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ryoma

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Basic Information

Ryoma is an AI-powered data agent framework designed to help teams analyze, engineer, and visualize data by combining language models with data tooling. The project provides prebuilt agents such as a SqlAgent that can connect to data sources, generate and run SQL, and stream results. It integrates with a broad tech stack including LangChain for LLM workflows, Reflex for a web UI called Ryoma Lab, Apache Arrow for in-memory data, Ibis for analytics, Feast for feature management, and Amundsen for metadata. The README shows how to install the package via pip and how to start an interactive lab application. Ryoma also documents supported model providers and supported data sources and engines, positioning itself as a framework for building AI-driven data workflows and interactive data assistants.

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App URL
https://github.com/project-ryoma/ryoma
Github Repository
https://github.com/project-ryoma/ryoma/blob/main/README.md

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App Details

Features
The repository includes a SqlAgent that connects to databases and generates executable SQL queries from natural language, with streaming outputs and a tool mode for running generated queries. It provides datasource connectors for Postgres, MySQL, Sqlite, BigQuery, Snowflake and file formats such as CSV, Excel, and Parquet, and lists planned support for Redshift and DynamoDB. It supports multiple model providers via Jupyter AI magics and documents required environment variables and Python packages for each provider. Ryoma offers Ryoma Lab, a Reflex-based UI for interacting with data and models, and integrates with data infrastructure components like Apache Arrow, Ibis, Feast and Amundsen. Installation is available via pip with optional extras for specific backends.
Use Cases
Ryoma helps data engineers, analysts and ML practitioners by automating the translation of natural language questions into data operations and queries, reducing manual SQL development. It centralizes connectors to common databases and file formats so teams can use the same agent interfaces across data sources. By integrating with model providers and data tooling, Ryoma makes it easier to prototype data workflows, run exploratory analysis, and serve features to models. The Reflex-based Ryoma Lab provides a UI to interact with agents and view results without building a custom frontend. The framework is packaged for simple installation and documents supported providers and engines to simplify adoption in existing data ecosystems.

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