REPOGEO REPORT · LITE

metauto-ai/GPTSwarm

Default branch main · commit c23a827f · scanned 5/29/2026, 12:13:10 PM

GitHub: 1,004 stars · 99 forks

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 metauto-ai/GPTSwarm, 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

Reposition the README's opening statement to highlight unique value

Why:

CURRENT

GPTSwarm is a graph-based framework for LLM-based agents, providing two high-level features:It lets you build LLM-based agents from graphs. * It enables the customized and automatic self-organization of agent swarms with self-improvement capabilities.

COPY-PASTE FIX

🐝 **GPTSwarm is the first graph-based framework for self-improving LLM-based agents, leveraging Reinforcement Learning and Prompting Optimization to enable customized, automatic self-organization of agent swarms.** It lets you build LLM-based agents from graphs and empowers them with advanced self-improvement capabilities.

mediumreadme#2

Add a dedicated 'Why GPTSwarm?' or 'Key Differentiators' section

Why:

COPY-PASTE FIX

## Why GPTSwarm?

Unlike generic LLM orchestration frameworks or basic agent CLIs, GPTSwarm focuses on:

*   **Graph-based Swarm Intelligence:** Design and execute complex multi-agent systems where agents self-organize and collaborate through defined graph structures.
*   **Self-Improvement via RL/Prompting Optimization:** Agents continuously learn and optimize their performance using built-in reinforcement learning and prompting optimization techniques.
*   **Decentralized & Emergent Behavior:** Facilitate emergent intelligence from a collective of specialized agents, moving beyond single-agent or simple manager-worker paradigms.

lowtopics#3

Add 'graph' and 'optimization' to repository topics

Why:

CURRENT

agent, ai, gpt, multi-agent, python, reinforcement-learning, self-improvement, society-of-mind, swarm-intelligence

COPY-PASTE FIX

agent, ai, gpt, multi-agent, python, reinforcement-learning, self-improvement, society-of-mind, swarm-intelligence, graph, optimization

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 metauto-ai/GPTSwarm

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

AutoGPT

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

AutoGPT · recommended 1×
BabyAGI · recommended 1×
LangChain · recommended 1×
LlamaIndex · recommended 1×
CrewAI · recommended 1×

CATEGORY QUERY
How to build self-organizing, self-improving multi-agent systems using LLMs?
you: not recommended
AI recommended (in order):
1. AutoGPT
2. BabyAGI
3. LangChain
4. LlamaIndex
5. CrewAI
6. MetaGPT
7. OpenAI Assistants API
AI recommended 7 alternatives but never named metauto-ai/GPTSwarm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a Python framework to design and optimize LLM agents through graph structures.
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Haystack (deepset-ai/haystack)
4. Marvin (PrefectHQ/marvin)
5. DSPy (stanfordnlp/dspy)
6. AutoGPT (Significant-Gravitas/AutoGPT)
AI recommended 6 alternatives but never named metauto-ai/GPTSwarm. 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 metauto-ai/GPTSwarm?
pass
AI named metauto-ai/GPTSwarm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts metauto-ai/GPTSwarm in production, what risks or prerequisites should they evaluate first?
pass
AI named metauto-ai/GPTSwarm 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 metauto-ai/GPTSwarm solve, and who is the primary audience?
pass
AI named metauto-ai/GPTSwarm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/metauto-ai/GPTSwarm"><img src="https://repogeo.com/badge/metauto-ai/GPTSwarm.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite