REPOGEO REPORT · LITE

tablegpt/tablegpt-agent

Default branch main · commit 26bc576b · scanned 6/16/2026, 9:47:01 AM

GitHub: 636 stars · 59 forks

AI VISIBILITY SCORE

35 /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

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 tablegpt/tablegpt-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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

llm-agent, tabular-data, question-answering, table-qa, langgraph, llm-evaluation, benchmarks, nlp, machine-learning

highreadme#2

Reposition the README introduction to highlight its core function for tabular data QA and evaluation

Why:

CURRENT

# TableGPT Agent

## Introduction

`tablegpt-agent` is a pre-built agent for TableGPT2 (huggingface), a series of LLMs for table-based question answering. This agent is built on top of the Langgraph library and provides a user-friendly interface for interacting with TableGPT2.

COPY-PASTE FIX

# TableGPT Agent: An LLM Agent for Tabular Data Question Answering and Evaluation

## Introduction

`tablegpt-agent` is a specialized, pre-built agent designed for robust natural language question answering on complex tabular datasets, powered by TableGPT2 LLMs. Built on Langgraph, it offers a user-friendly interface for interacting with TableGPT2 and includes comprehensive evaluation scripts for table-related benchmarks.

mediumabout#3

Update the repository's GitHub description

Why:

CURRENT

A pre-built agent for TableGPT2.

COPY-PASTE FIX

A specialized LLM agent for natural language question answering on tabular data, including comprehensive evaluation tools for LLMs on table benchmarks.

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 tablegpt/tablegpt-agent

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×
pandas-dev/pandas · recommended 2×
huggingface/transformers · recommended 2×
OpenAI GPT-4 · recommended 1×

CATEGORY QUERY
How to implement an AI agent for question answering on large tabular datasets?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. OpenAI GPT-4
3. GPT-3.5 Turbo
4. PandasAI (gventuri/pandas-ai)
5. LlamaIndex (run-llama/llama_index)
6. Pandas (pandas-dev/pandas)
7. Hugging Face Transformers (huggingface/transformers)
8. T5
9. BART
10. Polars (pola-rs/polars)
11. PostgreSQL
12. Snowflake
13. BigQuery
14. DataPrep.EDA (sfu-db/dataprep)
AI recommended 14 alternatives but never named tablegpt/tablegpt-agent. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help evaluate large language models on tabular data benchmarks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Hugging Face Datasets (huggingface/datasets)
3. Hugging Face Evaluate (huggingface/evaluate)
4. OpenAI API
5. Azure OpenAI Service
6. LangChain (langchain-ai/langchain)
7. LlamaIndex (run-llama/llama_index)
8. scikit-learn (scikit-learn/scikit-learn)
9. Pandas (pandas-dev/pandas)
10. Tabular Playground Series (Kaggle)
AI recommended 10 alternatives but never named tablegpt/tablegpt-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 tablegpt/tablegpt-agent?
pass
AI named tablegpt/tablegpt-agent explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts tablegpt/tablegpt-agent in production, what risks or prerequisites should they evaluate first?
pass
AI named tablegpt/tablegpt-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 tablegpt/tablegpt-agent solve, and who is the primary audience?
pass
AI named tablegpt/tablegpt-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 tablegpt/tablegpt-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.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/tablegpt/tablegpt-agent.svg)](https://repogeo.com/en/r/tablegpt/tablegpt-agent)

HTML

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

Pro

Subscribe to Pro for deep diagnoses

tablegpt/tablegpt-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