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Common AI agent framework solving your data problems
| Date | Stars |
|---|---|
| 2026-07-31 | 405 |
| 2026-08-06 | 405 |
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# Ryoma
AI Powered Data Agent framework, a comprehensive solution for data analysis, engineering, and visualization.
[](https://github.com/project-ryoma/ryoma/actions/workflows/build.yml?query=workflow%3Abuild)
[](https://pypi.org/project/ryoma/)
[](https://github.com/project-ryoma/ryoma/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)
[](https://github.com/psf/black)
[](https://github.com/PyCQA/bandit)
[](https://github.com/project-ryoma/ryoma/blob/main/.pre-commit-config.yaml)
[](https://github.com/project-ryoma/ryoma/blob/main/LICENSE)
[](https://github.com/project-ryoma/ryoma/blob/main/assets/images/coverage.svg)
## Tech Stack
Our platform leverages a combination of cutting-edge technologies and frameworks:
- **[Langchain](https://www.langchain.com/)**: Facilitates the seamless integration of language models into application workflows, significantly enhancing AI interaction capabilities.
- **[Reflex](https://reflex.dev/)**: An open-source framework for quickly building beautiful, interactive web applications in pure Python
- **[Apache Arrow](https://arrow.apache.org/)**: A cross-language development platform for in-memory data that specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
- **[Jupyter Ai Magics](https://github.com/jupyterlab/jupyter-ai)**: A JupyterLab extension that provides a set of magics for working with AI models.
- **[Amundsen](https://www.amundsen.io/)**: A data discovery and metadata platform that helps users discover, understand, and trust the data they use.
- **[Ibis](https://ibis-project.org/)**: A Python data analysis framework that provides a pandas-like API for analytics on large datasets.
- **[Feast](https://feast.dev/)**: An operational feature store for managing and serving machine learning features to models in production.
## Installation
Simply install the package using pip:
```shell
pip install ryoma_ai
```
Or with extra dependencies:
```shell
pip install ryoma_ai[snowflake]
```
## Basic Example
Below is an example of using Ryoma to connect to a postgres database and ask a question.
You can read more details in the [documentation](https://project-ryoma.github.io/ryoma/).
```python
from ryoma_ai import Ryoma
from ryoma_data import DataSource
# Connect to a postgres database
datasource = DataSource(
"postgres",
host="localhost",
port=5432,
database="dbname",
user="user",
password="password"
)
# Create Ryoma instance and SQL agent
ryoma = Ryoma(datasource=datasource)
agent = ryoma.sql_agent(model="gpt-4", mode="enhanced")
# Ask question to the agent
agent.stream("I want to get the top 5 customers which making the most purchases", display=True)
```
The Sql agent will try to run the tool as shown below:
```text
================================ Human Message =================================
I want to get the top 5 customers which making the most purchases
================================== Ai Message ==================================
Tool Calls:
sql_database_query (call_mWCPB3GQGOTLYsvp21DGlpOb)
Call ID: call_mWCPB3GQGOTLYsvp21DGlpOb
Args:
query: SELECT C.C_NAME, SUM(L.L_EXTENDEDPRICE) AS TOTAL_PURCHASES FROM CUSTOMER C JOIN ORDERS O ON C.C_CUSTKEY = O.O_CUSTKEY JOIN LINEITEM L ON O.O_ORDERKEY = L.L_Excerpt of 8,840 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f2e25456ad7b2ed8, llm:Topics: ai, data-science, llm; Description: 'Common AI agent framework solving your data problems'
matched fp:f2e25456ad7b2ed8, llm:Topics: ai, data-science, llm; Description: 'Common AI agent framework solving your data problems'
matched fp:f2e25456ad7b2ed8, llm:Topics: ai, data-science, llm; Description: 'Common AI agent framework solving your data problems'