RRepoGEO

REPOGEO REPORT · LITE

cortexkit/magic-context

Default branch master · commit 0925b8f7 · scanned 6/17/2026, 12:31:50 AM

GitHub: 949 stars · 50 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 cortexkit/magic-context, 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 Magic Context's specialized role in the README's opening paragraph.

    Why:

    CURRENT
    <p align="center"> <strong>Unbounded context. Memory that manages itself. One session, for life.</strong><br> The hippocampus for coding agents, part of CortexKit. </p>
    COPY-PASTE FIX
    Add the following sentence to the opening paragraph, after 'The hippocampus for coding agents, part of CortexKit.': 'It is a specialized Python library designed to provide self-managing, unbounded long-term memory for AI agents, complementing frameworks like LangChain and LlamaIndex.'
  • mediumcomparison#2
    Add a 'Comparison with Alternatives' section to README.

    Why:

    COPY-PASTE FIX
    Add a new top-level section to the README titled 'Comparison with Alternatives'. In this section, explicitly compare Magic Context with tools like LangChain, LlamaIndex, Pinecone, Weaviate, and Qdrant, explaining how Magic Context's specialized focus on self-managing, unbounded context for agents differs from or integrates with these broader solutions.
  • lowtopics#3
    Add more specific topics to clarify library type and core function.

    Why:

    CURRENT
    agent-framework, agent-memory, ai, ai-agents, ai-context, ai-memory, cache-aware, context-engineering, context-management, langchain, memory, opencode, pi-coding-agent
    COPY-PASTE FIX
    agent-framework, agent-memory, ai, ai-agents, ai-context, ai-memory, cache-aware, context-engineering, context-management, langchain, memory, opencode, pi-coding-agent, python-library, agent-memory-library, long-term-memory

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 cortexkit/magic-context
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 · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Weaviate · recommended 1×
  5. Qdrant · recommended 1×
  • CATEGORY QUERY
    What are good libraries for managing persistent, self-organizing memory in AI agent applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Weaviate
    4. Pinecone
    5. Qdrant
    6. Redis
    7. Faiss

    AI recommended 7 alternatives but never named cortexkit/magic-context. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement unbounded, long-term context management for AI agents in complex frameworks?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Pinecone
    3. Weaviate (weaviate/weaviate)
    4. ChromaDB (chromadb/chroma)
    5. LlamaIndex (run-llama/llama_index)
    6. Faiss (facebookresearch/faiss)
    7. Annoy (spotify/annoy)
    8. PostgreSQL
    9. pgvector (pgvector/pgvector)
    10. MongoDB
    11. Redis (redis/redis)
    12. Redis Stack
    13. RedisJSON (RedisLabs/RedisJSON)
    14. RediSearch (RedisLabs/RediSearch)
    15. Redis Vector Search
    16. Haystack (deepset-ai/haystack)
    17. Microsoft Semantic Kernel (microsoft/semantic-kernel)

    AI recommended 17 alternatives but never named cortexkit/magic-context. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 cortexkit/magic-context?
    pass
    AI named cortexkit/magic-context explicitly

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

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

    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
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