RRepoGEO

REPOGEO REPORT · LITE

emarco177/langgraph-course

Default branch main · commit 03f7369e · scanned 6/12/2026, 11:03:56 AM

GitHub: 597 stars · 282 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 emarco177/langgraph-course, 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 the README H1 to specify it's a course

    Why:

    CURRENT
    # LangGraph🦜🕸️ – Develop LLM-Powered AI Agents
    COPY-PASTE FIX
    # LangGraph🦜🕸️ Course – Develop LLM-Powered AI Agents
  • mediumtopics#2
    Add educational topics to improve categorization

    Why:

    CURRENT
    agentic-rag, langchain, langgraph, reflection
    COPY-PASTE FIX
    agentic-rag, langchain, langgraph, reflection, course, tutorial, education, learning
  • lowreadme#3
    Clarify production readiness in the README

    Why:

    COPY-PASTE FIX
    > **Build production-grade AI agents—fast.** This repository is the hands-on companion to my Udemy bestseller. Every branch is a *project*, every commit is a *lesson*. Clone it, code along, and ship your own LangGraph agents.

Note: While the agents and techniques taught are production-grade, this repository is a learning resource and not intended for direct production deployment.

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 emarco177/langgraph-course
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. AutoGen · recommended 1×
  4. Haystack · recommended 1×
  5. DSPy · recommended 1×
  • CATEGORY QUERY
    How to implement robust LLM agents with self-correction, reasoning, and adaptive RAG?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. Haystack
    5. DSPy
    6. CrewAI

    AI recommended 6 alternatives but never named emarco177/langgraph-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for designing conversational AI agents with reflection capabilities?
    you: not recommended
    AI recommended (in order):
    1. Scale AI
    2. Appen
    3. Surge AI
    4. Mixpanel
    5. Amplitude
    6. Google Analytics
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)
    9. OpenAI Function Calling
    10. Hugging Face Transformers (huggingface/transformers)
    11. Pyro (pyro-ppl/pyro)
    12. Stan (mc-stan/stan)
    13. Rasa (RasaHQ/rasa)
    14. Pinecone
    15. Weaviate (weaviate/weaviate)
    16. Chroma (chroma-core/chroma)
    17. Zendesk
    18. Intercom
    19. MLflow (mlflow/mlflow)
    20. Redis (redis/redis)
    21. PostgreSQL
    22. MongoDB
    23. LIME (marcotcr/lime)
    24. SHAP (slundberg/shap)
    25. Weights & Biases (wandb/wandb)
    26. Optimizely
    27. Split.io
    28. GitHub
    29. GitLab

    AI recommended 29 alternatives but never named emarco177/langgraph-course. 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 emarco177/langgraph-course?
    pass
    AI named emarco177/langgraph-course explicitly

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

  • If a team adopts emarco177/langgraph-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named emarco177/langgraph-course 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 emarco177/langgraph-course solve, and who is the primary audience?
    pass
    AI named emarco177/langgraph-course 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
  • Prioritized action items8 vs 3 in Lite