RRepoGEO

REPOGEO REPORT · LITE

tryAGI/LangChain

Default branch main · commit 26bb1d16 · scanned 6/23/2026, 10:32:08 AM

GitHub: 1,049 stars · 139 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 tryAGI/LangChain, 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
    Enhance README's opening statement to explicitly state its role as an LLM orchestration framework

    Why:

    CURRENT
    ⚡ Building applications with LLMs through composability ⚡ C# implementation of LangChain. We try to be as close to the original as possible in terms of abstractions, but are open to new entities.
    COPY-PASTE FIX
    ⚡ LangChain .NET: The C# framework for building and orchestrating LLM applications and agent workflows through composability. We aim for abstractions close to the original LangChain, while remaining open to new entities.
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled '## Comparison to Alternatives' or '## Why LangChain .NET?' that explicitly compares tryAGI/LangChain to Semantic Kernel and LLamaSharp, highlighting its strengths (e.g., adherence to original LangChain abstractions, maximum choice of options, community-driven).
  • lowtopics#3
    Expand GitHub topics to include 'ai-agents' and 'llm-orchestration'

    Why:

    CURRENT
    ai, csharp, dotnet, langchain, langchain-csharp, langchain-dotnet, llm, openai, sdk
    COPY-PASTE FIX
    ai, csharp, dotnet, langchain, langchain-csharp, langchain-dotnet, llm, openai, sdk, ai-agents, llm-orchestration

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 tryAGI/LangChain
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/semantic-kernel
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/semantic-kernel · recommended 2×
  2. SciSharp/LLamaSharp · recommended 2×
  3. Azure/azure-sdk-for-net · recommended 2×
  4. OkGoDoIt/OpenAI-DotNet · recommended 1×
  5. App-vNext/Polly · recommended 1×
  • CATEGORY QUERY
    How to build large language model applications in C# with composable components?
    you: not recommended
    AI recommended (in order):
    1. Semantic Kernel (microsoft/semantic-kernel)
    2. LLamaSharp (SciSharp/LLamaSharp)
    3. OpenAI .NET Library (OkGoDoIt/OpenAI-DotNet)
    4. Azure OpenAI Service SDK (Azure/azure-sdk-for-net)
    5. Polly (App-vNext/Polly)
    6. MediatR (jbogard/MediatR)
    7. ASP.NET Core (dotnet/aspnetcore)

    AI recommended 7 alternatives but never named tryAGI/LangChain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a .NET library to orchestrate LLM interactions and agent workflows.
    you: not recommended
    AI recommended (in order):
    1. Semantic Kernel (microsoft/semantic-kernel)
    2. LLamaSharp (SciSharp/LLamaSharp)
    3. BotSharp (SciSharp/BotSharp)
    4. Azure OpenAI Service SDK for .NET (Azure/azure-sdk-for-net)
    5. OpenAI .NET Library (okgodoit/OpenAI-DotNet)
    6. LangChain.NET (dmitry-s/LangChain.NET)

    AI recommended 6 alternatives but never named tryAGI/LangChain. 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 tryAGI/LangChain?
    pass
    AI named tryAGI/LangChain explicitly

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

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

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

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tryAGI/LangChain — 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