REPOGEO REPORT · LITE

SkyworkAI/DeepResearchAgent

Default branch main · commit 5e3c95d1 · scanned 6/21/2026, 8:22:00 AM

GitHub: 3,468 stars · 448 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

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 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

highreadme#1

Reposition README's opening sentence to highlight unique lifecycle/version management

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 self-evolution protocol and runtime for LLM-based agent systems, uniquely providing robust lifecycle, context, and version management to overcome the limitations of monolithic agent compositions.

mediumtopics#2

Add specific topics for self-evolving agents and lifecycle management

Why:

CURRENT

gaia, general-purpose, multiagent-systems, multimodel

COPY-PASTE FIX

gaia, general-purpose, multiagent-systems, multimodel, self-evolving-agents, agent-lifecycle, version-management

lowabout#3

Update the 'About' description to emphasize self-evolution and lifecycle management

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 multi-agent system and self-evolution protocol for LLM agents, offering robust lifecycle, context, and version management. It enables automated task decomposition and efficient execution 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

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

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

CATEGORY QUERY
How to build a hierarchical multi-agent system for complex general-purpose task automation?
you: not recommended
AI recommended (in order):
1. LangChain
2. AutoGen
3. CrewAI
4. Haystack
5. LlamaIndex
6. Open Interpreter
7. OpenAI API
8. Anthropic API
AI recommended 8 alternatives but never named SkyworkAI/DeepResearchAgent. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Framework for self-evolving LLM agents with robust lifecycle and version management?
you: not recommended
AI recommended (in order):
1. LangChain
2. AutoGPT
3. LlamaIndex
4. OpenAI Assistants API
5. Haystack
6. MetaGPT
AI recommended 6 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 named SkyworkAI/DeepResearchAgent explicitly
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.

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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

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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