REPOGEO REPORT · LITE

ginlix-ai/LangAlpha

Default branch main · commit db5e58ca · scanned 5/23/2026, 2:21:49 AM

GitHub: 1,154 stars · 167 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 ginlix-ai/LangAlpha, 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 to clearly state its LLM-powered financial agent framework nature

Why:

CURRENT

A vibe investing agent harness
LangAlpha is built to help interpret financial markets and support investment decisions.

COPY-PASTE FIX

LangAlpha: An open-source LLM-powered AI agent framework for financial market interpretation and investment decision support.

mediumabout#2
Refine the repository description to explicitly mention LLM-powered financial agents
Why:
CURRENT
```
Claude Code for Finance
```
COPY-PASTE FIX
```
An open-source LLM-powered AI agent framework for financial market analysis and investment decision support.
```

mediumcomparison#3

Add a "Comparison to Alternatives" section in the README

Why:

COPY-PASTE FIX

## Comparison to Alternatives

Unlike general-purpose machine learning libraries (e.g., PyTorch, TensorFlow, scikit-learn) or foundational LLM frameworks (e.g., LangChain, OpenAI GPT), LangAlpha is purpose-built as an LLM-powered AI agent framework specifically for quantitative trading and financial market analysis. We provide specialized tools for iterative thesis refinement, market screening, and pair-trade idea generation, moving beyond one-shot queries to support continuous investment workflows.

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 ginlix-ai/LangAlpha

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

QuantConnect (Lean Engine)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

QuantConnect (Lean Engine) · recommended 1×
Zipline · recommended 1×
PyTorch · recommended 1×
TensorFlow · recommended 1×
scikit-learn · recommended 1×

CATEGORY QUERY
How can I build an AI agent to interpret financial markets and support investment decisions?
you: not recommended
AI recommended (in order):
1. QuantConnect (Lean Engine)
2. Zipline
3. PyTorch
4. TensorFlow
5. scikit-learn
6. pandas
7. Ta-Lib
8. OpenBB Terminal
AI recommended 8 alternatives but never named ginlix-ai/LangAlpha. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help develop LLM-powered trading agents for market analysis and idea generation?
you: not recommended
AI recommended (in order):
1. OpenAI GPT-4
2. GPT-3.5 Turbo
3. Llama 3
4. Mixtral 8x7B
5. LangChain
6. Pandas
7. NumPy
8. Alpaca API
9. Interactive Brokers API
10. Pinecone
11. Weaviate
12. Streamlit
13. Gradio
AI recommended 13 alternatives but never named ginlix-ai/LangAlpha. 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 ginlix-ai/LangAlpha?
pass
AI named ginlix-ai/LangAlpha explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ginlix-ai/LangAlpha in production, what risks or prerequisites should they evaluate first?
pass
AI named ginlix-ai/LangAlpha 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 ginlix-ai/LangAlpha solve, and who is the primary audience?
pass
AI named ginlix-ai/LangAlpha 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 ginlix-ai/LangAlpha. 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/ginlix-ai/LangAlpha.svg)](https://repogeo.com/en/r/ginlix-ai/LangAlpha)

HTML

<a href="https://repogeo.com/en/r/ginlix-ai/LangAlpha"><img src="https://repogeo.com/badge/ginlix-ai/LangAlpha.svg" alt="RepoGEO" /></a>

Pro

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ginlix-ai/LangAlpha — 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