RRepoGEO

REPOGEO REPORT · LITE

BrandPeng/Langchain1.0-Langgraph1.0-Learning

Default branch main · commit c20eeee8 · scanned 6/11/2026, 12:22:59 AM

GitHub: 521 stars · 101 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
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 BrandPeng/Langchain1.0-Langgraph1.0-Learning, 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
  • highreadme#1
    Clarify README's opening to emphasize 'learning guide' for building agents/RAG

    Why:

    CURRENT
    # 🦜🔗 LangChain 1.0 & LangGraph 1.0 完整学习指南
> 这是一个系统学习 **LangChain 1.0** 和 **LangGraph 1.0** 的实践仓库，涵盖从基础概念到实战项目的完整学习路径。
    COPY-PASTE FIX
    # 🦜🔗 LangChain 1.0 & LangGraph 1.0 完整学习指南：构建Agent与RAG的实践教程
> 这是一个专注于 **LangChain 1.0** 和 **LangGraph 1.0** 的系统学习与实践仓库，旨在通过详细教程和实战项目，指导开发者如何从基础概念到高级应用，有效构建LLM驱动的Agent和RAG系统。
  • lowhomepage#2
    Add repository URL as homepage

    Why:

    COPY-PASTE FIX
    https://github.com/BrandPeng/Langchain1.0-Langgraph1.0-Learning

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 BrandPeng/Langchain1.0-Langgraph1.0-Learning
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. LlamaIndex · recommended 2×
  3. CrewAI · recommended 2×
  4. Haystack · recommended 2×
  5. AutoGPT · recommended 1×
  • CATEGORY QUERY
    How can I effectively build complex autonomous AI agents for LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. CrewAI
    5. Microsoft AutoGen
    6. Haystack
    7. BabyAGI

    AI recommended 7 alternatives but never named BrandPeng/Langchain1.0-Langgraph1.0-Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are suitable frameworks for developing multi-agent systems or advanced RAG pipelines?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGen
    5. CrewAI
    6. DSPy

    AI recommended 6 alternatives but never named BrandPeng/Langchain1.0-Langgraph1.0-Learning. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 BrandPeng/Langchain1.0-Langgraph1.0-Learning?
    pass
    AI did not name BrandPeng/Langchain1.0-Langgraph1.0-Learning — 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?

  • If a team adopts BrandPeng/Langchain1.0-Langgraph1.0-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BrandPeng/Langchain1.0-Langgraph1.0-Learning 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 BrandPeng/Langchain1.0-Langgraph1.0-Learning solve, and who is the primary audience?
    pass
    AI did not name BrandPeng/Langchain1.0-Langgraph1.0-Learning — 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 BrandPeng/Langchain1.0-Langgraph1.0-Learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/BrandPeng/Langchain1.0-Langgraph1.0-Learning.svg)](https://repogeo.com/en/r/BrandPeng/Langchain1.0-Langgraph1.0-Learning)
HTML
<a href="https://repogeo.com/en/r/BrandPeng/Langchain1.0-Langgraph1.0-Learning"><img src="https://repogeo.com/badge/BrandPeng/Langchain1.0-Langgraph1.0-Learning.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

BrandPeng/Langchain1.0-Langgraph1.0-Learning — 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