RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/langgraph-example

Default branch main · commit ecf328da · scanned 6/2/2026, 11:07:54 PM

GitHub: 508 stars · 355 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 langchain-ai/langgraph-example, 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
  • highabout#1
    Add a concise 'About' description

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    An example agent for deploying stateful, multi-actor LLM applications to LangGraph Cloud, demonstrating built-in persistence and API scaling.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, for example, using the MIT License or Apache-2.0 License, to clearly state the terms of use.

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 langchain-ai/langgraph-example
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. Redis · recommended 1×
  3. PostgreSQL · recommended 1×
  4. Microsoft Semantic Kernel · recommended 1×
  5. Azure Cosmos DB · recommended 1×
  • CATEGORY QUERY
    How to build stateful multi-actor LLM applications with persistent checkpoints?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Redis
    3. PostgreSQL
    4. Microsoft Semantic Kernel
    5. Azure Cosmos DB
    6. SQL Server
    7. Haystack
    8. Elasticsearch
    9. OpenSearch
    10. Weaviate
    11. Orleans
    12. Akka
    13. Azure Table Storage
    14. MongoDB
    15. Akka Persistence
    16. Cassandra
    17. LevelDB
    18. FastAPI
    19. Flask
    20. RabbitMQ
    21. Kafka
    22. Temporal.io
    23. Cadence

    AI recommended 23 alternatives but never named langchain-ai/langgraph-example. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help deploy LLM agent microservices with built-in API and scaling?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-3.5/GPT-4 API
    2. Azure Functions
    3. AWS Lambda
    4. LangChain (langchain-ai/langchain)
    5. FastAPI (tiangolo/fastapi)
    6. Kubernetes (kubernetes/kubernetes)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Flask (pallets/flask)
    9. Django (django/django)
    10. Google Cloud Run
    11. LlamaIndex (run-llama/llama_index)
    12. Ray Serve (ray-project/ray)
    13. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    14. Azure Container Apps
    15. Gradio (gradio-app/gradio)
    16. Docker (moby/moby)
    17. AWS App Runner
    18. Vercel AI SDK (vercel/ai)
    19. Next.js API Routes (vercel/next.js)

    AI recommended 19 alternatives but never named langchain-ai/langgraph-example. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • 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 langchain-ai/langgraph-example?
    pass
    AI named langchain-ai/langgraph-example explicitly

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

  • If a team adopts langchain-ai/langgraph-example in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/langgraph-example 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 langchain-ai/langgraph-example solve, and who is the primary audience?
    pass
    AI did not name langchain-ai/langgraph-example — likely talking about a different project

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

Embed your GEO score

Drop this badge into the README of langchain-ai/langgraph-example. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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  • Brand-free category queries5 vs 2 in Lite
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