RRepoGEO

REPOGEO REPORT · LITE

trustgraph-ai/trustgraph

Default branch master · commit 36eadbda · scanned 5/27/2026, 2:07:21 PM

GitHub: 2,115 stars · 243 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 trustgraph-ai/trustgraph, 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 'TrustGraph' name to avoid misinterpretation as 'decentralized trust'

    Why:

    CURRENT
    TrustGraph is an agent runtime platform built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control.
    COPY-PASTE FIX
    TrustGraph is an agent runtime platform (not a decentralized trust or reputation protocol) built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control.
  • mediumreadme#2
    Emphasize 'explainable AI agents' and 'multi-modal context' in README intro

    Why:

    CURRENT
    TrustGraph is an agent runtime platform built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control. The platform is the full stack for agentic systems: context graphs, memory, retrieval, orchestration, and inference for precision-critical agent workloads.
    COPY-PASTE FIX
    TrustGraph is an agent runtime platform (not a decentralized trust or reputation protocol) built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control. It enables explainable AI agents by managing multi-modal context and memory with a graph database. The platform is the full stack for agentic systems: context graphs, memory, retrieval, orchestration, and inference for precision-critical agent workloads.
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

TrustGraph differentiates itself from general-purpose graph databases (like Neo4j, Memgraph, ArangoDB) by providing a full-stack agent runtime specifically designed for grounding AI agents in verifiable, multi-modal context graphs, rather than just data storage. Unlike agent frameworks (like LangChain, LlamaIndex) that focus on orchestration, TrustGraph integrates the underlying context graph engine, multi-modal database, and RAG pipelines for precision-critical agent workloads, offering sovereign control and explainable outputs.

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 trustgraph-ai/trustgraph
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. LangChain · recommended 1×
  3. TerminusDB · recommended 1×
  4. LlamaIndex · recommended 1×
  5. Apache Jena · recommended 1×
  • CATEGORY QUERY
    How to build explainable AI agents using a knowledge graph for context?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Neo4j
    3. TerminusDB
    4. LlamaIndex
    5. Apache Jena
    6. Stardog
    7. Pydantic
    8. NetworkX
    9. OpenAI's GPT models
    10. Anthropic's Claude
    11. Llama 3
    12. Hugging Face Transformers
    13. PyTorch Geometric (PyG)
    14. Deep Graph Library (DGL)
    15. Protégé
    16. Drools

    AI recommended 16 alternatives but never named trustgraph-ai/trustgraph. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What platform manages agent memory and multi-modal context with a graph database?
    you: not recommended
    AI recommended (in order):
    1. Memgraph
    2. Neo4j
    3. ArangoDB
    4. TigerGraph
    5. Amazon Neptune
    6. Grakn (now Vaticle's TypeDB)

    AI recommended 6 alternatives but never named trustgraph-ai/trustgraph. 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 trustgraph-ai/trustgraph?
    pass
    AI named trustgraph-ai/trustgraph explicitly

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

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