REPOGEO REPORT · LITE

2FastLabs/agent-squad

Default branch main · commit db10bf56 · scanned 5/8/2026, 4:47:20 PM

GitHub: 7,619 stars · 720 forks

AI VISIBILITY SCORE

33 /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

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 2FastLabs/agent-squad, 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 paragraph to highlight core differentiator

Why:

CURRENT

<p align="center">Flexible, lightweight open-source framework for orchestrating multiple AI agents to handle complex conversations.</p>

COPY-PASTE FIX

<p align="center">Agent Squad is a flexible, lightweight framework that provides a higher-level abstraction for the *entire lifecycle* of multi-agent AI systems, from declarative definition to scalable deployment and ongoing management, building upon and extending existing agent frameworks.</p>

mediumcomparison#2

Add a 'Comparison to other frameworks' section in the README

Why:

COPY-PASTE FIX

## 🆚 Comparison to Other Frameworks

Agent Squad differentiates itself by providing a higher-level framework for the *entire lifecycle* of multi-agent AI systems, from declarative definition to scalable deployment and ongoing management. While frameworks like LangChain and CrewAI offer powerful tools for individual agent development and basic orchestration, Agent Squad focuses on the declarative definition, scalable deployment, and comprehensive management of complex multi-agent teams, often integrating with or extending capabilities from these foundational libraries.

lowtopics#3

Review and expand topics with competitor-aligned keywords

Why:

CURRENT

agentic-ai, agents, ai-agents, ai-agents-framework, anthropic, anthropic-claude, aws, aws-bedrock, aws-cdk, aws-lambda, chatbot, framework, generative-ai, machine-learning, openai, openaiapi, orchestrator, python, serverless, typescript

COPY-PASTE FIX

agentic-ai, agents, ai-agents, ai-agents-framework, anthropic, anthropic-claude, aws, aws-bedrock, aws-cdk, aws-lambda, chatbot, framework, generative-ai, machine-learning, openai, openaiapi, orchestrator, python, serverless, typescript, multi-agent, llm-framework

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 2FastLabs/agent-squad

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
Haystack · recommended 2×
LlamaIndex · recommended 1×
Microsoft Semantic Kernel · recommended 1×
Rasa · recommended 1×

CATEGORY QUERY
How to orchestrate multiple AI agents for complex conversational flows?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Microsoft Semantic Kernel
4. Haystack
5. Rasa
6. OpenAI Assistants API
AI recommended 6 alternatives but never named 2FastLabs/agent-squad. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good Python frameworks for building multi-agent conversational AI systems?
you: not recommended
AI recommended (in order):
1. CrewAI
2. LangChain
3. AutoGen
4. Haystack
5. SPADE
AI recommended 5 alternatives but never named 2FastLabs/agent-squad. 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 2FastLabs/agent-squad?
pass
AI named 2FastLabs/agent-squad explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts 2FastLabs/agent-squad in production, what risks or prerequisites should they evaluate first?
pass
AI named 2FastLabs/agent-squad 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 2FastLabs/agent-squad solve, and who is the primary audience?
pass
AI did not name 2FastLabs/agent-squad — 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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2FastLabs/agent-squad — 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