RRepoGEO

REPOGEO REPORT · LITE

LazyAGI/LazyLLM

Default branch main · commit 97716044 · scanned 5/17/2026, 7:41:38 AM

GitHub: 3,829 stars · 389 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
40 /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
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 LazyAGI/LazyLLM, 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
  • hightopics#1
    Add 'low-code' and 'llm-optimization' to topics

    Why:

    CURRENT
    agents, ai-agent, data, deep-learning, documentation-tool, finetuning, framework, knowlege-graph, langchain, lazyllm, llamaindex, llm, llms, rag
    COPY-PASTE FIX
    agents, ai-agent, data, deep-learning, documentation-tool, finetuning, framework, knowlege-graph, langchain, lazyllm, llamaindex, llm, llms, low-code, llm-optimization, rag
  • mediumreadme#2
    Strengthen the 'iterative optimization' statement in the README's opening

    Why:

    CURRENT
    It assists developers in creating complex AI applications at very low costs and enables continuous iterative optimization.
    COPY-PASTE FIX
    It assists developers in creating complex AI applications at very low costs, offering robust support for continuous iterative optimization and fine-tuning.
  • lowreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## LazyLLM vs. Other Frameworks

LazyLLM differentiates itself with a minimalist design and a strong focus on simplicity and ease of use, providing a lightweight, less opinionated framework for building multi-agent LLM applications. Unlike more feature-rich and abstracted alternatives like LangChain or LlamaIndex, LazyLLM prioritizes rapid prototyping, low-code development, and efficient iterative optimization, making it ideal for developers seeking agility and control.

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 LazyAGI/LazyLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. joaomdmoura/crewai · recommended 1×
  3. langchain-ai/langgraph · recommended 1×
  4. microsoft/autogen · recommended 1×
  5. TransformerOptimus/SuperAGI · recommended 1×
  • CATEGORY QUERY
    How to quickly build multi-agent LLM applications with a low-code framework?
    you: not recommended
    AI recommended (in order):
    1. CrewAI (joaomdmoura/crewai)
    2. LangChain (langchain-ai/langchain)
    3. LangGraph (langchain-ai/langgraph)
    4. AutoGen (microsoft/autogen)
    5. SuperAGI (TransformerOptimus/SuperAGI)
    6. AgentOps (AgentOps/agentops)
    7. LlamaIndex (run-llama/llama_index)

    AI recommended 7 alternatives but never named LazyAGI/LazyLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools simplify iterative optimization and fine-tuning for complex LLM applications?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B) (wandb/wandb)
    2. MLflow (mlflow/mlflow)
    3. LangChain (langchain-ai/langchain)
    4. LangSmith (langchain-ai/langsmith-sdk)
    5. DeepEval (confident-ai/deepeval)
    6. Hugging Face Accelerate (huggingface/accelerate)
    7. Optuna (optuna/optuna)

    AI recommended 7 alternatives but never named LazyAGI/LazyLLM. 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 LazyAGI/LazyLLM?
    pass
    AI named LazyAGI/LazyLLM explicitly

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

  • If a team adopts LazyAGI/LazyLLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LazyAGI/LazyLLM 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 LazyAGI/LazyLLM solve, and who is the primary audience?
    pass
    AI named LazyAGI/LazyLLM explicitly

    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 LazyAGI/LazyLLM. 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/LazyAGI/LazyLLM.svg)](https://repogeo.com/en/r/LazyAGI/LazyLLM)
HTML
<a href="https://repogeo.com/en/r/LazyAGI/LazyLLM"><img src="https://repogeo.com/badge/LazyAGI/LazyLLM.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

LazyAGI/LazyLLM — 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