RRepoGEO

REPOGEO REPORT · LITE

continuum-llms/chatgpt-memory

Default branch main · commit 51f57a6d · scanned 6/16/2026, 5:38:15 AM

GitHub: 529 stars · 63 forks

AI VISIBILITY SCORE
28 /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
2 / 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 continuum-llms/chatgpt-memory, 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 discontinuation notice in the README

    Why:

    CURRENT
    *Development on this repository has discontinued. Please check out OpenAI's retrieval plugin instead: https://github.com/openai/chatgpt-retrieval-plugin*

# ChatGPT Memory

Allows to scale the ChatGPT API to multiple simultaneous sessions with infinite contextual and adaptive memory powered by GPT and Redis datastore.
    COPY-PASTE FIX
    # ChatGPT Memory

Allows to scale the ChatGPT API to multiple simultaneous sessions with infinite contextual and adaptive memory powered by GPT and Redis datastore. This can be visualized as follows

*Note: Development on this repository has discontinued. Please check out OpenAI's retrieval plugin instead: https://github.com/openai/chatgpt-retrieval-plugin*
  • mediumtopics#2
    Add more specific topics to the repository

    Why:

    CURRENT
    chatgpt, chatgpt-api, memory, redis
    COPY-PASTE FIX
    chatgpt, chatgpt-api, memory, redis, conversational-ai, llm-memory, session-management, context-management, scalability
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

`ChatGPT Memory` is designed as a lightweight, dedicated framework specifically for managing conversational memory in LLM applications, particularly for scaling ChatGPT API usage with persistent contextual memory via Redis.

Unlike broader LLM frameworks such as LangChain or LlamaIndex, which offer extensive features for agent orchestration, tool integration, and diverse data loaders, `ChatGPT Memory` focuses solely on the memory aspect. It provides a streamlined solution for maintaining long-term, adaptive context across multiple chat sessions.

Compared to general-purpose vector databases like Pinecone, Weaviate, Chroma, or Qdrant, `ChatGPT Memory` offers a higher-level abstraction tailored for conversational memory, integrating directly with GPT for contextual understanding and Redis for efficient storage, rather than requiring manual vector operations.

For new projects, consider OpenAI's retrieval plugin as a modern alternative, as development on this repository has discontinued. `ChatGPT Memory` can still serve as a valuable reference for specific implementation patterns of contextual memory with Redis.

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 continuum-llms/chatgpt-memory
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. weaviate/weaviate · recommended 1×
  4. chroma-core/chroma · recommended 1×
  5. qdrant/qdrant · recommended 1×
  • CATEGORY QUERY
    How to manage long-term conversational memory for a large number of AI chat sessions?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Pinecone
    3. Weaviate (weaviate/weaviate)
    4. Chroma (chroma-core/chroma)
    5. Qdrant (qdrant/qdrant)
    6. Redis (redis/redis)
    7. RedisJSON (RedisJSON/RedisJSON)
    8. Redis Stack (redis/redis-stack)
    9. PostgreSQL
    10. pgvector (pgvector/pgvector)
    11. MongoDB
    12. MongoDB Atlas Search
    13. Couchbase
    14. Google Cloud Firestore
    15. AWS DynamoDB

    AI recommended 15 alternatives but never named continuum-llms/chatgpt-memory. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What solutions exist for scaling LLM API usage with persistent contextual memory using Redis?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. redis-py
    4. Pydantic
    5. Pickle
    6. JSON
    7. Faiss
    8. Pinecone
    9. Weaviate
    10. RedisJSON
    11. redis-json-py
    12. RedisGraph

    AI recommended 12 alternatives but never named continuum-llms/chatgpt-memory. 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 continuum-llms/chatgpt-memory?
    pass
    AI named continuum-llms/chatgpt-memory explicitly

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

  • If a team adopts continuum-llms/chatgpt-memory in production, what risks or prerequisites should they evaluate first?
    pass
    AI named continuum-llms/chatgpt-memory 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 continuum-llms/chatgpt-memory solve, and who is the primary audience?
    pass
    AI did not name continuum-llms/chatgpt-memory — 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite