RRepoGEO

REPOGEO REPORT · LITE

AgentEra/Agently

Default branch main · commit 6da1b5a1 · scanned 5/11/2026, 12:52:14 PM

GitHub: 1,571 stars · 173 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 AgentEra/Agently, 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
    Strengthen README's opening statement for category recall

    Why:

    CURRENT
    > **Build production-grade AI applications with stable outputs, composable agents, observable actions, and testable workflows.**
    COPY-PASTE FIX
    > **Agently is a Python framework for building reliable, production-grade generative AI applications with stable outputs, composable agents, observable actions, and testable LLM agent workflows.**
  • mediumtopics#2
    Add more specific topics for better category matching

    Why:

    CURRENT
    agent, agent-based-framework, agent-framework, chatglm, claude, deepseek, ernie, framework, gemini, google-gemini, gpt, llm-agent, llm-application, llm-apps, llm-framework, llmops, llms, minimax, python
    COPY-PASTE FIX
    agent, agent-based-framework, agent-framework, ai-framework, chatglm, claude, deepseek, ernie, framework, gemini, generative-ai-framework, google-gemini, gpt, llm-agent, llm-application, llm-apps, llm-framework, llmops, llms, minimax, production-ai, python, workflow-orchestration
  • lowabout#3
    Refine description to emphasize reliability and production-grade

    Why:

    CURRENT
    [GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code
    COPY-PASTE FIX
    [Reliable GenAI Application Development Framework] 🚀 Build production-grade GenAI applications quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code

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 AgentEra/Agently
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. Haystack · recommended 2×
  4. CrewAI · recommended 2×
  5. Microsoft Semantic Kernel · recommended 1×
  • CATEGORY QUERY
    What framework helps build reliable, production-ready generative AI applications with agent capabilities?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft Semantic Kernel
    4. Haystack
    5. AutoGPT
    6. CrewAI

    AI recommended 6 alternatives but never named AgentEra/Agently. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python framework to manage complex LLM agent workflows and easily swap models.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. CrewAI
    5. Marvin
    6. Guidance

    AI recommended 6 alternatives but never named AgentEra/Agently. 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 AgentEra/Agently?
    pass
    AI named AgentEra/Agently explicitly

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

  • If a team adopts AgentEra/Agently in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AgentEra/Agently 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 AgentEra/Agently solve, and who is the primary audience?
    pass
    AI named AgentEra/Agently explicitly

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

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AgentEra/Agently — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite