RRepoGEO

REPOGEO REPORT · LITE

opencog/atomspace

Default branch master · commit c8d633bf · scanned 6/10/2026, 11:47:51 AM

GitHub: 974 stars · 254 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 opencog/atomspace, 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 emphasize AGI focus

    Why:

    CURRENT
    The OpenCog AtomSpace is an in-RAM knowledge representation (KR) database with an associated query engine and graph-re-writing system. It is a kind of in-RAM generalized hypergraph (metagraph) database.
    COPY-PASTE FIX
    The OpenCog AtomSpace is the central in-RAM knowledge representation (KR) database and hypergraph rewriting system specifically designed as a platform for building Artificial General Intelligence (AGI) systems. It provides a unique generalized hypergraph (metagraph) database with advanced features for cognitive architectures.
  • mediumreadme#2
    Add a 'Comparison to other Graph Databases' section

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison to other Graph Databases' or 'Why AtomSpace is Different' that explicitly contrasts AtomSpace's AGI-centric features (e.g., cognitive metadata, truth values, attention values, hypergraph capabilities beyond triples) with general-purpose graph databases like Neo4j, TypeDB, AllegroGraph, Stardog, RDFox, HyperGraphDB, ArangoDB, and JanusGraph.
  • lowlicense#3
    Clarify license details in README

    Why:

    COPY-PASTE FIX
    Add a sentence or small section to the README, e.g., 'The AtomSpace project is licensed under [specify actual license(s) from the LICENSE file, e.g., a custom OpenCog license or a combination of licenses]. Please refer to the `LICENSE` file for full 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 opencog/atomspace
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TypeDB
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TypeDB · recommended 2×
  2. Neo4j · recommended 2×
  3. AllegroGraph · recommended 1×
  4. Stardog · recommended 1×
  5. RDFox · recommended 1×
  • CATEGORY QUERY
    What in-memory graph database systems offer advanced knowledge representation and graph rewriting capabilities?
    you: not recommended
    AI recommended (in order):
    1. AllegroGraph
    2. Stardog
    3. TypeDB
    4. Neo4j
    5. RDFox
    6. AnzoGraph DB

    AI recommended 6 alternatives but never named opencog/atomspace. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a hypergraph database for complex knowledge representation and advanced AI system development.
    you: not recommended
    AI recommended (in order):
    1. HyperGraphDB
    2. TypeDB
    3. Neo4j
    4. ArangoDB
    5. JanusGraph

    AI recommended 5 alternatives but never named opencog/atomspace. 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 opencog/atomspace?
    pass
    AI named opencog/atomspace explicitly

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

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

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

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opencog/atomspace — 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