RRepoGEO

REPOGEO REPORT · LITE

kayba-ai/agentic-context-engine

Default branch main · commit a5a2f1fa · scanned 5/29/2026, 11:02:19 AM

GitHub: 2,243 stars · 277 forks

AI VISIBILITY SCORE
27 /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
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 kayba-ai/agentic-context-engine, 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's opening to clearly state ACE is an LLM agent framework/tool.

    Why:

    CURRENT
    AI agents don't learn from experience.** They repeat the same mistakes every session, forget what worked, and ignore what failed. ACE adds a persistent learning loop that makes them better over time.
    COPY-PASTE FIX
    The **Agentic Context Engine (ACE) is an open-source framework** that adds a persistent learning loop to your LLM-based AI agents, enabling them to learn from experience, avoid repeating mistakes, and improve over time.
  • hightopics#2
    Add specific LLM agent framework/tooling topics.

    Why:

    CURRENT
    agent-learning, agent-memory, agents, ai, ai-agents, ai-tools, context-engineering, llm, machine-learning, memory, python
    COPY-PASTE FIX
    agent-learning, agent-memory, agents, ai, ai-agents, ai-tools, context-engineering, llm, machine-learning, memory, python, llm-agents, agent-framework, agent-orchestration, autonomous-agents
  • mediumlicense#3
    Clarify the project's license directly in the README.

    Why:

    COPY-PASTE FIX
    This project is licensed under [Insert specific license name(s) here, e.g., 'the Apache 2.0 License and the MIT License']. See the [LICENSE](LICENSE) file for details.

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 kayba-ai/agentic-context-engine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Deep Q-Networks
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Deep Q-Networks · recommended 1×
  2. Proximal Policy Optimization · recommended 1×
  3. Advantage Actor-Critic · recommended 1×
  4. Differentiable Neural Computers · recommended 1×
  5. Neural Turing Machines · recommended 1×
  • CATEGORY QUERY
    How to make AI agents learn from past interactions and avoid repeating errors?
    you: not recommended
    AI recommended (in order):
    1. Deep Q-Networks
    2. Proximal Policy Optimization
    3. Advantage Actor-Critic
    4. Differentiable Neural Computers
    5. Neural Turing Machines
    6. GPT-3
    7. BERT
    8. Long Short-Term Memory
    9. Gated Recurrent Unit
    10. MyCBR
    11. MAML

    AI recommended 11 alternatives but never named kayba-ai/agentic-context-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for adding persistent memory and self-improvement loops to LLM-based agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. MemGPT
    4. AutoGPT
    5. Weaviate
    6. Pinecone
    7. Redis

    AI recommended 7 alternatives but never named kayba-ai/agentic-context-engine. 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 kayba-ai/agentic-context-engine?
    pass
    AI did not name kayba-ai/agentic-context-engine — 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 kayba-ai/agentic-context-engine in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kayba-ai/agentic-context-engine 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 kayba-ai/agentic-context-engine solve, and who is the primary audience?
    pass
    AI did not name kayba-ai/agentic-context-engine — 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
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