WrenAI is an open-source Generative Business Intelligence (GenBI) agent developed by Canner, designed to enable seamless, natural-language interaction with structured data. It targets both technical and non-technical teams, providing the tools to query, analyze, and visualize data without writing SQL. All capabilities and integrations are verified against the official documentation and latest releases.

Key Capabilities

• Natural Language to SQL:Users can ask data questions in plain language (across multiple languages) and WrenAI translates these into accurate, production-grade SQL queries. This streamlines data access for non-technical users.
• Multi-Modal Output:The platform generates SQL, charts, summary reports, dashboards, and spreadsheets. Both textual and visual outputs (e.g., charts, tables) are available for immediate data presentation or operational reporting.
• GenBI Insights:WrenAI provides AI-generated summaries, reports, and context-aware visualizations, enabling quick, decision-ready analysis.
• LLM Flexibility:WrenAI supports a range of large language models, including:
  • OpenAI GPT series
  • Azure OpenAI
  • Google Gemini, Vertex AI
  • DeepSeek
  • Databricks
  • AWS Bedrock (Anthropic Claude, Cohere, etc.)
  • Groq
  • Ollama (for deploying local or custom LLMs)
  • Other OpenAI API-compatible and user-defined models.
• Semantic Layer & Indexing:Uses a Modeling Definition Language (MDL) for encoding schema, metrics, joins, and definitions, giving LLMs precise context and reducing hallucinations. The semantic engine ensures context-rich queries, schema embeddings, and relevance-based retrieval for accurate SQL.
• Export & Collaboration:Results can be exported to Excel, Google Sheets, or APIs for further analysis or team sharing.
• API Embeddability:Query and visualization capabilities are accessible via API, enabling seamless embedding in custom apps and frontends.

Architecture Overview

WrenAI’s architecture is modular and highly extensible for robust deployment and integration:

Semantic Engine Details

• Schema Embeddings:Dense vector representations capture schema and business context, powering relevance-based retrieval.
• Few-Shot Prompting & Metadata Injection:Schema samples, joins, and business logic are injected into LLM prompts for better reasoning and accuracy.
• Context Compression:The engine adapts schema representation size according to token limits, preserving critical detail for each model.
• Retriever-Augmented Generation:Relevant schema and metadata are gathered via vector search and added to prompts for context alignment.
• Model-Agnostic:Wren Engine works across LLMs via protocol-based abstraction, ensuring consistent context regardless of backend.

Supported Integrations

• Databases and Warehouses:Out-of-the-box support for BigQuery, PostgreSQL, MySQL, Microsoft SQL Server, ClickHouse, Trino, Snowflake, DuckDB, Amazon Athena, and Amazon Redshift, among others.
• Deployment Modes:Can be run self-hosted, in the cloud, or as a managed service.
• API and Embedding:Easily integrates into other applications and platforms via API.

Typical Use Cases

• Marketing/Sales:Rapid generation of performance charts, funnel analyses, or region-based summaries from natural language prompts.
• Product/Operations:Analyze product usage, customer churn, or operational metrics with follow-up questions and visual summaries.
• Executives/Analysts:Automated, up-to-date business dashboards and KPI tracking, delivered in minutes.

Conclusion

WrenAI is a verified, open-source GenBI solution that bridges the gap between business teams and databases through conversational, context-aware, AI-powered analytics. It is extensible, multi-LLM compatible, secure, and engineered with a strong semantic backbone to ensure trustworthy, explainable, and easily integrated business intelligence.

Check out the GitHub Page. All credit for this research goes to the researchers of this project.

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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.