RRepoGEO

REPOGEO REPORT · LITE

agentscope-ai/agentscope-runtime

Default branch main · commit 723f61e8 · scanned 5/30/2026, 10:52:40 PM

GitHub: 804 stars · 156 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 agentscope-ai/agentscope-runtime, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README to clarify its reference status and value

    Why:

    CURRENT
    The current README starts with an "Archive Notice" followed by a full project description.
    COPY-PASTE FIX
    Replace the current 'Archive Notice' and subsequent project description with a concise statement: 'This repository serves as a historical reference for AgentScope 1.0, showcasing its production-grade runtime features like secure tool sandboxing and Agent-as-a-Service APIs. For active development, new features, and community support, please migrate to AgentScope 2.0 at https://github.com/agentscope-ai/agentscope.'
  • mediumreadme#2
    Add a dedicated 'Reference Value' or 'Migration Guide' section to the README

    Why:

    COPY-PASTE FIX
    Add a new H2 section to the README, for example: `## AgentScope Runtime: A Historical Reference` followed by text explaining which specific features (e.g., secure tool sandboxing, Agent-as-a-Service APIs, scalable deployment) were part of AgentScope 1.0 and how they relate to AgentScope 2.0, guiding users interested in the historical architecture or specific feature implementations.

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 agentscope-ai/agentscope-runtime
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kubernetes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kubernetes · recommended 1×
  2. https://github.com/kubeflow/kubeflow · recommended 1×
  3. AWS SageMaker · recommended 1×
  4. Google Cloud Vertex AI · recommended 1×
  5. Azure Machine Learning · recommended 1×
  • CATEGORY QUERY
    How can I deploy and manage AI agent applications scalably in a production environment?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. Kubeflow (https://github.com/kubeflow/kubeflow)
    3. AWS SageMaker
    4. Google Cloud Vertex AI
    5. Azure Machine Learning
    6. Hugging Face Inference Endpoints
    7. Ray Serve (https://github.com/ray-project/ray)
    8. MLflow (https://github.com/mlflow/mlflow)
    9. Docker
    10. FastAPI (https://github.com/tiangolo/fastapi)

    AI recommended 10 alternatives but never named agentscope-ai/agentscope-runtime. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a runtime for AI agents offering secure tool execution and full-stack observability.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. LlamaIndex
    4. LlamaCloud
    5. LlamaParse
    6. OpenAI Assistants API
    7. Microsoft Semantic Kernel
    8. Azure Monitor
    9. Application Insights
    10. Haystack
    11. Grafana
    12. Prometheus
    13. ELK Stack
    14. Elasticsearch
    15. Logstash
    16. Kibana
    17. FastAPI
    18. Flask
    19. Express
    20. NestJS
    21. OpenTelemetry
    22. Jaeger
    23. Datadog
    24. New Relic
    25. Honeycomb
    26. Grafana Tempo

    AI recommended 26 alternatives but never named agentscope-ai/agentscope-runtime. 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 agentscope-ai/agentscope-runtime?
    pass
    AI named agentscope-ai/agentscope-runtime explicitly

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

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