REPOGEO REPORT · LITE

asinghcsu/AgenticRAG-Survey

Default branch main · commit 1d91e657 · scanned 6/21/2026, 10:52:51 AM

GitHub: 1,671 stars · 179 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

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 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 asinghcsu/AgenticRAG-Survey, 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

highabout#1

Clarify the 'About' description to emphasize it's a survey repository

Why:

CURRENT

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

COPY-PASTE FIX

Official repository for the 'Agentic Retrieval-Augmented Generation: A Survey' paper, exploring advanced RAG systems enhanced with AI LLM agents.

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a `LICENSE` file in the repository root, for example, using the MIT License template, to clearly state the terms of use.

mediumreadme#3

Add a concise introductory sentence to the README's opening

Why:

COPY-PASTE FIX

Add the following sentence immediately after the main title: 'This is the official repository accompanying the comprehensive survey paper on Agentic Retrieval-Augmented Generation, providing a curated resource for researchers and practitioners.'

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 asinghcsu/AgenticRAG-Survey

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

langchain-ai/langchain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchain · recommended 2×
run-llama/llama_index · recommended 2×
joaomdmoura/crewAI · recommended 2×
deepset-ai/haystack · recommended 2×
microsoft/autogen · recommended 2×

CATEGORY QUERY
How can I enhance RAG systems with autonomous AI agents for better performance?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. AutoGPT (Significant-Gravitas/AutoGPT)
4. CrewAI (joaomdmoura/crewAI)
5. Haystack (deepset-ai/haystack)
6. OpenAI Assistants API
7. AutoGen (microsoft/autogen)
AI recommended 7 alternatives but never named asinghcsu/AgenticRAG-Survey. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the different agentic workflow patterns for building advanced retrieval-augmented generation?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Haystack (deepset-ai/haystack)
4. AutoGen (Microsoft) (microsoft/autogen)
5. CrewAI (joaomdmoura/crewAI)
AI recommended 5 alternatives but never named asinghcsu/AgenticRAG-Survey. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
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 asinghcsu/AgenticRAG-Survey?
pass
AI did not name asinghcsu/AgenticRAG-Survey — 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 asinghcsu/AgenticRAG-Survey in production, what risks or prerequisites should they evaluate first?
pass
AI named asinghcsu/AgenticRAG-Survey 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 asinghcsu/AgenticRAG-Survey solve, and who is the primary audience?
pass
AI did not name asinghcsu/AgenticRAG-Survey — 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?

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MARKDOWN (README)

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asinghcsu/AgenticRAG-Survey — 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