REPOGEO REPORT · LITE

bragai/bRAG-langchain

Default branch main · commit a3e5c7b0 · scanned 6/20/2026, 10:22:49 AM

GitHub: 4,117 stars · 495 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 bragai/bRAG-langchain, 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 emphasize "guide" and "boilerplate"

Why:

CURRENT

# Retrieval-Augmented Generation (RAG) Project

#### 🔜 Check out bragai.dev (launching soon)

This repository contains a comprehensive exploration of Retrieval-Augmented Generation (RAG) for various applications.

COPY-PASTE FIX

# Retrieval-Augmented Generation (RAG) Project: A Comprehensive Guide & Boilerplate

#### 🔜 Check out bragai.dev (launching soon)

This repository offers a comprehensive, hands-on guide and ready-to-use boilerplate code for building your own Retrieval-Augmented Generation (RAG) applications from introductory to advanced levels.

mediumreadme#2

Clarify the repository's license in the README

Why:

COPY-PASTE FIX

## License

This project is licensed under [Specify License Name(s) here, e.g., "a custom license combining MIT and Apache 2.0 terms"]. Please refer to the [LICENSE](LICENSE) file for full details.

lowreadme#3

Add a "How is this different?" section to the README

Why:

COPY-PASTE FIX

## How is bRAG-langchain different?

Unlike foundational frameworks like LangChain or LlamaIndex, this repository focuses on providing a ready-to-use, opinionated collection of advanced RAG optimization techniques (e.g., query transformation, re-ranking, contextual compression) integrated into practical, guided examples and boilerplate code. It serves as a hands-on learning resource and a starting point for building sophisticated RAG applications, rather than just a library of components.

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 bragai/bRAG-langchain

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×
Chroma · recommended 2×
Pinecone · recommended 2×
OpenAI API · recommended 1×

CATEGORY QUERY
How can I build a custom retrieval-augmented generation application using Python?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. OpenAI API
4. Anthropic Claude
5. Mistral
6. Llama 2
7. Chroma
8. Pinecone
9. Faiss
10. Sentence-Transformers
AI recommended 10 alternatives but never named bragai/bRAG-langchain. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good resources for learning and implementing a RAG chatbot boilerplate?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack (deepset/Haystack)
4. Hugging Face Transformers
5. Hugging Face Datasets
6. Pinecone
7. Weaviate
8. Chroma
9. Qdrant
AI recommended 9 alternatives but never named bragai/bRAG-langchain. 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 bragai/bRAG-langchain?
pass
AI named bragai/bRAG-langchain explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts bragai/bRAG-langchain in production, what risks or prerequisites should they evaluate first?
pass
AI named bragai/bRAG-langchain 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 bragai/bRAG-langchain solve, and who is the primary audience?
pass
AI named bragai/bRAG-langchain explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/bragai/bRAG-langchain"><img src="https://repogeo.com/badge/bragai/bRAG-langchain.svg" alt="RepoGEO" /></a>

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bragai/bRAG-langchain — 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