RRepoGEO

REPOGEO REPORT · LITE

msoedov/langcorn

Default branch main · commit f7fde47e · scanned 6/11/2026, 7:21:56 AM

GitHub: 939 stars · 71 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 msoedov/langcorn, 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's opening statement to emphasize LLM specialization

    Why:

    CURRENT
    # Langcorn

LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience.
    COPY-PASTE FIX
    # Langcorn: Automagically Serve LangChain LLM Apps as FastAPI APIs

Langcorn is the **FastAPI-native server for LangChain applications**, designed to **automagically turn your LLM chains and agents into production-ready REST APIs** with minimal boilerplate. Stop building custom FastAPI wrappers for LangChain; Langcorn handles the serving, scaling, and API generation for you.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Comparison to LangServe and other LLM API tools' that outlines Langcorn's unique benefits, such as its FastAPI-native approach and automatic API generation, compared to alternatives like LangServe.
  • lowtopics#3
    Add more specific LLM serving/deployment topics

    Why:

    CURRENT
    api, fastapi, langchain, langchain-python, large-language-models, llm, llmops, openai-api, rest-api, vercel, vercel-serverless-functions
    COPY-PASTE FIX
    api, fastapi, langchain, langchain-python, large-language-models, llm, llmops, openai-api, rest-api, vercel, vercel-serverless-functions, llm-serving, llm-deployment, ai-api

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 msoedov/langcorn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Gunicorn
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Gunicorn · recommended 2×
  2. FastAPI · recommended 1×
  3. Uvicorn · recommended 1×
  4. LangServe · recommended 1×
  5. Flask · recommended 1×
  • CATEGORY QUERY
    How to easily deploy and serve LangChain LLM applications with a REST API?
    you: not recommended
    AI recommended (in order):
    1. FastAPI
    2. Uvicorn
    3. Gunicorn
    4. LangServe
    5. Flask
    6. Gunicorn
    7. Django REST Framework
    8. Modal
    9. Hugging Face Inference Endpoints

    AI recommended 9 alternatives but never named msoedov/langcorn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a scalable framework to expose large language model chains as performant APIs.
    you: not recommended
    AI recommended (in order):
    1. FastAPI (tiangolo/fastapi)
    2. Ray Serve (ray-project/ray)
    3. Flask (pallets/flask)
    4. Gunicorn (benoitc/gunicorn)
    5. Uvicorn (encode/uvicorn)
    6. Django REST Framework (encode/django-rest-framework)
    7. Triton Inference Server (triton-inference-server/server)
    8. KServe (kserve/kserve)

    AI recommended 8 alternatives but never named msoedov/langcorn. 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 msoedov/langcorn?
    pass
    AI named msoedov/langcorn explicitly

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

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

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

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msoedov/langcorn — 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