RRepoGEO

REPOGEO REPORT · LITE

daveebbelaar/langchain-experiments

Default branch main · commit 7c2f86e1 · scanned 6/28/2026, 10:27:58 PM

GitHub: 1,139 stars · 639 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
33 /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
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 daveebbelaar/langchain-experiments, 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 H1 to clarify it's a RAG experimentation hub

    Why:

    CURRENT
    # LangChain Experiments

This repository focuses on experimenting with the LangChain library for building powerful applications with large language models (LLMs).
    COPY-PASTE FIX
    # LangChain RAG Experiments: A Comprehensive Exploration

This repository offers a systematic collection of experiments and examples focused on Retrieval Augmented Generation (RAG) using the LangChain framework. It explores a wide array of vector databases, LLM providers, and LangChain's integration packages to build powerful applications with large language models (LLMs).
  • mediumtopics#2
    Add more specific topics to clarify content and scope

    Why:

    CURRENT
    ai, langchain, langchain-python, python, slack-bot
    COPY-PASTE FIX
    ai, langchain, langchain-python, python, slack-bot, rag, llm-applications, llm-experiments, vector-databases
  • lowreadme#3
    Add a 'What this repo is NOT' section to the README

    Why:

    COPY-PASTE FIX
    ## What this repository is NOT

This repository is not a standalone LLM framework or library. It is an experimental playground and collection of examples demonstrating how to leverage existing frameworks like LangChain to build advanced LLM applications, particularly focusing on Retrieval Augmented Generation (RAG). It is not intended for direct production use without significant adaptation and hardening.

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 daveebbelaar/langchain-experiments
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. Haystack · recommended 2×
  3. OpenAI Python Library · recommended 2×
  4. Hugging Face Transformers · recommended 2×
  5. LlamaIndex · recommended 1×
  • CATEGORY QUERY
    What are the best Python frameworks for building large language model applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Python Library
    5. Hugging Face Transformers
    6. FastAPI

    AI recommended 6 alternatives but never named daveebbelaar/langchain-experiments. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Comparing Python libraries for developing advanced conversational AI applications?
    you: not recommended
    AI recommended (in order):
    1. Rasa Open Source
    2. Haystack
    3. LangChain
    4. DeepPavlov
    5. OpenAI Python Library
    6. Hugging Face Transformers

    AI recommended 6 alternatives but never named daveebbelaar/langchain-experiments. 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 daveebbelaar/langchain-experiments?
    pass
    AI named daveebbelaar/langchain-experiments explicitly

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

  • If a team adopts daveebbelaar/langchain-experiments in production, what risks or prerequisites should they evaluate first?
    pass
    AI named daveebbelaar/langchain-experiments 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 daveebbelaar/langchain-experiments solve, and who is the primary audience?
    pass
    AI did not name daveebbelaar/langchain-experiments — 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?

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daveebbelaar/langchain-experiments — 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