eosphoros
@eosphoros-ai • Building Open AI-Native Data Infrastructure. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
96%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
15 repos
Visible snapshot
1 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (7), Unknown (3), CSS (2)
Average size
1.8K
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-01-19(219d ago)GitHub API fallback15 repositories
96%
of the visible star count comes from this organization's top three repositories.
1.8K
stars per repository in this same snapshot.
Python
is the most common language here, with 1 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 75% of its visible star count.
Flagship share 75%Breakout repo: DB-GPT
Organization pages work best when you separate portfolio breadth from flagship concentration. In eosphoros's case, the visible top three repositories account for about 96% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (7), Unknown (3), CSS (2). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | eosphoros-ai/DB-GPT open-source agentic AI data assistant for the next generation of AI + Data products. | Python | 19.8K |
| 2 | eosphoros-ai/Awesome-Text2SQL Curated tutorials and resources for Large Language Models, Text2SQL, Text2DSL、Text2API、Text2Vis and more. | 3.7K | |
| 3 | eosphoros-ai/DB-GPT-Hub A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL | Python | 2K |
| 4 | eosphoros-ai/liteio LiteIO is a cloud-native block device service that uses multiple storage engines, including SPDK and LVM, to achieve high performance. It is specifically designed for Kubernetes in a hyper-converged architecture, allowing for block device provisioning across the entire cluster. | Go | 318 |
| 5 | eosphoros-ai/DB-GPT-Web DB-GPT WebUI，LLM to vision. | TypeScript | 265 |
| 6 | eosphoros-ai/dbgpts Intelligent data apps and assets with LLMs | Python | 193 |
| 7 | eosphoros-ai/DB-GPT-Plugins Multi-Agents & Plugins repo for DB-GPT, Can complete various tasks around databases. | Python | 103 |
| 8 | eosphoros-ai/Falcon Falcon is a continuously-evolving, high-quality benchmark for natural-language-to-SQL (Text2SQL) systems. | HTML | 51 |
| 9 | eosphoros-ai/sqlgpt-parser sqlgpt-parser is a Python implementation of an SQL parser that effectively converts SQL statements into Abstract Syntax Trees (AST). By leveraging AST tree comparisons between two SQL queries, it becomes possible to achieve robust evaluation of text-to-SQL models. | Python | 36 |
| 10 | eosphoros-ai/Text2SQL-Eval Text2SQL-Eval is a Text-to-SQL evaluating component for LLM trained on an open-source training dataset. | Python | 15 |
| 11 | eosphoros-ai/community | 14 | |
| 12 | eosphoros-ai/Bard-Proxy A proxy for Google Bard LLM | Python | 10 |
| 13 | eosphoros-ai/website DB-GPT WebSite | CSS | 3 |
| 14 | eosphoros-ai/DataSage DataSage is the next-generation data analysis agent, the data analysis expert right at your side | 0 | |
| 15 | eosphoros-ai/Docs DB-GPT Document website. | CSS | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.