REPOGEO REPORT · LITE

NirDiamant/Controllable-RAG-Agent

Default branch main · commit fcbfb173 · scanned 6/26/2026, 6:03:04 AM

GitHub: 1,613 stars · 264 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

28 /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

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 NirDiamant/Controllable-RAG-Agent, 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 opening to highlight unique control and graph-based approach

Why:

CURRENT

An advanced Retrieval-Augmented Generation (RAG) solution designed to tackle complex questions that simple semantic similarity-based retrieval cannot solve. This project showcases a sophisticated deterministic graph acting as the "brain" of a highly controllable autonomous agent capable of answering non-trivial questions from your own data.

COPY-PASTE FIX

This repository offers a unique, highly controllable Retrieval-Augmented Generation (RAG) solution, distinct from generic frameworks. It leverages a sophisticated deterministic graph to provide explicit, fine-grained control and transparency over complex, multi-step question answering, enabling autonomous agents to tackle non-trivial questions from your own data.

mediumhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://diamant-ai.com/rag-made-simple
```

lowtopics#3

Add more specific topics to reinforce unique features

Why:

CURRENT

advanced-rag, agent, genai, langchain, langgraph, llm, llms, openai, python, rag

COPY-PASTE FIX

advanced-rag, agent, genai, langchain, langgraph, llm, llms, openai, python, rag, controllable-rag, deterministic-graph, rag-agent-framework

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 NirDiamant/Controllable-RAG-Agent

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×
LlamaIndex · recommended 2×
Haystack · recommended 2×
Microsoft Semantic Kernel · recommended 1×
OpenAI Assistants API · recommended 1×

CATEGORY QUERY
How to build a sophisticated RAG agent for complex, multi-step question answering?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. Microsoft Semantic Kernel
5. OpenAI Assistants API
6. AutoGen
7. Rasa
AI recommended 7 alternatives but never named NirDiamant/Controllable-RAG-Agent. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking an advanced RAG framework for building controllable agents with deterministic reasoning graphs.
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. Haystack
4. DSPy
5. Ragas
AI recommended 5 alternatives but never named NirDiamant/Controllable-RAG-Agent. 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 NirDiamant/Controllable-RAG-Agent?
pass
AI did not name NirDiamant/Controllable-RAG-Agent — 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 NirDiamant/Controllable-RAG-Agent in production, what risks or prerequisites should they evaluate first?
pass
AI named NirDiamant/Controllable-RAG-Agent 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 NirDiamant/Controllable-RAG-Agent solve, and who is the primary audience?
pass
AI named NirDiamant/Controllable-RAG-Agent 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 NirDiamant/Controllable-RAG-Agent. 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/NirDiamant/Controllable-RAG-Agent.svg)](https://repogeo.com/en/r/NirDiamant/Controllable-RAG-Agent)

HTML

<a href="https://repogeo.com/en/r/NirDiamant/Controllable-RAG-Agent"><img src="https://repogeo.com/badge/NirDiamant/Controllable-RAG-Agent.svg" alt="RepoGEO" /></a>

Pro

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NirDiamant/Controllable-RAG-Agent — 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