WrenAI – An open-source commercial AI Agent tool that generates SQL from natural language

What is WrenAI？

WrenAI is an open-source commercial intelligent AI Agent tool developed by Canner. It enables users to query, analyze, and visualize structured data through natural language interaction—without the need to write complex SQL code. Users can simply ask questions in plain language, and WrenAI will generate precise SQL queries, outputting results in various formats such as charts and reports. By leveraging schema embedding and relevance-based retrieval, it ensures query accuracy and contextual alignment. WrenAI supports multiple mainstream databases (e.g., PostgreSQL, MySQL, Snowflake) and deployment modes (self-hosted, cloud, etc.), and is compatible with various large language models such as OpenAI’s GPT series and Google Gemini.

Key Features of WrenAI

• Natural Language Data Query: Users can ask questions in natural language, and WrenAI will translate them into SQL queries—no coding required.

• Multimodal Data Output: Supports output in multiple formats such as SQL, charts, summary reports, dashboards, and spreadsheets to meet different scenario needs.

• AI-Driven Analytical Insights: Provides AI-generated summaries, reports, and context-aware visualizations to help users complete data analysis quickly.

• Multi-Database Support: Compatible with major databases including PostgreSQL, MySQL, and Snowflake, adaptable to various data environments.

• Flexible Deployment: Offers self-hosted, cloud-based, or managed service deployment options to suit different user requirements.

• Strong LLM Compatibility: Supports various large language models such as OpenAI’s GPT series and Google Gemini, offering diverse options.

• Modular & Extensible Design: Features a modular architecture supporting custom connectors, templates, and domain-specific integrations for enhanced flexibility and applicability.

Technical Principles of WrenAI

• Natural Language Processing & Semantic Understanding: Uses NLP technology to interpret user commands in natural language, transforming them into structured query requirements and accurately capturing user intent.

• SQL Query Generation: Based on the interpreted user request, WrenAI encodes schema, metrics, joins, and definitions using a modeling definition language to provide large language models with precise context, enabling the generation of production-grade SQL queries.

• Schema Embedding & Semantic Retrieval: The semantic engine leverages schema embedding and relevance-based retrieval to ensure rich query content, accurate schema mapping, and precise SQL generation—reducing hallucinations and improving query accuracy and efficiency.

• Multimodal Output Support: Delivers output in various formats including SQL, charts, summary reports, dashboards, and spreadsheets, meeting diverse presentation and analysis needs.

Project Repository

• GitHub: https://github.com/Canner/WrenAI

Use Cases of WrenAI

• Data Analysis & Decision Support: Helps businesses and teams quickly gain data insights, supporting data-driven decision-making in scenarios requiring rapid market response.

• Simplified Data Query Process: Offers non-technical users (e.g., business analysts, product managers) a convenient way to query data without writing SQL, lowering the entry barrier.

• Automated Reporting: Automatically generates analytical reports and visualizations, saving time on manual report creation and improving work efficiency.

• Multi-Database Integration: Ideal for organizations using multiple databases, providing seamless connectivity and a unified interface for querying and analysis.

• Integration with Data Tools: Works with popular tools like Excel and Google Sheets, enabling users to analyze data within familiar environments.