RRepoGEO

REPOGEO REPORT · LITE

SkyworkAI/DeepResearchAgent

Default branch main · commit 5e3c95d1 · scanned 5/11/2026, 5:21:46 AM

GitHub: 3,379 stars · 440 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 SkyworkAI/DeepResearchAgent, 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
  • hightopics#1
    Add specific topics for self-evolving agents and agent lifecycle management

    Why:

    CURRENT
    gaia, general-purpose, multiagent-systems, multimodel
    COPY-PASTE FIX
    gaia, general-purpose, multiagent-systems, multimodel, llm-agents, self-evolving-agents, agent-lifecycle, agent-versioning
  • highreadme#2
    Strengthen the README's opening sentence to emphasize framework and robust lifecycle

    Why:

    CURRENT
    Deep Research Agent is a self-evolution protocol and runtime for LLM-based agent systems.
    COPY-PASTE FIX
    Deep Research Agent is a pioneering framework and self-evolution protocol for LLM-based agent systems, uniquely providing robust lifecycle, context, and version management.
  • mediumabout#3
    Update the repository description to highlight self-evolving and robust lifecycle aspects

    Why:

    CURRENT
    DeepResearchAgent is a hierarchical multi-agent system designed not only for deep research tasks but also for general-purpose task solving. The framework leverages a top-level planning agent to coordinate multiple specialized lower-level agents, enabling automated task decomposition and efficient execution across diverse and complex domains.
    COPY-PASTE FIX
    DeepResearchAgent is a hierarchical, self-evolving multi-agent system designed for deep research and general-purpose task solving. This framework uniquely leverages a top-level planning agent to coordinate specialized lower-level agents, enabling automated task decomposition, efficient execution, and robust lifecycle management across diverse and complex domains.

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 SkyworkAI/DeepResearchAgent
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. AutoGen · recommended 1×
  3. Haystack · recommended 1×
  4. Mesa · recommended 1×
  5. SPADE · recommended 1×
  • CATEGORY QUERY
    What are the best multi-agent frameworks for complex task decomposition and execution?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. Haystack
    4. Mesa
    5. SPADE
    6. JADE

    AI recommended 6 alternatives but never named SkyworkAI/DeepResearchAgent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build self-evolving LLM agent systems with robust lifecycle and version control?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. MLflow
    4. Weights & Biases
    5. DVC
    6. Kubernetes
    7. Ray

    AI recommended 7 alternatives but never named SkyworkAI/DeepResearchAgent. 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 SkyworkAI/DeepResearchAgent?
    pass
    AI did not name SkyworkAI/DeepResearchAgent — 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 SkyworkAI/DeepResearchAgent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SkyworkAI/DeepResearchAgent 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 SkyworkAI/DeepResearchAgent solve, and who is the primary audience?
    pass
    AI named SkyworkAI/DeepResearchAgent 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 SkyworkAI/DeepResearchAgent. 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/SkyworkAI/DeepResearchAgent.svg)](https://repogeo.com/en/r/SkyworkAI/DeepResearchAgent)
HTML
<a href="https://repogeo.com/en/r/SkyworkAI/DeepResearchAgent"><img src="https://repogeo.com/badge/SkyworkAI/DeepResearchAgent.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

SkyworkAI/DeepResearchAgent — 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