RRepoGEO

REPOGEO REPORT · LITE

memodb-io/Acontext

Default branch main · commit 259d73bf · scanned 5/25/2026, 5:12:03 PM

GitHub: 3,448 stars · 317 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 memodb-io/Acontext, 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
  • highabout#1
    Clarify the repository's 'About' description to prevent miscategorization

    Why:

    CURRENT
    Agent Skills as a Memory Layer
    COPY-PASTE FIX
    An open-source skill memory layer for AI agents, enabling them to learn, reuse, and share skills as explicit, editable files across LLMs and frameworks.
  • highreadme#2
    Strengthen README's opening to emphasize unique agent memory approach

    Why:

    CURRENT
    Acontext is an open-source skill memory layer for AI agents. It **automatically** captures learnings from agent runs and stores them as **agent skill files** — files you can read, edit, and share across agents, LLMs, and frameworks.
    COPY-PASTE FIX
    Acontext is an open-source skill memory layer for AI agents, designed to make agent learning transparent, explicit, and shareable. Unlike opaque memory systems or generic context propagation, Acontext automatically captures learnings as **agent skill files** — explicit, editable, and shareable files that agents, LLMs, and frameworks can reuse to learn from mistakes and reuse what worked.
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Acontext vs. Alternatives (e.g., LangChain Memory, LlamaIndex, OpenAI Assistants API)
While many frameworks offer memory solutions for AI agents, Acontext takes a unique approach by treating agent memory as explicit, editable 'skill files'. This contrasts with systems that rely on opaque vector stores or internal state, allowing developers to directly inspect, debug, and manage an agent's learned capabilities. Acontext focuses on true skill reuse and transparency, enabling agents to learn from mistakes and share knowledge across different LLMs and frameworks without context pollution.

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 memodb-io/Acontext
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. OpenAI Assistants API · recommended 1×
  5. RLlib · recommended 1×
  • CATEGORY QUERY
    How can I build AI agents that learn from past interactions and reuse skills?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Assistants API
    5. RLlib
    6. BabyAGI / Auto-GPT

    AI recommended 6 alternatives but never named memodb-io/Acontext. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help manage agent skills and prevent memory from polluting LLM context?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Function Calling / Tool Use
    4. Semantic Kernel
    5. Haystack

    AI recommended 5 alternatives but never named memodb-io/Acontext. 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 memodb-io/Acontext?
    pass
    AI named memodb-io/Acontext explicitly

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

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

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

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memodb-io/Acontext — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite