RRepoGEO

REPOGEO REPORT · LITE

semantica-agi/semantica

Default branch main · commit 073e8df7 · scanned 5/26/2026, 5:06:35 PM

GitHub: 1,173 stars · 182 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 semantica-agi/semantica, 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 README opening to clarify agent-centric value

    Why:

    CURRENT
    The Accountability and Context Layer for AI · Context Graphs · Decision Intelligence · Full Provenance
    COPY-PASTE FIX
    Semantica: The Accountability and Context Layer for AI Agents · Context Graphs · Decision Intelligence · Full Provenance for Explainable AI Systems
  • mediumtopics#2
    Add specific topics for accountability and explainability

    Why:

    CURRENT
    agent-memory, agentic-ai, ai-agents, ai-infrastructure, context-graph, context-management, data-infrastructure, developer-tools, graph-analytics, graph-modeling, graphrag, knowledge-engineering, knowledge-graphs, ontology-engineering, python-library, rag, schema-design, semantic-layer, semantic-web
    COPY-PASTE FIX
    agent-memory, agentic-ai, ai-agents, ai-accountability, ai-provenance, ai-infrastructure, context-graph, context-management, data-infrastructure, decision-intelligence, developer-tools, explainable-ai, graph-analytics, graph-modeling, graphrag, knowledge-engineering, knowledge-graphs, neuro-symbolic-ai, ontology-engineering, python-library, rag, schema-design, semantic-layer, semantic-web
  • lowreadme#3
    Integrate AGI/neuro-symbolic context into README problem statement

    Why:

    CURRENT
    Most AI agents act without a trail. Semantica adds the layer your stack is missing: structured context graphs, auditable decision records, and full provenance from every output back to its source — so your AI isn't just powerful, it's accountable.
    COPY-PASTE FIX
    Most AI agents act without a trail. Semantica adds the layer your stack is missing: structured context graphs, auditable decision records, and full provenance from every output back to its source — enabling the robust cognitive architectures needed for explainable AGI through neuro-symbolic integration. Your AI isn't just powerful, it's accountable.

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 semantica-agi/semantica
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
open-telemetry/opentelemetry-specification
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. open-telemetry/opentelemetry-specification · recommended 1×
  2. mlflow/mlflow · recommended 1×
  3. apache/atlas · recommended 1×
  4. pachyderm/pachyderm · recommended 1×
  5. wandb/wandb · recommended 1×
  • CATEGORY QUERY
    How to add an auditable context layer and full provenance to my AI agent's decisions?
    you: not recommended
    AI recommended (in order):
    1. OpenTelemetry (open-telemetry/opentelemetry-specification)
    2. MLflow (mlflow/mlflow)
    3. Apache Atlas (apache/atlas)
    4. Pachyderm (pachyderm/pachyderm)
    5. Weights & Biases (wandb/wandb)
    6. DVC (iterative/dvc)

    AI recommended 6 alternatives but never named semantica-agi/semantica. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python framework helps build explainable AI systems using knowledge graphs for semantic retrieval?
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. LlamaIndex
    3. LangChain
    4. PyKEEN
    5. RDFLib
    6. Gensim

    AI recommended 6 alternatives but never named semantica-agi/semantica. 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 semantica-agi/semantica?
    pass
    AI named semantica-agi/semantica explicitly

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

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

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

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semantica-agi/semantica — 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