RRepoGEO

REPOGEO REPORT · LITE

mpaepper/llm_agents

Default branch main · commit 5040c3c9 · scanned 6/28/2026, 10:48:29 PM

GitHub: 1,046 stars · 85 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
27 /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
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 mpaepper/llm_agents, 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
    Clarify the unique value proposition in the README's opening

    Why:

    CURRENT
    ## LLM Agents

Small library to build agents which are controlled by large language models (LLMs) which is heavily inspired by <a href="https://github.com/hwchase17/langchain/" target="_blank">langchain</a>. The goal was to get a better grasp of how such an agent works and understand it all in very few lines of code. Langchain is great, but it already has a few more files and abstraction layers, so I thought it would be nice to build the most important parts of a simple agent from scratch.
    COPY-PASTE FIX
    ## LLM Agents: Build and Understand LLM-Controlled Agents from Scratch

This is a lightweight library designed for developers and researchers who want to build and deeply understand LLM-controlled agents with minimal abstraction. Inspired by LangChain, this project distills the core mechanics of an agent into very few lines of code, making it ideal for learning agent architecture from the ground up without the complexity of larger frameworks.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    deep-learning, langchain, llms, machine-learning
    COPY-PASTE FIX
    deep-learning, langchain, llms, machine-learning, llm-agents-from-scratch, educational-code, minimal-llm-framework, agent-architecture
  • lowabout#3
    Refine the repository description to highlight its unique focus

    Why:

    CURRENT
    Build agents which are controlled by LLMs
    COPY-PASTE FIX
    A lightweight, educational library to build LLM-controlled agents from scratch, simplifying complex agent architectures for better understanding.

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 mpaepper/llm_agents
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. run-llama/llama_index · recommended 1×
  3. microsoft/guidance · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. jxnl/instructor · recommended 1×
  • CATEGORY QUERY
    Looking for a lightweight library to build custom AI agents with large language models.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex (run-llama/llama_index)
    3. Guidance (microsoft/guidance)
    4. Transformers (huggingface/transformers)
    5. Instructor (jxnl/instructor)
    6. LiteLLM (BerriAI/litellm)

    AI recommended 6 alternatives but never named mpaepper/llm_agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build autonomous LLM agents from scratch for better understanding?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Faiss
    5. Pinecone
    6. Weaviate
    7. Python Standard Library
    8. Pydantic
    9. Docker

    AI recommended 9 alternatives but never named mpaepper/llm_agents. 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 mpaepper/llm_agents?
    pass
    AI did not name mpaepper/llm_agents — 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 mpaepper/llm_agents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mpaepper/llm_agents 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 mpaepper/llm_agents solve, and who is the primary audience?
    pass
    AI did not name mpaepper/llm_agents — 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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mpaepper/llm_agents — 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