RRepoGEO

REPOGEO REPORT · LITE

inclusionAI/AWorld

Default branch main · commit e71aaa6e · scanned 6/26/2026, 9:16:53 AM

GitHub: 1,203 stars · 124 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 inclusionAI/AWorld, 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
    Clarify AWorld's core identity as an agent framework in the README's opening

    Why:

    CURRENT
    General AI often hits a "wall of context"—the nuanced data, workflows, and intuition that define your world. An agent's true power lies not in the model alone, but in its Agent Harness: the framework orchestrating its tools, memory, context, and execution. This is the AWorld Thesis: A powerful harness is not enough. True AI scaling is unlocked only when experts like you embed the invaluable knowledge, effectively building the gate in that wall. AWorld is the platform designed for this singular purpose. We provide a complete, battle-tested Harness as the recipe for you, the expert, to forge your knowledge into a fleet of autonomous agents. Together, we move beyond AI's generic promise to create robust, precise applications that master your specific domain.
    COPY-PASTE FIX
    AWorld is a comprehensive agent framework (an 'Agent Harness') designed to empower domain experts to build, orchestrate, and deploy fleets of autonomous AI agents that master their specific domains. It provides the battle-tested tools for managing agent memory, context, and execution, moving beyond generic AI to create robust, precise applications. The core AWorld Thesis is that true AI scaling is unlocked when experts embed their invaluable knowledge into this harness, effectively building the 'gate in the wall' of AI's context limitations.
  • mediumtopics#2
    Refine topics to emphasize agent orchestration and de-emphasize generic simulation

    Why:

    CURRENT
    agent-framework, agent-learning, agent-runtime, browsecomp, environment, gaia, mcp, rl-training, world-model, xbench
    COPY-PASTE FIX
    agent-framework, agent-learning, agent-runtime, agent-orchestration, agent-harness, multi-agent-systems, knowledge-embedding, rl-training, tool-use, context-management, xbench
  • mediumcomparison#3
    Add a 'Comparison to other Agent Frameworks' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to other Agent Frameworks

AWorld stands apart from other agent frameworks like LangChain, LlamaIndex, or AutoGPT by focusing on empowering domain experts to embed their unique knowledge directly into a robust 'Agent Harness.' While other frameworks provide foundational tools for agent construction, AWorld emphasizes the complete lifecycle of knowledge-driven agent development, orchestration, and deployment within specific, complex domains. Our platform is designed for experts to transform their nuanced understanding into precise, autonomous agent fleets, rather than just providing generic agent building blocks.

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 inclusionAI/AWorld
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. AutoGPT · recommended 1×
  4. BabyAGI · recommended 1×
  5. Microsoft Semantic Kernel · recommended 1×
  • CATEGORY QUERY
    How to build and manage AI agents with complex tool orchestration and memory?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. Microsoft Semantic Kernel
    6. Haystack
    7. CrewAI

    AI recommended 7 alternatives but never named inclusionAI/AWorld. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for simulating environments and training AI agents effectively?
    you: not recommended
    AI recommended (in order):
    1. Gymnasium
    2. Unity ML-Agents
    3. DeepMind Lab
    4. MetaWorld
    5. Isaac Sim
    6. AirSim
    7. RoboSchool

    AI recommended 7 alternatives but never named inclusionAI/AWorld. 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 inclusionAI/AWorld?
    pass
    AI named inclusionAI/AWorld explicitly

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

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

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

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