kai Tian, Weizhi Wang.
    COPY-PASTE FIX
    # Awesome-Memory-for-Agents: A Curated Collection of Papers on Memory for Language Agents

This repository is an awesome list, providing a curated collection of research papers on memory mechanisms for language agents, structured by a core taxonomy. It is maintained by Hongyi Liu, Yu Fu, Kaiyan Zhang, contributed by Yuxin Zuo, Che Jiang, Guoli Jia, Yuru Wang, Kaikai Zhao, Yuchen Fan, Zhenzhao Yuan, Kai Tian, Weizhi Wang.
  • mediumhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/TsinghuaC3I/Awesome-Memory-for-Agents

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 TsinghuaC3I/Awesome-Memory-for-Agents
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. PostgreSQL · recommended 2×
  3. SQLite · recommended 2×
  4. langchain-ai/langchain · recommended 1×
  5. chroma-core/chroma · recommended 1×
  • CATEGORY QUERY
    How can I implement persistent memory for my language model agent?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Chroma (chroma-core/chroma)
    3. Pinecone
    4. Weaviate (weaviate/weaviate)
    5. LlamaIndex (run-llama/llama_index)
    6. FAISS (facebookresearch/faiss)
    7. Milvus (milvus-io/milvus)
    8. Qdrant (qdrant/qdrant)
    9. Redis (redis/redis)
    10. RedisJSON (RedisLabsModules/RedisJSON)
    11. Redis Stack (redis/redis-stack)
    12. PostgreSQL
    13. pgvector (pgvector/pgvector)
    14. MongoDB (mongodb/mongo)
    15. SQLite

    AI recommended 15 alternatives but never named TsinghuaC3I/Awesome-Memory-for-Agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best strategies for managing long-term information in autonomous AI agents?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Milvus
    4. OpenAI's text-embedding-ada-002
    5. Sentence-BERT
    6. Neo4j
    7. Amazon Neptune
    8. Grakn (now TypeDB)
    9. PostgreSQL
    10. pg_embedding
    11. pgvector
    12. SQLite
    13. LangChain's Memory Modules
    14. LlamaIndex's Indexing Structures

    AI recommended 14 alternatives but never named TsinghuaC3I/Awesome-Memory-for-Agents. 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 TsinghuaC3I/Awesome-Memory-for-Agents?
    pass
    AI did not name TsinghuaC3I/Awesome-Memory-for-Agents — 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 TsinghuaC3I/Awesome-Memory-for-Agents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named TsinghuaC3I/Awesome-Memory-for-Agents 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 TsinghuaC3I/Awesome-Memory-for-Agents solve, and who is the primary audience?
    pass
    AI did not name TsinghuaC3I/Awesome-Memory-for-Agents — 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?

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TsinghuaC3I/Awesome-Memory-for-Agents — 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