RRepoGEO

REPOGEO REPORT · LITE

composable-models/llm_multiagent_debate

Default branch main · commit 98467493 · scanned 6/5/2026, 4:23:05 PM

GitHub: 534 stars · 82 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 composable-models/llm_multiagent_debate, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, multi-agent-systems, debate, factuality, reasoning, icml-2024, research-code, language-models
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT License) to the repository root.
  • highreadme#3
    Reposition the README's introductory paragraph to clarify project scope

    Why:

    CURRENT
    This is a preliminary implementation of the paper "Improving Factuality and Reasoning in Language Models through Multiagent Debate". More tasks and settings will be released soon.
    COPY-PASTE FIX
    This repository provides the official research implementation for the ICML 2024 paper "Improving Factuality and Reasoning in Language Models through Multiagent Debate," focusing on enhancing LLM performance on complex tasks through structured multi-agent argumentation. More tasks and settings will be released soon.

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 composable-models/llm_multiagent_debate
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google's Gemini
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google's Gemini · recommended 2×
  2. microsoft/autogen · recommended 1×
  3. joaomdmoura/crewai · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I improve language model factuality and reasoning using multi-agent systems?
    you: not recommended
    AI recommended (in order):
    1. AutoGen (microsoft/autogen)
    2. CrewAI (joaomdmoura/crewai)
    3. LangChain (langchain-ai/langchain)
    4. LlamaIndex (run-llama/llama_index)
    5. Haystack (deepset-ai/haystack)
    6. OpenAI API
    7. Anthropic API
    8. Google's Gemini

    AI recommended 8 alternatives but never named composable-models/llm_multiagent_debate. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What techniques enhance LLM performance on math and reasoning tasks beyond basic prompting?
    you: not recommended
    AI recommended (in order):
    1. Code Interpreter
    2. LangChain
    3. LlamaIndex
    4. SymPy
    5. OpenAI's Function Calling
    6. Google's Gemini

    AI recommended 6 alternatives but never named composable-models/llm_multiagent_debate. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 composable-models/llm_multiagent_debate?
    pass
    AI named composable-models/llm_multiagent_debate explicitly

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

  • If a team adopts composable-models/llm_multiagent_debate in production, what risks or prerequisites should they evaluate first?
    pass
    AI named composable-models/llm_multiagent_debate 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 composable-models/llm_multiagent_debate solve, and who is the primary audience?
    pass
    AI did not name composable-models/llm_multiagent_debate — 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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composable-models/llm_multiagent_debate — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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