RRepoGEO

REPOGEO REPORT · LITE

Eigenwise/atomic-agents

Default branch main · commit dfe17760 · scanned 6/10/2026, 3:02:01 PM

GitHub: 5,971 stars · 513 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 Eigenwise/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
  • highreadme#1
    Reposition the README's opening paragraph to emphasize atomicity and contrast with other frameworks

    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 Agentic AI pipelines and applications. It champions **atomicity**, enabling you to construct AI applications from single-purpose, reusable, and composable components (agents, tools, context providers). This approach offers a highly predictable and testable alternative to monolithic frameworks, allowing developers to apply standard software engineering principles to AI development.
  • mediumreadme#2
    Add a dedicated 'Why Atomic Agents?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Atomic Agents?
While frameworks like LangChain and LlamaIndex offer broad capabilities, Atomic Agents focuses on **atomicity** for unparalleled modularity and testability. We provide a lightweight alternative for developers who prioritize:
*   **Fine-grained Control:** Build agents from small, predictable components.
*   **Testability:** Easily unit-test individual agent components.
*   **Maintainability:** Simple, single-purpose components reduce complexity.
*   **Performance:** Minimal overhead for efficient agent execution.
  • mediumtopics#3
    Expand repository topics with more specific terms related to agentic AI and modular frameworks

    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, ai-agents, agentic-ai, modular-ai, python-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 Eigenwise/atomic-agents
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. deepset/haystack · recommended 1×
  4. CrewAI · recommended 1×
  5. Autogen · recommended 1×
  • CATEGORY QUERY
    How to build modular and reusable AI agent pipelines with Python?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/haystack)
    4. CrewAI
    5. Autogen
    6. Marvin

    AI recommended 6 alternatives but never named Eigenwise/atomic-agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a lightweight framework for composable AI agent development using LLMs.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Assistants API
    5. AutoGPT
    6. Guidance

    AI recommended 6 alternatives but never named Eigenwise/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 Eigenwise/atomic-agents?
    pass
    AI did not name Eigenwise/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?

  • If a team adopts Eigenwise/atomic-agents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Eigenwise/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 Eigenwise/atomic-agents solve, and who is the primary audience?
    pass
    AI named Eigenwise/atomic-agents 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 Eigenwise/atomic-agents. 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/Eigenwise/atomic-agents.svg)](https://repogeo.com/en/r/Eigenwise/atomic-agents)
HTML
<a href="https://repogeo.com/en/r/Eigenwise/atomic-agents"><img src="https://repogeo.com/badge/Eigenwise/atomic-agents.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Eigenwise/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