REPOGEO REPORT · LITE

BrainBlend-AI/atomic-agents

Default branch main · commit ac65f62f · scanned 6/29/2026, 4:57:13 AM

GitHub: 6,010 stars · 515 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

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 BrainBlend-AI/atomic-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

highabout#1

Refine the repository's 'About' description

Why:

CURRENT

Building AI agents, atomically

COPY-PASTE FIX

A lightweight, modular Python framework for building robust, composable AI agent pipelines with single-purpose components.

highreadme#2

Strengthen the README's opening paragraph

Why:

CURRENT

The Atomic Agents framework is designed around the concept of atomicity to be an extremely lightweight and modular framework for building Agentic AI pipelines and applications without sacrificing developer experience and maintainability.

COPY-PASTE FIX

Atomic Agents is an extremely lightweight and modular Python framework for building robust, composable AI agent pipelines. It emphasizes single-purpose components, enabling developers to create predictable and maintainable AI applications with the same software engineering principles they already know.

mediumtopics#3

Add more specific topics to improve categorization

Why:

CURRENT

ai, artificial-intelligence, large-language-model, large-language-models, llms, openai, openai-api

COPY-PASTE FIX

ai, artificial-intelligence, large-language-model, large-language-models, llms, openai, openai-api, agent-framework, modular-ai, composable-ai, single-responsibility-principle

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 BrainBlend-AI/atomic-agents

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×
LlamaIndex · recommended 2×
Haystack · recommended 2×
CrewAI · recommended 2×
AutoGen · recommended 1×

CATEGORY QUERY
How can I build AI agent applications using composable, single-purpose components?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. AutoGen
5. CrewAI
6. Marvin
7. OpenAI Assistants API
AI recommended 7 alternatives but never named BrainBlend-AI/atomic-agents. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are lightweight Python frameworks for building robust, maintainable LLM agent pipelines?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. CrewAI
5. Guidance
6. LiteLLM
7. Instructor
AI recommended 7 alternatives but never named BrainBlend-AI/atomic-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 BrainBlend-AI/atomic-agents?
pass
AI named BrainBlend-AI/atomic-agents explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts BrainBlend-AI/atomic-agents in production, what risks or prerequisites should they evaluate first?
pass
AI named BrainBlend-AI/atomic-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 BrainBlend-AI/atomic-agents solve, and who is the primary audience?
pass
AI did not name BrainBlend-AI/atomic-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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MARKDOWN (README)

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BrainBlend-AI/atomic-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