RRepoGEO

REPOGEO REPORT · LITE

SciSharp/BotSharp

Default branch master · commit b52cbf57 · scanned 5/22/2026, 1:13:05 PM

GitHub: 3,058 stars · 634 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
62 /100
Needs work
Category recall
1 / 2
Avg rank #5.0 when recommended
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 SciSharp/BotSharp, 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 multi-agent AI system positioning in README

    Why:

    CURRENT
    The Open Source AI Agent Application Framework
## Connect LLMs to your existing application focused on your business
    COPY-PASTE FIX
    The Open Source AI Agent Application Framework
## Build and Orchestrate Multi-Agent AI Systems in .NET
Connect LLMs to your existing application focused on your business, enabling intelligent agents to collaborate on complex workflows.
  • mediumtopics#2
    Expand topics with LLM and agent orchestration terms

    Why:

    CURRENT
    ai-agent, chatbot, multi-agent
    COPY-PASTE FIX
    ai-agent, chatbot, multi-agent, llm, agent-orchestration, .net-ai
  • lowreadme#3
    Add a 'Key Features' or 'Use Cases' section highlighting multi-agent capabilities

    Why:

    COPY-PASTE FIX
    ## Key Features
*   **Multi-Agent Orchestration:** Design and manage complex workflows where multiple specialized AI agents collaborate to achieve business goals.
*   **LLM Integration:** Seamlessly connect with various Large Language Models to power agent intelligence.
*   **Extensible .NET Framework:** Build custom agents and integrate with existing .NET applications.

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
1 / 2
50% of queries surface SciSharp/BotSharp
Avg rank
#5.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
microsoft/botframework-sdk
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/botframework-sdk · recommended 1×
  2. Azure Cognitive Services · recommended 1×
  3. LUIS · recommended 1×
  4. QnA Maker · recommended 1×
  5. nunit/nunit · recommended 1×
  • CATEGORY QUERY
    Looking for a .NET framework to develop conversational AI agents with natural language understanding.
    you: #5
    AI recommended (in order):
    1. Microsoft Bot Framework (microsoft/botframework-sdk)
    2. Azure Cognitive Services
    3. LUIS
    4. QnA Maker
    5. BotSharp (BotSharp/BotSharp) ← you
    6. NUnit (nunit/nunit)
    7. xUnit (xunit/xunit)
    8. Moq (moq/moq4)
    9. ASP.NET Core (dotnet/aspnetcore)
    Show full AI answer
  • CATEGORY QUERY
    How to implement a multi-agent AI system in C# for business process automation?
    you: not recommended
    AI recommended (in order):
    1. Akka.NET
    2. Microsoft Orleans
    3. TPL Dataflow (Task Parallel Library Dataflow)
    4. Reactive Extensions (Rx.NET)

    AI recommended 4 alternatives but never named SciSharp/BotSharp. 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 SciSharp/BotSharp?
    pass
    AI named SciSharp/BotSharp explicitly

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

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

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

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SciSharp/BotSharp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite