RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/agents-from-scratch

Default branch main · commit 0d4fe031 · scanned 5/23/2026, 8:13:06 AM

GitHub: 1,777 stars · 360 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
35 /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
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 langchain-ai/agents-from-scratch, 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
  • highabout#1
    Update the repository description to emphasize its role as a guide/course

    Why:

    CURRENT
    Build an email assistant with human-in-the-loop and memory
    COPY-PASTE FIX
    A comprehensive guide and course to building AI agents from scratch, focusing on an email assistant with human-in-the-loop and memory.
  • hightopics#2
    Add specific topics that highlight the repo's educational nature

    Why:

    CURRENT
    agents, memory
    COPY-PASTE FIX
    agents, memory, tutorial, guide, course, learning, from-scratch, langchain, langgraph
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory, choosing an appropriate open-source license (e.g., MIT, Apache-2.0) for the course material.

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/agents-from-scratch
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. OpenAI GPT-4 · recommended 1×
  3. GPT-3.5 Turbo · recommended 1×
  4. Pinecone · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    How to build an AI email assistant with memory and human-in-the-loop capabilities?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI GPT-4
    3. GPT-3.5 Turbo
    4. Pinecone
    5. Weaviate
    6. Qdrant
    7. React
    8. Vue
    9. Angular
    10. Python
    11. Node.js
    12. LlamaIndex
    13. PostgreSQL
    14. pgvector
    15. Streamlit
    16. Gradio
    17. Microsoft Semantic Kernel
    18. Azure OpenAI Service
    19. Azure Cosmos DB
    20. Power Apps
    21. .NET
    22. ASP.NET Core
    23. Hugging Face Transformers
    24. Llama 2
    25. Mistral
    26. FAISS
    27. Annoy
    28. Flask
    29. Django

    AI recommended 29 alternatives but never named langchain-ai/agents-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a guide to develop custom AI agents with evaluation and memory features.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset) (deepset-ai/haystack)
    4. AutoGen (Microsoft) (microsoft/autogen)
    5. OpenAI Assistants API
    6. CrewAI (joaomdmoura/crewai)
    7. BabyAGI / Auto-GPT

    AI recommended 7 alternatives but never named langchain-ai/agents-from-scratch. 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 langchain-ai/agents-from-scratch?
    pass
    AI named langchain-ai/agents-from-scratch 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/agents-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/agents-from-scratch 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/agents-from-scratch solve, and who is the primary audience?
    pass
    AI named langchain-ai/agents-from-scratch explicitly

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

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langchain-ai/agents-from-scratch — 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